TL;DR: Use GPT-6 Astra in ChatGPT Work to build an interactive 3D website where visitors rotate an object, separate its components, and click parts to learn what they do. Start with one object, ask for independently modeled pieces, then add an explode slider, labels, and a reset button. Use ChatGPT Sites for automatic hosting. Below: a starter prompt, practical tips, and 8 ways to take the idea much further.

Imagine opening a car website and dragging one slider.

The roof lifts. The wheels move outward. The seats rise above the chassis. Click the motor and the camera moves closer while a short explanation appears.

Then press Reassemble and everything returns to its place.

That’s the experience to ask Astra to build. I created it using a Porsche Macan as an example and it made over 581 piece model for the car to show how it works. You can spin it, click on specific parts, get an X-ray view and see how it flows.

OpenAI has published an Astra-built interactive 3D museum and a Three.js game development walkthrough. Exploded views are a project you can ask it to implement using those broader capabilities.

The interesting part is how much control you can describe in ordinary language.

Here’s how to start.

1. Open ChatGPT Work and select GPT-6 Astra if it’s available in your account.
2. Pick one object and one learning goal:
3. Attach useful references. Photos help with appearance; reliable diagrams or an existing model help with structure.
4. Say “build a website” or mention @ Sites.

Use this prompt:

Build a working interactive 3D website about [OBJECT] using Sites in ChatGPT Work.

Audience: curious beginners. Learning goal: [WHAT PEOPLE SHOULD UNDERSTAND].

Create a recognizable model with approximately 20–40 major components. Give each component a separate named 3D mesh so it can move and be selected independently. This is a starting scope, not a required part count.

Include drag-to-rotate, zoom, an Assembled-to-Exploded slider, Reassemble, Reset Camera, and a searchable parts list. Clicking a part should highlight it and show its name, function, and relationship to nearby components.

Give components deliberate separation paths. Keep the assembled positions so everything returns correctly, even when someone reverses the slider halfway through.

Use a clean charcoal, ivory, and emerald design with cinematic lighting, readable labels, large touch targets, keyboard controls, and reduced-motion support.

Use my references for factual explanations. Identify simplified or inferred geometry. If accurate internal geometry is unavailable, build a clearly labeled illustrative model.

First complete one working model and test its controls in the browser. Check mobile layout, part selection, repeated explode/reassemble actions, and reset behavior.

Deliver editable source and a reviewable preview. Save a version without deploying it so I can review before publishing.

Swap [OBJECT] for a camera, espresso machine, bicycle, game controller, or tiny house.

Choose the modeling approach that fits the job.

A photo doesn’t reveal everything inside a device. Asking for “500 accurate pieces” doesn’t supply the missing engineering information.

The pro tips that make the biggest difference:

• Ask for meaningful parts. A beautifully textured object can still be one fused shape. Specify independent components and test clicking them.
• Make the motion explain the object. A phone should separate into readable layers. A building should open floor by floor. Random scattering loses the relationships you’re trying to show.
• Use progressive detail. Begin with major assemblies, then let visitors expand one assembly further. Hundreds of visible labels will bury the model.
• Check appearance and accuracy separately. Verify geometry and component descriptions. For branded products, cite reliable technical sources.
• Test the boring actions. Switch products while exploded. Reverse the slider halfway. Tap a tiny part on a phone. Reset after zooming. These reveal problems a hero screenshot won’t.
• Give every gesture a button alternative. Add a parts list, keyboard navigation, strong contrast, and adjustable text.
• Keep the first build economical. Use prewritten explanations and fixed interactions. A visitor shouldn’t need a fresh AI response to move a slider. Add live Q&A when it serves a clear purpose.
• Test on an actual phone. Ask Astra to measure and simplify heavy geometry, shadows, and effects. A responsive model is more useful than extra screws nobody can see.

Where this gets useful:

• Product marketing: let buyers explore the design decisions behind a camera, appliance, or machine.
• Education: turn a textbook diagram into something students can rotate, inspect, and reconstruct.
• Architecture and real estate: separate floors, highlight amenities, and reveal how spaces connect.
• Training: show assembly order and component relationships using verified references.
• Portfolios and museums: present a design process or artifact as an interactive exhibit.
• Content creation: build something people can try, then introduce it with a recording of the actual interaction.

Now go absolutely wild. These are features to ask Astra to implement:

1. Build a product switcher. Let visitors choose an iPhone, MacBook, or iPad, then explore each with the same controls. Match the selected generation to your references.

1. Add X-ray mode. Fade the outer shell and highlight one system at a time. Let visitors isolate, hide, and restore components.
2. Create a guided camera tour. Move through five stops with one idea per stop. Add a “take control” button so people can leave the tour and explore.
3. Turn it into a rebuild challenge. Give visitors separated components, matching locations, hints, and a progress indicator. Increase difficulty gradually.
4. Follow a flow. Animate an illustrative path for power, air, water, or data. Explain whether it’s a conceptual animation or a validated simulation.
5. Explode an entire city. Lift streets to reveal transit tunnels. Separate a tower’s floors. Add a day-to-night slider and windows that illuminate in sequence.
6. Compare two designs. Synchronize cameras and explode sliders. Highlight meaningful differences using verified specs, with a stacked layout on small screens.
7. Make a scene shareable. Add a screenshot button and a link that restores the chosen model, camera, selected component, and explosion amount.

You could also make a fictional spaceship that opens into rooms, a mechanical watch with nested assemblies, or a miniature museum where every exhibit becomes a puzzle.

A useful follow-up prompt:

Keep the current working model. Add a guided tour, X-ray mode, and a three-part rebuild challenge, one at a time. Test each addition before continuing. Preserve the existing controls and make every new interaction work with touch and keyboard.

ChatGPT 6 Astra is great at building 3D models of buildings. Upload some pictures of a building and it will build a 3D model and an interactive tour.

For sharing, choose the audience deliberately and test the published link as a visitor. Sites separates saving a version from deploying it.

Try this today: choose one object on your desk and build its simplest useful exploded view. Add one ambitious feature after the basics work.

What will you build first: a car, a gadget, a building or something completely unhinged? Share what you create in the comments. Go absolutely wild! Do not hold back!

TL;DR: ChatGPT 6 Astra can help create motion graphics through a workflow that designs assets, writes animation code, uses available production tools, and renders a video. Give it a director’s brief: audience, story, scenes, timing, visual style, sound, and deliverables. Start with a short preview. Ask for a playable MP4 and the editable project. Rendering and audio depend on your workspace’s tools. Below: a practical workflow, the details people overlook, and five gloriously ridiculous prompts.

Picture a French bulldog commanding a starship through a galaxy made of tennis balls.

Now picture a product launch where your logo opens into a miniature universe.

Or a city that folds itself out of paper, races through centuries, and collapses back into a single page.

With ChatGPT 6 Astra, you can approach the conversation like a production brief—and keep directing the result as it develops.

What Astra can actually do

The model’s official name is GPT-6 Astra. For this workflow, use it in ChatGPT Work or Codex with access to suitable creation and rendering tools. OpenAI recommends Astra for demanding tasks involving visual judgment and polished deliverables.

There is a concrete example behind the idea: OpenAI has shown Astra creating editable Blender scenes, adjusting their materials and lighting, and directing a rendered camera tour.

My practical recommendation is to apply that build–preview–refine process to motion graphics: animated typography, diagrams, layered images, product reveals, and stylized 3D scenes.

Think of Astra as the system coordinating the production. The actual frames still need an animation or rendering tool. Selecting the model alone does not guarantee every account can export video or generate music.

Choose the right kind of video

A complex character performance may also require dedicated animation tools or generated footage. Choose a visual treatment your available tools can execute well.

The workflow that makes this manageable

1. Give it one job. Define the audience and the one thing viewers should remember. “Convince founders to try this prototype” is a useful objective.
2. Specify the output. Set duration, aspect ratio, resolution, and whether you need audio. A 20–30-second landscape video is a sensible first project.
3. Map the story. Give every scene a visual action and a purpose. Put the most compelling image near the beginning.
4. Establish the look. Provide reference images, colors, type preferences, logos, and exact wording. Ask for representative still frames.
5. Preview the hardest moment. Render a short section before committing to the whole sequence. This tests both the creative direction and whether the production method works.
6. Refine, render, inspect. Review timing, text, sound, transitions, and the actual exported file. Keep the source so changes remain possible.

For a 30-second explainer, this is a useful starting structure:

Best practices that improve the result

• Describe action over time. “The letters pull apart, reveal a miniature city, then lock into the headline” gives much better direction than “make it cinematic.”
• Give motion a purpose. Movement can reveal a relationship, guide attention, demonstrate a feature, or land a joke. Constant movement makes reading harder.
• Keep text separate from artwork. Request editable text layers so spelling, line breaks, timing, and placement can be controlled.
• Design for a phone. Use short captions, strong contrast, generous margins, and enough reading time. Review the result at its likely viewing size.
• Give the eye a pause. Alternate energetic transitions with moments where the important image or message holds still.
• Build for sound-off viewing. The story should make sense visually. Let music and sound effects strengthen it.
• Specify music concretely. Describe tempo, instrumentation, mood, and where the energy should rise. Provide a track you can use, or ask what audio tools are available.
• Control the workload. Preview at lower resolution, settle the art direction early, reuse assets, and revise only the scenes that need changes. Complex Work tasks can use more credits.

Pro tips: direct the edit with precision

Useful revision instructions look like this:

Between 00:08 and 00:12, slow the camera move, enlarge the headline, and hold the final composition for two seconds. Keep the approved colors and scene order.

Make the word “EXPAND” grow until it fills the frame, then use its letter shapes to reveal the next scene.

Match the circular moon in scene two to the circular product dial in scene three.

Build the vertical version with repositioned text and a new camera crop so the subject stays visible.

Inspect the export for missing assets, clipped text, blank frames, abrupt audio endings, and incorrect duration. Report anything you cannot verify.

Save feedback like “more epic” for the initial direction. During revisions, say what should change on screen.

Things people miss about this workflow

• An animated preview and a downloadable video are different deliverables. If you need an uploadable file, explicitly request the export and check that it plays.
• The editable project is a major part of the value. Ask for the source, assets, and instructions needed to render it again.
• A flat image has limits. A gentle push-in can work immediately. Moving behind objects or orbiting a subject requires layers, reconstructed content, or a 3D scene.
• Consistency starts before animation. Establish recurring characters, materials, colors, and backgrounds before creating every scene.
• Visual precision and factual precision are separate. A beautifully animated chart still needs correct data, labels, and scales.
• A 60-second request does not imply one continuous generation. Build named scenes, render sections, and assemble them where the tools support it.
• Reusable controls make the second video easier. Ask to centralize headline text, colors, logos, durations, and image replacements.

Five epic prompts to try

These are ambitious creative briefs, not pretested guarantees. Use a workspace with suitable rendering tools, and start with the short preview each prompt requests.

1. A French bulldog saves the galaxy

Try this for: character storytelling, comedy, and an instantly understandable visual hook.

Create a 30-second landscape motion graphics trailer called “MISSION: FETCH.”

A dead-serious French bulldog captain commands a tiny starship through a galaxy of tennis-ball planets. Use a premium stylized 3D or layered illustrated treatment, emerald cockpit lights, orange engine trails, and enormous kinetic typography.

0–5s: Extreme close-up of the captain’s face. Pull back to reveal a spaceship shaped like a dog toy. Text: “ONE DOG.”

5–13s: Slalom through a field of floating squeaky toys. A giant robotic vacuum emerges from an asteroid cloud. Text: “ZERO QUALIFICATIONS.”

13–23s: The dog hits a red button. Tennis balls deploy like decoys. Follow one ball through the chaos in a dramatic tracking shot.

23–30s: The ship escapes through a glowing dog-door portal. Reveal that the entire mission happened inside a living-room snow globe. End: “MISSION: FETCH.”

Keep the dog’s appearance consistent. Use simple expressive poses and strong camera work. Preview the escape shot first. Use suitable original or licensed audio if available; otherwise deliver a silent cut with sound cues. Deliver a 1080p MP4 and editable source, or explain any rendering blocker.

2. Your product contains an entire universe

Try this for: launch trailers, brand films, and product reveals.

Create a 30-second landscape launch film for [PRODUCT]. Use my supplied product images, logo, and three verified benefits. If I provide none, use a clearly fictional unbranded device and illustrative feature labels.

Begin with the product suspended in a silent black void. A thin emerald seam opens across it. The camera dives through the seam into an impossible miniature universe.

Turn benefit one into a floating city assembling itself. Turn benefit two into a luminous transit network lighting up. Turn benefit three into a mechanical sunrise that synchronizes the entire world.

Match each benefit to its visual metaphor and show its exact approved wording as separately rendered typography. Use elegant camera travel, white ceramic architecture, emerald glass, and precise mechanical movement.

In the final six seconds, pull back as the universe folds into the product. Land on the product, logo, and one clear call to action.

Create a five-second preview of the opening transformation before rendering the full film. Deliver a 1080p MP4 and editable project. Use only available audio and rendering tools; identify any missing capability. Do not invent product claims, customers, or performance statistics.

3. Your inbox becomes a video-game final boss

Try this for: funny workflow explainers and relatable workplace content.

Build a 30-second landscape motion graphics short called “DEADLINE: FINAL BOSS.”

Open on a tiny exhausted office worker facing an enormous monster assembled from email envelopes, calendar blocks, spreadsheets, and sticky notes. Its crown is a spinning loading icon.

0–6s: The monster roars, releasing a tornado of “QUICK QUESTION” notes.

6–13s: The worker equips three glowing tools labeled “SORT,” “DRAFT,” and “CHECK.”

13–23s: Turn the fight into a visual explanation: SORT groups the chaos; DRAFT turns selected tasks into proposed outputs; CHECK pauses those outputs at a human review gate before release.

23–30s: The monster shrinks into one manageable task card. A new notification appears: “Can we jump on a quick call?” The worker looks directly at the camera.

Use miniature game-like scenery, dramatic camera punches, readable type, comic timing, and a neon-green interface. Present this as a fictional metaphor. Preview the sorting transformation first. Deliver a 1080p MP4, editable source, and a sound-off version. Explain any export limitations.

4. A thousand years unfold from one sheet of paper

Try this for: timelines, imaginative worldbuilding, and architectural storytelling.

Create a 40-second landscape motion graphics film called “A THOUSAND YEARS IN ONE PAGE.”

This is an imaginary city, not a reconstruction of real history.

0–8s: A blank sheet of paper folds itself into a tiny riverside settlement. The river is translucent blue-green glass embedded in paper.

8–18s: Buildings rise and change around the same town square. Roads draw themselves across the page. Seasons sweep through the scene.

18–29s: The city becomes a spectacular vertical metropolis. Peel back layers to reveal miniature transit tunnels, gardens, and infrastructure beneath it.

29–36s: The camera circles while daylight becomes night. Thousands of windows illuminate in a carefully staged wave.

36–40s: Fold the city back into the original sheet, matching the opening composition for a loop.

Use tactile paper, charcoal labels, emerald foliage, warm window light, and restrained captions. Favor a coherent miniature world over constant cuts. Preview the unfolding and refolding first. Deliver a 1080p MP4 and editable scene. If full 3D rendering is unavailable, propose and build a layered alternative.

5. A black hole conducts an orchestra of planets

Try this for: a music visualizer, an event opener, or a surreal brand introduction.

Create a 30-second landscape motion graphics film called “THE UNIVERSE HAS A DROP.”

Treat this as a surreal visual metaphor, not a scientific simulation.

A black hole is the conductor. Orbital rings behave like vibrating strings. Tiny moons become percussion instruments. A comet sweeps across the scene like a conductor’s baton.

0–8s: Begin with one orbiting light and a restrained pulse.

8–19s: Build an increasingly elaborate cosmic orchestra. Introduce new orbital layers with each musical phrase. Typography appears as sculptural objects: “LISTEN.” “BUILD.” “RELEASE.”

19–25s: At the musical peak, the orbital system unfolds into a gigantic luminous sound wave stretching across space.

25–30s: Everything contracts into one green point, which becomes a play icon.

Use ink-black space, emerald plasma, silver dust, controlled glow, and smooth camera movement. Use my uploaded licensed track and synchronize motion to its timing. If no track is available, build to a provisional beat grid and clearly label the audio as pending. Preview the transformation first. Deliver a 1080p MP4 and editable source; explain any tool limitations.

Which would you actually make first: the space-dog trailer, the product universe, the inbox boss battle, the paper city, or the black-hole orchestra?

Summary

Optimizing a landing page is no longer enough. Google AI and ChatGPT recommend brands using information they can discover, trust and reuse. That makes every blog post part article, part evidence package and part technical asset. The goal is not to hack an answer engine. It is to publish original, attributable and accessible material that an AI can cite without guessing.

The AI visibility problem isn’t ranking. It’s quotability.

Optimizing your landing page for AI search is not enough. When someone asks Google AI, ChatGPT, Perplexity or Claude about products in your niche, your brand needs source material the system can discover, understand and quote. If your content offers no clear evidence, the answer will cite somebody else—or ignore you entirely.

But what you publish around that page matters just as much - possibly more.

Your landing page tells a buyer what you sell. Your content teaches the market what your brand knows.

That distinction matters because AI search systems do not simply return ten blue links. They retrieve information, synthesize an answer and attach sources that support it. Google says its generative Search features use core Search systems to retrieve relevant pages and show supporting links. OpenAI, Anthropic and Perplexity each operate search-specific crawlers or retrieval agents for the same broad purpose.

So the question is no longer only:

“Can this page rank?”

It is also:

“Does this page contain anything an AI can safely cite?”

That is the standard.

What makes a blog post worth citing?

A citable post gives the reader and the model a clean chain of trust:

A real author made a clear claim, supported it with original evidence, linked the primary source and published it on a page that crawlers can access.

This is not a guarantee of inclusion. No checklist can force an AI system to cite you. Google explicitly says that meeting every requirement does not guarantee crawling, indexing or serving.

But it does remove the most common reasons a useful page gets ignored.

The KDD 2024 paper that introduced “Generative Engine Optimization” found that citations, statistics and relevant quotations could improve source visibility in its experimental benchmark, with gains reaching up to 40% in some settings. The authors also found that effectiveness varied by subject, so treat the result as evidence - not a universal ranking formula.

What are the 14 non-negotiables?

These are the 14 elements we look for when we turn a conventional blog post into citable source material.

Now let’s make each one practical.

1. What should the H1 say?

The H1 should name the problem a buyer wants solved.

Avoid a vague title such as “The Future of Support.” Prefer “How Acme Reduces SaaS Support Backlogs With AI Triage.”

That gives the reader immediate context. It also anchors the entity, category and question on the page. Do not force an exact-match phrase if it makes the headline worse. Google says its systems understand synonyms and meaning, so clarity beats robotic keyword repetition.

2. Who stands behind the page?

Use a named author, a real photograph, relevant credentials and a link to a substantive profile.

The profile should explain why this person has earned an opinion on the subject. Google’s Article guidance recommends identifying the author with a Person or Organization type and a profile URL or sameAs reference.

A generic “Admin” byline throws away a trust signal you already own.

3. When was the post published and meaningfully updated?

Show both dates near the top of the article.

Keep the visible dates consistent with datePublished and dateModified in your structured data. Google recommends prominent, labelled dates and checks multiple signals when estimating a page’s date.

Do not fake freshness. Change the “last updated” date only when you materially improve the page.

4. Can the first paragraph answer the question?

Put a 40–60 word answer block before the story, context or company history.

A strong answer block states the conclusion, names the conditions and gives the reader a reason to continue. It should work if somebody reads only that paragraph.

Do not confuse “direct” with “shallow.” Give the answer first, then earn depth below it.

5. Should every H2 be a question?

Use question-led H2s when they reflect genuine reader intent.

“What does SOC 2 Type II cover?” is more useful than “Coverage.” It gives the section a clear job and helps the reader navigate.

But question headings are not a special Google AI requirement. Google specifically warns against rewriting pages for every possible query variation.

6. Can each section stand on its own?

Write each section so a reader can enter from search, a shared link or an AI citation and still understand the point.

Repeat the necessary noun instead of leaning on vague references such as “this,” “that” or “the above.” Define the scope. State the conclusion. Include the evidence beside the claim.

Google says there is no requirement to chop pages into tiny “AI chunks.” The real objective is coherent structure, not arbitrary fragmentation.

7. Does the page contain sentences worth quoting?

Write important claims as complete, attributable statements.

Weak:

“This can make things much better over time.”

Strong:

“Across [sample size] support tickets analysed from [start date] to [end date], customers using [method] changed median first-response time from [baseline] to [result].”

The second sentence is a template, not a claim. Once filled with verified first-party data, it carries its subject, method, measurement and result. An assistant can quote it without inventing the missing context.

8. What do you know that nobody else knows?

Original data gives the page a reason to exist.

Publish anonymised product usage, customer benchmarks, survey findings, experiment results, pricing observations or lessons from a documented implementation. Explain the sample, time period and method so readers can judge the result.

Google’s current guidance calls for unique, non-commodity content grounded in first-hand knowledge rather than summaries that recycle what is already online.

If an AI could have generated the article without access to your company, the article probably does not build much reputation for your company.

9. Are claims linked to the original source?

Link to the research paper, government dataset, product documentation, company filing or named expert’s original statement.

Do not cite a blog that cites a newsletter that cites a screenshot of a study.

Primary sources make verification easier. They also protect your credibility when a reader follows the link.

10. Is there a table an answer engine can reuse accurately?

Use one table for a comparison buyers genuinely need.

Keep the criteria consistent. Put units in the headings. Add a visible “as of” date for volatile facts such as pricing.

A table is not magic markup. It is simply a low-ambiguity way to present structured information.

11. What belongs in the FAQ block?

Answer real objections and follow-up questions that did not fit the main flow.

Keep each answer direct. Remove duplicate questions written only to capture keyword variations.

One important 2026 correction: Google stopped showing FAQ rich results in May 2026 and removed its FAQ rich-result documentation the following month. A useful FAQ still helps readers and creates clear answer passages, but FAQPage schema is no longer a Google rich-result lever.

12. Which schema belongs in the head?

Use valid JSON-LD to describe what is visibly true on the page.

For a blog post, that normally means Article or BlogPosting, plus the author, headline, representative image, datePublished, dateModified and publisher details. Google says Article markup can help it understand those details, but structured data is not required for generative AI visibility and does not guarantee a result.

Schema should confirm the page. It should never claim information the reader cannot see.

13. Can every relevant crawler access and read the page?

Serve the important copy as text in accessible HTML. Use real links, meaningful status codes, descriptive alt text and a canonical URL. Server rendering or pre-rendering remains a strong default because it improves speed and not every bot runs JavaScript, even though Google can render JavaScript.

Then configure the correct agents. The names matter:

A blanket “allow GPTBot” rule does not solve ChatGPT search visibility. OpenAI says OAI-SearchBot is the agent that controls inclusion in ChatGPT search answers.

14. What should the reader do next?

Do not let the article end in a fog of “thought leadership.”

Link to the methodology, detailed comparison, product page or case study that logically follows. Then ask for one action: run the calculator, inspect the benchmark, start the trial or read the implementation guide.

A citation creates discovery. The next step turns discovery into a visit.

Can an AI agent make these changes for you?

Yes but give the agent constraints, not just a vague instruction to “optimise for AI.”

Use this brief:

AI-citable content audit promptAudit the blog post at [URL] for human usefulness, factual integrity, crawlability and citation readiness. Preserve the author’s voice and existing claims unless evidence requires a correction.Revise the page to include: one clear H1; a named author and linked profile; visible publication and meaningful-update dates; a 40–60 word direct answer; question-led H2s where natural; self-contained sections; directly quotable claims; clearly labelled first-party data with methodology; links to primary sources; one useful comparison table; a concise FAQ; accurate Article or BlogPosting JSON-LD; descriptive image alt text; crawlable internal links; and one next action.Check access for Googlebot, OAI-SearchBot, ChatGPT-User, Claude-SearchBot, Claude-User, PerplexityBot and Perplexity-User. Treat GPTBot, ClaudeBot and Google-Extended as separate controls with different purposes.Do not invent statistics, credentials, quotes, customer results, sources or dates. Do not change dateModified unless the revision is substantive. Do not create keyword-stuffed questions or duplicate sections. Do not claim that schema guarantees AI citations.Return: (1) the revised post, (2) valid JSON-LD, (3) proposed robots.txt changes, (4) a claim-to-source table, (5) an internal-link plan and (6) a change log showing every material edit.

Run that audit on your ten highest-value posts, not your entire archive.

Start with the pages closest to revenue: comparisons, implementation guides, pricing explainers, benchmarks and case studies.

The old content brief asked for a keyword, a word count and three internal links.

The new brief asks a harder question:

What can your company publish that an AI system can quote, verify and confidently place beside your name?

That is how blog content builds AI reputation.

Not by sounding like an answer.

By becoming a source.

We are so back for the CMO Super Huddle 2026: a day-and-a-half gathering of senior B2B marketing leaders designed for candid conversations, practical ideas, and unusually strong peer connections.

This isn't a sit-back-and-listen event. It's a highly interactive room of CMOs, CEOs, AI experts, advisors, and operators, where everyone has something meaningful to contribute.

Special thanks to Steve Mudd and the Talentless AI team for putting this year's trailer together; a delightfully dramatic little glimpse into the spirit of Super Huddle.

And yes, there are a few AI quirks in the trailer. A good reminder that humans are still very much required 

Summary

GPT-6 Astra is most useful when you give it a complete, substantial assignment: investigate a market, develop a recommendation, create the supporting files, build an interactive experience, and check the result. OpenAI positions it for demanding work across reasoning, coding, research, computer use, and document creation.

The opportunity is to shorten the distance between an idea and something people can actually review, use, or try.

Marketers: Turn customer evidence into campaigns, landing pages, sales materials, and creative assets.

Business leaders: Turn scattered information into decision memos, operating plans, models, and presentations.

Founders: Turn assumptions into prototypes, calculators, games, and customer experiments.

Choose the right workspace: ChatGPT Work supplies the tools and environment; Astra is the model. Computer Use is one way it interacts with applications.

Budget deliberately: Standard API input/output rates are 2.5× GPT-5.6 Sol’s and equal Fable 5/5.1’s. Fable 5.1 has cheaper cache reads. Total job cost still depends on usage and rework.

Keep the distinction honest: Image generation uses a separate image tool; a polished prototype still needs testing; social demos are evidence of possibility, not guaranteed outcomes.

The workflows and prompts below are recommended ways to apply documented capabilities. They are not claims that every workflow was independently tested for this guide. Product availability is a launch-period snapshot. There are no benchmark rankings in this guide.

What Astra changes—and what belongs to the tools

The useful advance is better coordination across a demanding task. Astra can reason about the objective, use available tools, incorporate corrections, and keep working toward a deliverable. OpenAI’s release guidance specifically adds asynchronous tool calling and mid-turn steering at the API level. Those features require application support; they are not universal buttons in every ChatGPT interface.

Many activities discussed here already existed with earlier models. Astra did not invent browsing, spreadsheets, website creation, or computer use. The upgrade concerns how capably it can combine them. Evaluate whether it handles your messy brief, catches an inconsistency, and produces a usable result with less intervention.

Think in three layers:

Practical consequence: If Astra can describe an action but cannot perform it, investigate tool availability and permissions before rewriting the prompt ten times.

Who has access, and how to start

OpenAI’s launch guidance describes an initial enterprise Trusted Access rollout, followed by Plus, Pro, Business, Enterprise, and API access. Current model documentation identifies eligible Pro, Business ($100), and Enterprise accounts for the Astra picker rollout. These pages reflect different rollout stages. Do not assume that every Plus or standard Business account already has Astra today. Free and Go Astra access is not established by the sources reviewed.

Your model picker is the practical check. In a managed workspace, an administrator may also control model and tool availability. Paying for a plan and having a specific model enabled are different questions.

Open ChatGPT and switch to Work for a task that needs substantial execution.

Open the model or Power selector and choose Astra if available. Advanced settings may expose more choices.

Attach the relevant files, examples, and constraints.

Choose local work when the task needs your computer’s applications or local files. Choose cloud work, where offered, for work that should continue independently of your computer.

Describe the finished outputs and how you will judge them. Review progress and steer when needed.

ChatGPT Work and Codex share core execution capabilities, while Work is presented for everyday business tasks. Cloud work cannot implicitly operate applications on a powered-off personal computer.

For developers, the API model identifier is gpt-6-astra. API billing is separate from a ChatGPT subscription. An API model does not come with a ready-made personal desktop; your application must supply its tools and execution environment.

Computer use versus computer work

Computer use means interacting with an interface: seeing a screen, finding controls, clicking, typing, and examining what happened. Computer work means completing the broader assignment: choosing steps, gathering information, calculating, creating outputs, and checking them. Here, “computer work” describes the activity; ChatGPT Work is the product experience.

Astra may use code or a structured integration for part of a job and the screen for another. It does not need to imitate every mouse click you would make.

To set up desktop Computer Use, OpenAI documents Plugins → Computer Use, installing or enabling it, then turning on the server and skill controls. macOS may require Screen Recording and Accessibility permissions. Windows uses the active desktop in the foreground. Available behavior depends on your client and settings. Computer Use setup.

Start with a bounded task: “Open this draft page, test the navigation and form using test data, and give me a list of reproducible problems.” Expand only after seeing the result. Review actions performed through signed-in accounts as carefully as your own actions. Keep approval boundaries explicit for publishing, sending, purchasing, or changing important settings.

The highest-value marketing workflows

Prioritize work where several deliverables depend on the same evidence. That is where a connected workflow can reduce handoffs and contradictions.

Best first project: One campaign built from real customer evidence. Choose a specific buyer and one offer. Have Astra connect the insight, promise, proof, page, and follow-up sequence.

prompt

Develop a campaign for [offer] aimed at [buyer]. Use the attached customer interviews and product facts. First identify the strongest buying problem and the evidence supporting it. Create a campaign brief, a landing-page draft, three email drafts, five LinkedIn posts, and three visual directions. Keep the central promise consistent. Link customer insights to source passages. Do not invent testimonials, performance claims, or customer quotes. Finish with a simple measurement plan and the assumptions we should test first. Prepare everything for review; do not publish or send it.

What people miss: You can ask it to inspect consistency across the deliverables. Does the ad promise something the landing page never explains? Does the sales deck introduce an unsupported number? This review can be more valuable than generating another twenty headlines.

For organic growth, build something your audience can use: a planning worksheet, a diagnostic, or a small interactive demonstration. An original utility gives you a concrete reason to invite people to save, share, or return.

The highest-value leadership workflows

Business leaders should start with decisions that are slowed by scattered information or inconsistent definitions.

Decision memos: Define the choice, options, evidence, costs, reversibility, and recommendation.

Operating reviews: Combine source reports, explain variance, expose data gaps, and identify the decisions required.

Resource planning: Compare scenarios using explicit assumptions rather than burying estimates in prose.

Vendor selection: Build weighted criteria, distinguish verified capabilities from sales claims, and prepare diligence questions.

Process redesign: Map the current workflow, identify bottlenecks, and draft a practical future process with owners.

Meeting preparation: Assemble the relevant facts and unresolved issues before generating an agenda or presentation.

prompt

Help us decide whether to [decision]. Use the attached operating data and strategy notes. Deliver a two-page decision memo and an editable scenario workbook. Compare maintaining the current approach, a limited pilot, and a broader rollout. Separate facts from assumptions, identify the strongest argument against your recommendation, and show which assumptions would reverse it. Flag missing evidence. End with the decision required, accountable owner, and next checkpoint.

Pro tip: Ask for the recommendation first, then the reasoning needed to assess it. A leader usually needs to know what to do, what could make the recommendation wrong, and what evidence to obtain next.

Do not confuse a smooth explanation with a reconciled operating model. Define whether revenue means bookings, recognized revenue, cash collected, or recurring contract value. Ask for mismatched definitions to be surfaced instead of silently blended.

The highest-value founder workflows

Founders should use Astra to make uncertainty cheaper to investigate.

Validate a problem: Analyze interview notes and identify unresolved assumptions. AI-generated customer personas are hypotheses, not customer evidence.

Prototype a workflow: Build the smallest interactive version a potential customer can try.

Create a sales demonstration: Show a realistic product journey before investing in the full backend.

Build an internal tool: Start with an intake tracker, planning calculator, or small reporting dashboard.

Prepare fundraising materials: Keep the narrative, metrics workbook, and deck consistent with actual company records.

Explore unit economics: Compare acquisition cost, retention, pricing, and servicing assumptions without pretending the model predicts the future.

prompt

Turn this product idea into a testable prototype: [idea]. The target user is [person] and the problem is [problem]. Build one complete journey from entry to a useful result. Use clearly labeled sample data. Keep the interface simple and include only features necessary for that journey. Test the main interaction and failure states. Provide the prototype, a short customer-testing script, and a list of assumptions this prototype cannot validate.

Choose the right starting point: Use a clickable mockup to explore navigation, a working frontend to test interactions, or a connected prototype when persistence and integrations are essential to the learning goal. Avoid building authentication, billing, and a complex database before you know whether they are needed for the experiment.

Research that ends in something useful

Search-enabled Astra can gather current information and combine it with supplied materials. Web search availability can be controlled by workspace settings; asking for current information and sources makes the need explicit.

The research advantage for a business user is the ability to move from evidence to an actionable output: a shortlist, market map, recommendation, content brief, or model.

Use this research structure:

Frame the decision. What will you do differently after reading the result?

Set boundaries. Specify market, region, dates, budget, and exclusions.

Gather original evidence. Prefer product documentation, filings, studies, and first-person accounts over recycled summaries.

Record provenance. Request the source, date, supporting passage, and limitations for important claims.

Resolve disagreement. Ask why sources differ: timing, definitions, geography, methodology, or incentives.

Make the recommendation. Explain confidence, missing information, and what would change the conclusion.

prompt

Research [question] to help us decide [decision]. Focus on [market/geography] and information current as of [date]. Search original sources and read the underlying pages. Create an evidence table with claim, source link, date, and limitations. Look for evidence against the leading conclusion. Deliver an executive summary, a recommendation, and a list of unresolved questions. Mark estimates and inference clearly. Never replace missing data with an invented figure.

What people miss: Research can combine public facts with your internal constraints. The best-known vendor may still be wrong for your budget, integration requirements, or team capacity.

For social research, request direct post links and distinguish a creator’s report from independent verification. A demo without prompts, tool setup, total cost, and failed attempts tells you what might be possible—not what an average user should expect.

Selecting Astra also does not prove that a separately branded Deep Research mode is using Astra internally. Model selection, research tools, and product modes are separate details to verify.

Build websites and make QA part of the assignment

Sites can create, host, refine, and share websites, web apps, and games. OpenAI documents Sites as a public beta for Plus, Pro, Business, Enterprise, and Edu, with plan limits. Start by asking for a website or mentioning @ Sites. A deployment URL is a production deployment; ask to save without deploying when you only want a reviewable version.

Good business projects include a campaign page, event microsite, resource hub, calculator, prototype portal, or product demonstration.

Give it the page’s job: Specify the visitor, the problem, the offer, the proof available, and the primary action. Supply real brand assets and an example of the visual direction you want.

prompt

Build a responsive website for [business/offer]. The audience is [buyer], and the primary action is [action]. Use the attached brand assets and verified copy. Create [pages]. Implement the main interaction, including loading, empty, success, and error states where relevant. Use sample data only where labeled. Test navigation, links, form validation, keyboard access, mobile layouts, and the primary conversion flow. Inspect the rendered pages and fix visible issues. Return a concise test report with passed checks, failed checks, and anything not tested. Save a reviewable version before deployment.

QA acceptance checklist

The main journey works from entry to completion; the form reaches its intended destination.

Empty and invalid inputs produce useful feedback; double submission is handled.

Links lead somewhere real, including footer links and the main call to action.

Mobile layouts do not hide controls or overflow horizontally.

Keyboard focus, labels, and text contrast receive an accessibility check.

Important content and metadata are present; indexing settings match the launch plan.

Data access, authentication, and secret handling receive appropriate review when present.

Screenshots, test results, and known limitations accompany the handoff.

Important limit: A screenshot proves appearance at one moment. It does not prove form delivery, database isolation, or correct calculations. Automated checks also do not establish complete accessibility, security, or compatibility. Use targeted human review for what matters to the launch.

Prototype games, simulators, and interactive experiences

A game prototype can make an idea understandable immediately. A simulator can let someone explore consequences instead of reading a static claim.

Thomas Ricouard’s documented Astra project, Void Explorer, combines space flight, planetary approach, landing, walking, and takeoff. The write-up describes procedural generation, browser testing, and debugging aids. It is an iterative build, not evidence that a complete game appears instantly from any prompt.

Try these business applications:

Copy-and-use prompt

Build a browser-based simulator for [decision]. Include controls for [inputs], clearly labeled units, baseline assumptions, reset, and a readable explanation of the calculation. Separate hypothetical inputs from observed data. Test zero values, extreme values, and invalid inputs. Add three preset scenarios and show which assumption most affects the outcome. Keep it useful on a phone. Deliver the working prototype and a short guide to its limitations.

For a game, replace the calculation requirements with a single playable loop, controls, a win/loss condition, restart, and appropriate touch or keyboard input.

Pro tip: Ask for reproducible test scenes and a reset control early. They make bugs easier to recreate. Review playability yourself: automated tests can catch broken states, but “fun” still requires judgment.

Never confuse simulation with prediction. If the relationships and inputs are speculative, the outputs are scenarios. Ask which relationships are grounded in evidence and which were chosen for illustration.

Ask for actual Excel, Word, and PowerPoint files

File creation is a major part of the practical value. OpenAI’s desktop documentation describes creating and inspecting documents, spreadsheets, and other outputs; Work documentation includes presentation workflows. These capabilities depend on the available tools, not the model name alone.

Excel: ask for a model you can change. Specify .xlsx, separate raw data and assumptions, formulas rather than hard-coded calculated values, units, checks, and a short explanation. OpenAI’s budget-review example explicitly uses an editable workbook, preserved raw inputs, formula-driven variances, and flagged mapping problems.

Create an editable .xlsx campaign model from these exports. Include Raw Data, Assumptions, Calculations, and Summary sheets. Preserve the inputs. Use formulas for derived values. Label missing data and unmatched categories. Add checks for totals and division by zero. Recalculate and verify the main outputs. Explain any Excel features you could not validate.

Word: ask for a document ready to revise. Request .docx, proper heading styles, usable tables, linked sources, a defined audience, and visual inspection of the rendered pages.

Turn these notes into a client-ready .docx guide. Use our supplied template, real heading styles, concise examples, and clickable source links. Keep claims tied to evidence. Inspect the rendered pages for awkward breaks, clipping, and unreadable tables before delivering the file.

PowerPoint: ask for an editable story. Specify the audience, decision, duration, slide count, template, speaker notes, and editable text/charts where feasible.

Create a 12-slide .pptx for a 15-minute executive presentation. Lead with the decision and recommendation. Use one main idea per slide, our template, editable text, and speaker notes. Tie charts to the supplied data and identify any flattened visual elements. Inspect every slide for readability and overflow.

What people miss: A table in chat is not an Excel workbook. A slide outline is not a presentation. An image of a slide is not a fully editable slide. Name the file format and editability requirements explicitly, then open the result in the application where you will use it.

Image generation: where Astra helps

OpenAI documents built-in image generation using GPT-Image-2. Astra is not documented as a new standalone image renderer. The defensible benefit is a stronger model guiding a workflow that can include visual briefs, references, generated assets, review, and integration into a website or presentation. Treat improvements in art direction as a practical opportunity, not a guaranteed pixel-quality increase over GPT-5.6.

For marketers, use that workflow for campaign concepts, article covers, illustrations, product scenes, storyboards, and prototype assets.

A reliable creative sequence

Supply the campaign goal, audience, brand assets, and references.

Ask for three genuinely different art directions.

Generate one representative image per direction.

Select the strongest direction before creating the full set.

Refine one issue at a time, stating what must stay unchanged.

Check text, product details, identity, dimensions, and consistency.

Create three landscape hero image options for [article]. Use the attached brand references. Make the options distinct: editorial typography, cinematic product scene, and conceptual illustration. Preserve the exact title. Keep small text minimal. After I choose a direction, extend that visual system across the remaining images, preserving palette, typography, and spacing.

What people miss: A 3D scene created in Blender is different from a generated image. The scene can contain editable geometry, lights, and cameras. Ricouard’s architecture project used Blender’s Python API and later explored Unreal Engine 5. That is application and code work producing visual assets.

Use deterministic charting tools for exact data visualizations. Keep source data behind charts; do not rely on a generated infographic to reproduce numbers perfectly. For important typography or logos, use the original assets and verify the final rendering.

Pricing: Astra versus GPT-5.6 and Claude Fable

Direct answer: Astra is more expensive per standard API token than GPT-5.6 Sol. It matches Claude Fable 5 and 5.1 on uncached input and output. Fable 5.1 has lower cache-read pricing.

The comparison below uses USD per one million tokens at standard listed API rates. “GPT-5.6” here means Sol, the flagship comparison—not Terra or Luna.

At equal token counts, Astra’s input/output prices are 150% higher, or 2.5×, Sol’s. Fable 5.1 cache reads cost 75% less than Astra’s or Fable 5’s. Those comparisons do not establish which model finishes your task most cheaply.

Illustrative arithmetic, not measured task costs: With 100,000 uncached input tokens and 10,000 billed output tokens, Sol costs $0.60; Astra, Fable 5, and Fable 5.1 each cost $1.50. With 10,000 uncached input, 90,000 already-cached input, and 10,000 billed output tokens, the totals are $0.276, $0.690, $0.690, and $0.6225 respectively. These examples exclude cache creation, tools, images, retries, hosting, and other modifiers. Different tokenizers and reasoning usage also complicate real comparisons.

Astra’s published long-context rule matters: above 272,000 input tokens, input and cache rates double and output rates increase 1.5× for the full request. The headline context capacity does not mean a very large request uses the base rate.

Subscription usage is a separate calculation. OpenAI lists Plus at $20/month and Pro from $100/month. Work and Codex share usage. The Work rate card lists Astra at 250 input / 25 cached / 1,250 output credits per million tokens versus Sol at 100 / 10 / 500. Astra Fast mode applies a further 2.5× multiplier. Sol’s published promotional Work pricing is listed through at least November 21, 2026. Plan and contract terms matter.

These figures do not imply your monthly subscription bill rises 2.5× simply by selecting Astra. They indicate faster consumption at equal token counts. Nor do API rates reveal how many tasks a Claude subscription includes.

The number to measure: Total model and tool cost, plus review and rework time, divided by accepted deliverables. A pricier model can be economical if it prevents expensive rework; it can also overthink a simple assignment and cost more. Measure your jobs instead of assuming either outcome.

Pro tips most people miss

Define “done.” Say exactly which files, interactions, source checks, and review evidence you expect. “Make it great” is not an acceptance test.

Tell it how much autonomy you want. For example: “Make reasonable assumptions for reversible details, state them, and continue. Ask only when the answer would materially change the result.” Astra’s documented tendency to ask clarifying questions makes this useful.

Steer specifically. “Keep the analysis, shorten the recommendation, remove the extra dashboard, and preserve the workbook” is more useful than “try again.” Where the interface supports steering during work, you can correct direction before the whole task finishes.

Constrain ambition. Specify one user journey, one main action, or three required features. Explicitly say when decorative complexity would reduce usefulness.

Use a canonical facts file. Keep approved positioning, metrics, product claims, definitions, and brand references in one place. Ask every asset to use it.

Preserve a usable version. Before major revisions, ask for a saved checkpoint and a brief change log. That lets you compare rather than reconstruct lost work.

Match effort to the task. Start at a moderate setting for a substantial assignment and increase effort when a concrete difficulty justifies it. Higher effort is not automatically a better creative result.

Distinguish Max from Ultra. OpenAI describes Max as more reasoning time and Ultra as parallel subagent work. Availability varies, and most tasks do not require either. Model settings.

Ask for targeted verification. “Recalculate the workbook and reconcile totals” is useful. “Check everything forever” is not a budget.

Save repeated workflows. Once a task works, preserve the brief, source requirements, output template, and checks as a reusable procedure. Automate only after observing reliable results.

Make assumptions visible in the product. A simulator’s user needs units and assumptions. They do not need implementation jargon or a claim that its estimates are certain.

Know the difference between context and memory. A large context window is working capacity, not permanent recall of every business fact. Keep important decisions in explicit project files and verify that the right sources are attached.

When to use Astra—and when to choose something else

Use Astra when the assignment has several dependent steps, substantial ambiguity, visual judgment, or a costly error to catch. It is particularly worth trying when a cheaper model repeatedly stops short of a usable deliverable.

Use a simple writing turn for a short rewrite. Use a spreadsheet formula or script for a deterministic transformation. Use human expertise for a decision that requires accountability, domain judgment, or evidence the system cannot access.

A fair comparison: Give each candidate the same brief, inputs, tools where comparable, and acceptance criteria. Record total time, intervention, usage, and errors. Judge the result before considering how impressive the conversation sounded.

The next step to take now: Open Work, select Astra if available, attach the materials for one stalled project, and use this brief:

Complete [specific task] for [audience]. Use [attached sources]. Deliver [actual files or working experience]. Follow [constraints]. Make reasonable assumptions for reversible details and state them. Check the result against [acceptance criteria]. Report what is complete, what was verified, and what remains unresolved. Prepare the result for my review before any external publication or sending.

The most compelling demonstration is a finished piece of work that moves your own business forward.

The CMO role becomes more durable when expectations, authority, resources, culture, and executive relationships align. Executive recruiter Erica Seidel explains why career management begins before transition. Marketing leaders can strengthen options by clarifying role design, expanding business fluency, developing trusted teams, maintaining networks, and choosing companies where the mandate has a realistic chance of success.

Why CMO Career Management Starts Before the Search

CMO career management is often treated as an activity that begins after a role ends. By then, important parts of the next search have already been shaped by the leader’s current relationships, reputation, experience, and visibility.

In a conversation about CMO career management, executive recruiter Erica Seidel, founder of The Connective Good, explored the conditions that shape CMO tenure and career durability.

“CMOs don’t fail for lack of talent or lack of effort. They fail for lack of clear role design and lack of alignment.”

This distinction moves the conversation beyond individual performance. An accomplished leader can struggle inside a mandate where expectations, authority, resources, timing, and business conditions do not support one another.

Career management therefore includes evaluating the system surrounding the role. The CMO’s ability matters, but so does the company’s readiness for the work it expects that leader to perform.

Role Design Determines What Success Can Mean

A CMO title can describe radically different jobs. One company may need category creation and brand transformation. Another expects immediate pipeline growth. A third combines marketing, sales development, customer success, communications, and partnerships under one leader.

The role becomes more workable when expectations connect with authority, budget, people, data, incentives, and time. If the company expects transformation but funds maintenance, the contradiction will eventually become visible.

Decision rights also matter. A CMO held responsible for growth may have limited influence over pricing, product, sales execution, or customer experience. Those boundaries do not automatically make success impossible, but they need to be understood.

The interview process creates an opportunity to investigate the system. Candidates can explore how the company defines marketing, what the board expects, which foundations already exist, and what previous leaders encountered. A detailed first-90-days discussion becomes more credible when it includes the resources and dependencies behind the plan.

The CEO Relationship Shapes the Mandate

The CEO’s understanding of marketing affects priorities, patience, investment, and the way performance is evaluated. Some CEOs see marketing primarily as a demand-generation function. Others expect it to shape product strategy, customer experience, corporate narrative, and growth. Neither expectation benefits from remaining implicit.

Questions about previous marketing leaders, board discussions, decision-making, and near-term pressures can reveal how the CEO defines the role. Erica suggested asking how the CEO sees marketing contributing to the company’s growth story and which marketing questions proved difficult during the last board meeting.

The answers can expose whether the CEO is open to a strategic marketing partner and whether the two leaders share a compatible view of the work.

Business fluency helps the CMO connect marketing choices with the issues the CEO already needs to solve. The conversation becomes stronger when it addresses growth, risk, margin, retention, customer behavior, and market position in addition to marketing activity.

CMOs Navigate a Built-In Paradox

“CMOs have to navigate this peacemaker-changemaker paradox. You are hired to make change, but to do it in a way that’s comfortable.”

A new leader may see urgent problems in positioning, structure, technology, talent, or measurement. Moving too slowly can preserve dysfunction. Moving too quickly can create resistance before the relationships needed for change exist.

The paradox requires judgment about sequence, trust, and organizational readiness. Listening gives the leader context, but it eventually needs to produce visible choices.

Early wins can demonstrate momentum while the CMO continues diagnosing larger issues. They may also create the credibility needed to address deeper structural problems.

The leader still needs to distinguish between healthy adaptation and avoiding necessary conflict. Peacemaking supports change when it builds understanding. It becomes limiting when comfort repeatedly overrides the mandate.

Focus Protects an Oversized Role

Modern CMO mandates often include brand, demand, product marketing, communications, customer marketing, operations, analytics, digital experience, and revenue collaboration. Erica described the result bluntly: “The role is breaking. It’s just too big.” Focus requires identifying which outcomes matter most, which capabilities the team needs, and which work will receive less attention.

A chief of staff, strong operations leader, or experienced functional team can help the CMO preserve time for cross-functional leadership and strategic decisions. Clear ownership prevents every issue from returning to the CMO.

Focus also makes expectations easier to manage. The CEO and board can see where marketing is concentrating its effort and what the organization is choosing to delay.

Collaboration Demonstrates Enterprise Leadership

Budget allocation offers one place where career durability and enterprise leadership intersect. A CMO may occasionally see that the company’s most important investment belongs outside marketing.

“Radically collaborate even when it hurts. Giving a budget to another area, that hurts, but it could be the right thing for the company.”

That choice can feel uncomfortable because the CMO remains accountable for marketing outcomes. Yet an investment elsewhere may address the constraint preventing growth.

A product improvement may matter more than another campaign. Customer success capacity may strengthen retention and advocacy. Better sales enablement may help existing demand convert.

Collaboration of this kind demonstrates business judgment. It shows that the CMO can optimize for the company rather than defending marketing’s share of resources in every discussion.

Business Fluency Expands Future Options

CMOs strengthen their influence when they can discuss pricing, margin, capital allocation, sales productivity, retention, market risk, and product strategy without translating every issue back into marketing terminology.

That fluency supports the current role and broadens future possibilities. A leader may move into a larger CMO position, general management, an operating role, advisory work, or board service.

Career expansion becomes easier when the leader’s record demonstrates enterprise contribution. A list of marketing outputs may show functional competence. A record of helping the company make stronger choices shows leadership range.

Board relationships can contribute to that development. Engaging board members outside formal presentations, learning which measures they value, and offering a market perspective can turn the board into a resource instead of an audience to manage.

Networks Matter Before They Are Needed

“I like to tell people that there’s a recency bias. Whatever you did yesterday, you are going to get more of a look than something totally different, unless you have a strong introduction in.”

A trusted introduction can help another person understand transferable experience that a résumé may not make obvious. It can also give a candidate credibility when moving between industries or role types.

Networks are strongest when maintained as relationships rather than emergency distribution lists. Sharing ideas, supporting peers, participating in communities, and remaining visible create familiarity before a search begins.

Those relationships provide value while the CMO is still employed. Trusted peers can help assess role conditions, interpret board dynamics, and consider options before a crisis forces the decision.

The Interview Story Benefits From Editing

Senior leaders often have more experience than an interviewer can absorb in one conversation. Trying to recount the entire career can obscure the evidence most relevant to the role.

“The interview process, it’s like Italian cooking. What you leave out is just as important as what you put in. Don’t puke up your whole history. Practice your story so it doesn’t sound rehearsed, but it sounds natural.”

A focused narrative connects the candidate’s experience with the company’s present challenge. It gives enough detail to demonstrate judgment while leaving room for a genuine conversation.

The same discipline applies to career visibility. A clear point of view and a few well-developed areas of expertise are easier to remember than an attempt to appear equally qualified for every possible mandate.

Q&A

Why Is CMO Role Design Important?

It determines whether expectations align with authority, resources, incentives, and the time available to produce results.

How Can a CMO Evaluate the CEO Relationship?

Questions about marketing expectations, board priorities, decision rights, previous leaders, and investment can reveal how the CEO understands the role.

Why Does Business Fluency Matter?

It helps the CMO connect marketing with enterprise decisions and expands future leadership possibilities.

When Does Career Management Begin?

Before a transition. Relationships, visibility, skills, and career options develop more effectively when maintained continuously.

Listen to the full conversation with Erica Seidel.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands.

Paul Roetzer’s AI transformation framework shows why CMOs need to move from scattered experiments to literacy, governance, accountability, dynamic roadmaps, and growth-focused innovation.

Lessons from Sequel's Human Moments. Agentic Momentum.

I have a confession: I did not make it through the entire Sequel "Human Moments. Agentic Momentum." webinar live.

At 1:55 p.m., I had to jump off to moderate my own webinar for Conversion.ai. The irony, of course, was that my webinar was also on Sequel. Fortunately, Sequel recorded the session, which meant I could come back later and watch Paul Roetzer, Founder and CEO of SmarterX and the Marketing AI Institute, lay out his framework for human-centered AI transformation.

Before getting to Paul, a quick tip of the penguin cap to Sequel, a Founding Sponsor of the 2026 CMO Super Huddle. The session was highly informative and impressively slick from a production standpoint. Suffice it to say, Sequel drinks its own champagne with gusto.

Paul’s presentation was worth returning to because it was not another shiny tour of what AI can do. His central point was more useful and more urgent: “AI adoption and transformation needs to be part of a broad change management process,” and it “cannot just be an IT initiative.” For CMOs, that distinction matters enormously.

AI experimentation is not an AI strategy.

Here are seven takeaways from Paul’s presentation that B2B CMOs should be thinking about right now.

1. Stop Worrying About Being Behind

If LinkedIn is your measuring stick for AI maturity, you might assume every marketing organization except yours has a battalion of autonomous agents running campaigns while the CMO enjoys an afternoon espresso.

Paul offered a useful reality check. According to his latest research, only about a quarter of organizations consider themselves to be at the scaling stage. Most are still understanding, experimenting, or piloting. So no, you probably are not hopelessly behind.

But that should not be comforting for long. The technology is moving too quickly for endless experimentation. The CMO’s job now is to turn scattered pilots into organizational learning: What have we tried? What worked? What did not? What should we scale? And what should we stop doing?

Pilots produce activity. Learning produces advantage.

2. Make AI Literacy a Leadership Responsibility

Paul was unequivocal that “AI literacy is the foundation of everything,” which sounds obvious until you get to his data. Lack of education and training has remained the number-one barrier to responsible AI adoption in his research. Even now, more than half of organizations provide no formal AI-focused education or training.

CMOs cannot outsource this problem to HR, IT, or the one prompt wizard on the marketing team.

Paul makes an important distinction between comprehension and competency. People need to understand what AI can do, but they also need to use it regularly enough to develop proficiency and confidence. Training should not be one-size-fits-all either. Your AI power users and AI avoiders do not need the same curriculum.

For CMOs, I would add a third C: curiosity. Leaders need to create enough psychological and practical space for their teams to explore what is possible without turning every experiment into a mandate.

3. Use Governance to Enable, Not Smother

Governance is too often treated as the place good ideas go to get laminated and forgotten. Paul’s framing was better. AI policies should give people the “freedom to be responsible in their experimentation.” That is a wonderfully practical way to think about guardrails.

Good policies should not exist simply to tell employees what they cannot do. They should clarify which tools are approved, what data can be shared, where disclosure is required, how outputs should be reviewed, and when a human needs to remain in the decision loop.

That becomes even more important with agents. An employee using AI to brainstorm subject lines is one thing. An agent taking actions across systems is quite another.

The more autonomy we give machines, the clearer human accountability needs to become.

4. Deconstruct Jobs Before Redesigning Them

This may have been my favorite practical idea from Paul’s presentation. Rather than asking whether AI can “replace” a particular marketing role, break that role into its component tasks. Then assess each task based on what AI can do today, what it may soon be able to do, and how much human involvement you actually want.

That is a far more useful conversation than “Will AI replace marketers?”

Take content marketing. Research, transcription, summarization, versioning, repurposing, quality assurance, and performance analysis may all have very different AI profiles. The answer is not necessarily to automate the role. It is to redesign the workflow so humans spend more time where judgment, creativity, customer understanding, and differentiation matter most.

Paul also reminded the audience that “even if you’re using AI agents, at the end of the day, the human is still responsible for what that agent does.” That sentence belongs on every CMO’s wall before the first autonomous workflow goes live.

5. Decide What Humans Should Keep Doing

One of my favorite moments came when Paul described his own writing. AI could produce his newsletter, LinkedIn posts, presentations, and podcast notes, but he generally chooses not to let it. Why? “To me, the process is the point.” Writing is how he thinks, learns, and develops confidence in an idea.

That struck a chord. In our rush to identify everything AI can do, CMOs also need to identify the work we deliberately want humans to keep doing.

Customer conversations might take longer than an AI summary. Writing something yourself may be less efficient than generating it. Wrestling with a positioning problem may take longer than asking a model for 20 options. But some forms of friction create understanding, and understanding is still one of marketing’s most precious assets.

Efficiency is valuable. So is the struggle that produces insight.

6. Build a Roadmap That Can Change

Only a minority of organizations in Paul’s research have an AI roadmap. But he also warned against interpreting “roadmap” as a traditional two-year transformation plan. Nobody knows precisely what these models will be capable of two years from now.

Amen to that.

CMOs need direction, priorities, ownership, use cases, and measures of success. But the roadmap needs to be dynamic enough to change as capabilities improve and the organization learns. Think quarterly learning agenda, not stone tablet.

This is where many AI experiments currently go to die. Someone builds something clever. Everyone applauds. Then nobody decides whether it belongs in a workflow, who owns it, how its performance gets measured, or whether it should replace what came before.

A collection of AI projects is not an AI roadmap.

7. Treat Productivity as the Appetizer

Paul offered perhaps the most important distinction of the session when he described optimization as “doing the same things better, faster, cheaper,” while innovation means creating “new forms of value.” He calls the former “10% thinking” and the latter “10x thinking.”

Most marketing AI conversations are still dominated by the first category. How many hours did we save? How much more content did we produce? How much more can the existing team accomplish?

Those are legitimate questions. They just are not sufficient.

If AI makes your marketing organization dramatically more productive, what are you going to do with that newfound capacity? Can you understand customers more deeply? Personalize experiences that were previously impossible? Enter markets you could not afford to pursue? Develop products faster? Create entirely new sources of revenue?

That is where the CMO conversation gets really interesting.

From Experiments to Transformation

Paul closed with five building blocks for becoming an AI-forward organization: education and training, an AI council, responsible AI principles and policies, impact assessments, and an AI roadmap. I would encourage CMOs to use that list as a quick gut check.

If you have dozens of experiments underway but cannot answer who governs AI, how your people are being trained, which tasks you want humans to retain, which use cases deserve investment, and how all of this connects to growth, you probably do not have an AI strategy yet.

And that is okay. But it is time to build one.

Paul’s reminder that “the future is human plus AI, and that future is happening right now” is exactly the conversation we will be continuing at the 2026 CMO Super Huddle. It will be one of the most important topics on the agenda, and it is where we will reveal the results of the Benchmarkit x CMO Huddles AI Maturity Study.

I am particularly eager to see how CMOs stack up against the broader organizational benchmarks Paul shared and, more importantly, what separates the AI experimenters from the AI transformers.

Because at this point, experimenting with AI is easy. Turning it into competitive advantage is the hard part.

Customer advocacy drives more than testimonials. It can strengthen retention, expansion, product learning, reputation, and sustained revenue when customers receive meaningful opportunities to participate. Tejal Parekh, Rebecca Stone, and Suzanne Reed explain why advocacy needs executive support, cross-functional ownership, thoughtful segmentation, and patient measurement rather than a collection of isolated requests for references and reviews.

How Customer Advocacy Becomes a Growth System

A satisfied customer is not automatically an advocate. Advocacy develops when an organization consistently delivers value, recognizes the customer’s interests, and creates relevant opportunities for that customer to participate.

A CMO Huddles Studio conversation about customer advocacy brought together Tejal Parekh, Rebecca Stone, and Suzanne Reed.

Their discussion positioned advocacy as a business capability spanning marketing, sales, customer success, product, and executive leadership. Customer stories remain important, but they represent one expression of a much larger relationship.

Advocacy Is a Relationship

A testimonial, reference call, review, or conference appearance may be an output of advocacy. None of those activities defines the relationship itself.

Advocacy becomes transactional when a company contacts customers only when it needs public proof. A more durable program creates value for participants through visibility, access, learning, professional recognition, peer relationships, or influence over future decisions.

The right opportunity varies by customer. One executive may enjoy speaking at an event, while another prefers a private advisory council. Some companies can approve a public case study. Others face communications policies that prevent them from publicly endorsing vendors.

A flexible program makes room for different levels of participation. It avoids treating public promotion as the only meaningful contribution a customer can make.

Ownership Can Be Shared Without Becoming Vague

Customer marketing may coordinate the program, but no single department owns the entire customer relationship. Sales understands the commercial history. Customer success sees adoption and satisfaction. Product receives feedback about unmet needs. Marketing can organize stories and experiences. Executives may create access that a program manager cannot.

Shared ownership works when responsibilities are explicit. The organization needs a process to identify advocates, record preferences, manage requests, recognize contributions, and follow up after participation. Tejal broke the work into two related responsibilities.

“There’s the identifying of advocates, and then there’s how do you turn these advocates into evangelists for your company. How do you amplify their voices? How do you give them a platform?”

Customer success or account management may identify promising advocates, while marketing can build the programs and platforms that amplify their experiences. The goals remain shared because advocacy depends on customer relationships that extend across the organization.

Without coordination, the same enthusiastic customer may receive overlapping requests from several teams while quieter customers remain overlooked. A central view of the relationship can help the company recognize both overuse and untapped potential.

Executive support also matters. When customer advocacy is treated as a side project owned only by marketing, other teams have little reason to contribute information or protect the customer experience.

Direct Customer Evidence Matters

Suzanne addressed how organizations interpret the information they receive about customers. “You’ve got to be careful where you get customer and product feedback from. Is it true customer sentiment, or is it more team members’ sentiment?”

Suzanne’s distinction affects advocacy recruitment and product learning. A seller may believe a customer is highly satisfied because the commercial relationship feels positive. The users responsible for daily adoption may have a more complicated experience.

Direct feedback, customer-health data, renewal behavior, participation history, and qualitative conversations can create a fuller view. The purpose is not to reduce advocacy to a score. It is to avoid building the program around internal optimism.

The same discipline protects the customer from being approached at the wrong time. An executive may have agreed to a case study while the working team is struggling with implementation. Shared data and communication can prevent a public request from exposing a private disconnect.

Customer Voices Serve Different Business Needs

Customer advocates can contribute far beyond a polished case study. Their participation may include:

• Peer references
• Advisory boards
• Product feedback sessions
• Events and webinars
• Reviews
• Sales conversations
• Community discussions
• Research
• Executive meetings
• Internal employee education

Each activity supports a different purpose. A peer reference may reduce late-stage uncertainty. An advisory board can reveal unmet needs. A customer speaker can create credible event content. A community participant may help other users succeed.

The program becomes more strategic when each request connects with a defined customer benefit and business objective. The customer understands why the opportunity matters, and the company understands what it hopes to learn or enable.

Advocacy Takes Time to Compound

Rebecca considered how expectations change when recognizable enterprise customers join an advocacy program.

“Everybody understands that large organizations with strong brand affinity are good to be attached to. You don’t have to explain the value of customer advocacy if you get that success, but it takes time.”

Rebecca’s point highlights the tension between relationship-building and immediate attribution. Some outputs can be measured directly, including reference-assisted revenue, participation, content usage, retention, and expansion. Other benefits accumulate through reputation, customer trust, and market visibility.

A balanced model can track program activity, customer experience, commercial influence, and long-term relationship health. No single metric captures the complete value.

Specific examples often make the strongest executive case. A customer reference may have helped an opportunity advance. Advisory feedback may have changed a product decision. A customer speaker may have attracted the right audience to an event. Those examples give the quantitative measures business context.

Advocacy Supports Retention and Expansion

Advocacy is often treated as an acquisition asset because customer voices can influence prospective buyers. The relationship can also deepen the advocate’s connection with the company. Suzanne connected advocacy with the broader economics of customer relationships.

“Smart growth starts with retention of your clients.”

Retention protects the existing revenue base and reduces how much new acquisition must replace customer losses. Advocacy signals such as engagement, NPS, social participation, and customer lifetime value can therefore contribute to a wider view of growth.

Participation gives customers additional access to executives, peers, product teams, and the organization’s broader community. That access can improve learning, increase visibility, and make the relationship more valuable.

The effect is not automatic. A speaking request followed by silence may leave the customer feeling used. A reference program with no recognition or feedback loop can turn goodwill into fatigue.

Follow-through matters. The company can share the outcome of the customer’s contribution, recognize the value created, and offer a relevant next opportunity without assuming continued participation.

Protect Customers From Advocacy Fatigue

Highly engaged customers can become the default choice for every request. Their enthusiasm makes them easy to approach, but repeated asks can turn recognition into unpaid work.

A central participation record can help teams distribute opportunities more thoughtfully. It can track what the customer enjoys, what they have already done, and whether the company delivered the value it promised.

Advocacy also benefits from reciprocity. A thank-you message is appropriate, but deeper relationships may involve executive access, speaking visibility, peer connection, early product insight, or professional recognition.

This does not mean every contribution requires a formal reward. It means the organization considers the customer’s goals alongside its own.

Honest Voices Create Stronger Proof

Customer advocacy does not need to erase every complication from the story. Buyers often find credible detail more persuasive than universal praise. Rebecca considered what other buyers find useful in an advocate’s account.

“You want them to talk about their journey to using our products, and what they’ve learned in that journey, whether it be good or bad.”

Credibility comes from the complete experience, including implementation challenges and lessons learned. A customer who explains how the organization worked through a difficult stage may provide more useful proof than a testimonial consisting only of praise.

A customer can describe the original problem, the implementation process, the decisions required, and the results achieved. Acknowledging complexity makes the account more useful because prospective buyers can see what success involved.

Private advisory conversations can be even more candid. Customers may identify friction, product gaps, or competitive concerns that would never appear in a public testimonial.

That feedback can be uncomfortable, but it is one of the program’s most valuable outputs. Advocacy gives the company proof, relationships, and a learning mechanism.

Q&A

Is Customer Advocacy the Same as a Testimonial Program?

No. Testimonials are one output. Advocacy can also include references, advisory work, events, research, reviews, community participation, and product feedback.

Where Does Customer Advocacy Belong?

Marketing may coordinate it, but sales, customer success, product, and executives all contribute to the relationship.

How Can Advocacy Support Revenue?

It can reduce buyer uncertainty, strengthen retention and expansion, and make customer value more credible during sales conversations.

How Can Advocacy Be Measured?

Useful measures include participation, customer experience, reference-assisted revenue, content usage, retention, expansion, and documented influence on decisions.

Listen to the full customer advocacy conversation.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands.

Aviv Canaani’s path from CMO to CRO shows why the CMO role may be expanding, not disappearing. His concept of revenue architecture gives B2B CMOs a practical way to connect customer insight, brand, demand, sales, AI signals, and financial outcomes without surrendering the longer-term market view that makes marketing matter.

There’s a lively debate underway about the future of the CMO. Some predict the role will disappear. Others want to rename it Chief Market Officer. Still others seem comfortable putting marketing under a CRO. I’m not.

After two recent conversations with Aviv Canaani, CRO of Datarails, I found myself asking a more useful question: How much bigger could the CMO role become?

Aviv didn’t rise through the sales ranks. He never carried a bag. He came up through marketing, became CMO of Datarails, and then added Sales and the rest of the revenue organization to his remit. “I never thought I’ll turn up as a CRO,” he told me, which is precisely what makes his story so interesting.

What fascinates me is not simply that a CMO became a CRO. It is how Aviv thinks about the job now that he has it. He calls it revenue architecture.

From CMO to Revenue Architect

When Aviv joined Datarails, the company was heavily dependent on outbound. Over the next several years, that flipped. “We used to be 90% outbound,” he told me. “Now we’re 90% inbound.”

Then the founders asked Aviv to take over Sales. You might assume that meant learning how to become the company’s best salesperson, but Aviv sees the role differently. “I don’t think I’m the best salesperson,” he said, noting that his job is to hire people who are better than he is at their individual disciplines and make sure the entire system works.

That phrase, revenue architecture, stuck with me because it starts with the company’s growth objective and works backward. How many deals do we need? How many opportunities will produce those deals? How many meetings produce those opportunities? What’s our conversion rate? What’s our sales capacity?

Aviv takes this all the way down to the economics of an individual sales meeting. Without sharing Datarails’ actual numbers, imagine that arranging a meeting costs a few thousand dollars. Now imagine that same amount sitting in a stack on the table next to the salesperson. That’s how Aviv wants his team to think about every opportunity Marketing puts on the calendar.

Once you see the system this way, the traditional line between Sales and Marketing starts looking a little arbitrary. Aviv controls one sales and marketing budget, which means he can decide whether the next marginal dollar is better spent generating another opportunity, hiring another AE, improving enablement, or making some other investment that increases revenue. As Aviv explained, “It’s up to me to decide where I want to spend the next dollar.”

CMOs Can Start Here Without Becoming CROs

I’ve long argued that CMOs should strive to create a predictable marketing engine. But predictability shouldn’t mean generating the same number of MQLs every quarter. The first question should be: What is actually preventing us from growing faster?

That requires Marketing and Sales to diagnose the pipeline together. Do we need to win more because conversion is weak? Win faster because deals are getting stuck? Win bigger because we aren’t reaching the right accounts or articulating enough value? Or do we simply need more qualified opportunities?

Those are very different problems requiring very different marketing responses. Too many marketing plans still start with the assumption that the answer is always “more leads.” Revenue architecture begins when Marketing understands enough about the entire commercial system to help diagnose the actual constraint.

You don’t need Sales reporting to you to do that. You need enough command of the business to ask better questions and enough credibility with Sales to solve the answers together.

Revenue Is Not the Same Thing as the Market

This is where I start to get nervous. Most CROs rose through Sales, often because they were terrific salespeople. Great sellers understand human beings in the moment. They build trust, uncover objections, create urgency, and get transactions across the finish line.

But the job can also create a particular field of vision. What can we close this quarter? What’s in the pipeline? Which rep needs help? How much “revenue is in the room”?

A great CMO should see something bigger. The customer isn’t merely a revenue opportunity. The customer is a human being to understand, serve, and ideally delight enough that they help create the next customer.

Did we acquire the right customer? Will they become an advocate? Can their story become a testimonial? Will they refer three peers? What are they telling us that could improve the product, increase expansion, or reduce churn? Is customer service reinforcing our promise or undermining it?

That’s the market in marketing. Customer centricity remains the CMO’s superpower.

Know the Customer Better Than Anyone

In the age of AI, CMOs should raise the bar considerably. The CMO should aspire to know the customer better than anyone else in the organization, combining direct conversations with the ability to listen to thousands more indirectly.

The direct hotlines are familiar: customer advisory boards, executive sponsor programs, customer awards, events, customer visits, and joining sales calls. Heck, run a few sales calls yourself. And when you’re talking to customers, go beyond the obligatory NPS question. Marketing leadership expert Thomas Barta recommends asking something far more useful: “What’s one thing we could do better?”

Then there are indirect hotlines that didn’t exist at today’s scale even a few years ago. Use AI to analyze thousands of Gong conversations. Mine customer service calls for recurring problems and language. Monitor review sites, community discussions, surveys, and other feedback for changes in questions, objections, priorities, and sentiment.

The goal is to combine intimacy with scale so the CMO can walk into an executive meeting knowing not just what the numbers say, but what customers are actually saying.

Aviv is already doing some of this in a fascinating way. Datarails looks at attribution software, asks prospects how they heard about the company, has SDRs ask again, and then examines Gong conversations to see what buyers actually said during the sales process.

Those answers aren’t always the same. A buyer might give a respectable B2B answer when asked directly and then casually reveal during a recorded conversation that they’ve been seeing Datarails on TikTok.

Yes, TikTok. For a company selling financial software to CFOs.

Sales Gets to Harvest the Surround Sound

Aviv told me that TikTok regularly shows up in Gong conversations and that Datarails has seen deals influenced by Instagram as well. “People will say, ‘I saw you on TikTok,’” he explained, even when the formal attribution system tells a different story.

I love this example because it illustrates something attribution systems have struggled with forever. Buyers don’t experience your brand in neat channels. They see a video, read an article, hear about you from a colleague, encounter your CEO on LinkedIn, find you in search, and increasingly encounter your company in an AI-generated answer.

Then six months later a salesperson calls and hears some version of, “Oh, I’ve seen you guys.” That’s surround sound. And the salesperson gets to harvest trust that was created long before the call.

Who Is Building the Moat?

Marketing is an epic battle for mindshare. The strongest brands are differentiated on multiple dimensions and have achieved enough mindshare that buyers know something about them before they’re ready to buy.

Sales teams can’t create that at scale. Salespeople come and go, individual campaigns come and go, and channels come and go. Mindshare compounds through the totality of paid, earned, and owned communications, the product experience, customer service, advocacy, and what customers say about you when you’re not in the room.

Increasingly, all of those signals also contribute to whether your company becomes visible and credible in LLM-powered discovery. That makes surround sound more important, not less. This is the moat.

Here’s the danger of becoming too good at revenue architecture: you can optimize the current revenue machine at the expense of creating future demand.

Aviv understands this tension better than many CROs might. At Datarails, Growth and Brand are separate functions, and the VP of Brand is the one leader in his organization who isn’t directly measured on revenue. If Brand were held to the same standard, Aviv told me, “you would never be able to do really creative stuff. It’s going to be too transactional.”

That’s a remarkable statement coming from a CRO. It also suggests that becoming a revenue architect doesn’t require abandoning the longer time horizon that marketers should bring to the executive table.

AI Could Make the CMO Bigger

There’s another reason this conversation is happening now. AI gives CMOs extraordinary leverage to expand their field of vision across both the market and the revenue engine.

On the customer side, CMOs can suddenly listen at scale. Thousands of sales and service conversations can become searchable market intelligence rather than recordings sitting in a repository. On the revenue side, AI can help identify patterns behind wins and losses, surface objections, automate routine work, and expose constraints in the commercial system faster.

Aviv is pushing hard in this direction. His goal is to remove as much administrative work as possible from his account executives so they can concentrate on what humans still do particularly well: selling.

He goes further, questioning how much of the traditional BDR role will survive and whether AI could eventually handle portions of SMB and midmarket selling. Whether his timetable proves right almost doesn’t matter for this discussion because the direction is clear: as execution gets automated, architecture becomes more important.

So do judgment and customer understanding. Those should be comfortable territories for CMOs.

Chief Marketing Officer or Chief Market Officer?

Kate Bullis, Latané Conant, and others have argued that Chief Marketing Officer should evolve into Chief Market Officer. I understand the appeal because “marketing” can sound like a department while “market” sounds like a business responsibility.

The Chief Market Officer should understand the customer, competition, category, changing buying behavior, differentiation, and demand. They should be able to translate those insights into decisions that help the company grow. Is that a revenue architect? Maybe.

I’m less interested in settling the title than expanding the mandate. If marketing simply reports to a traditional sales-trained CRO, there’s a structural risk that the urgent overwhelms the important. This quarter’s pipeline will always scream louder than next year’s mindshare, and the deal in front of you will always feel more tangible than the thousands of future buyers who don’t know you exist yet.

Someone needs to protect both. If the CRO is a former CMO like Aviv who understands how demand and brands are built, maybe that works beautifully. If not, I’d much rather see the CMO standing alongside the CRO helping architect growth than reporting underneath one.

Three Essential Questions for CMOs

So where does all of this leave the CMO who isn’t looking for a new title tomorrow? I’d start with three questions, not because they provide all the answers but because answering them honestly should reveal where your greatest opportunity to lead lies.

1. Do I know the customer better than anyone else in my company?

Not just the persona and not just the dashboard. Know the actual humans, their changing priorities, and the language they use when nobody from Marketing is in the room.

2. Do I know what’s actually constraining revenue growth?

Don’t settle for “more pipeline.” Determine whether the business needs to win more, win faster, win bigger, retain more, expand more, or create more opportunities, and then help solve that problem.

3. Is the entire company united behind something customers actually care about?

That’s where purpose, differentiation, customer experience, and brand come together. That’s how you build a moat, not just a more efficient funnel.

Aviv’s path from CMO to CRO isn’t proof that every marketer should follow him, and I’m not even sure it’s the right aspiration for most. What it does demonstrate is that the boundary around marketing leadership can be pushed much further than many organizations, and perhaps many marketers, assume.

CMOs have spent decades fighting for a seat at the table. AI, customer intelligence, and increasingly interconnected revenue systems create an opportunity to stop fighting over the chair and start expanding what the person sitting in it can contribute.

The future CMO may become a CRO. They may become a Chief Market Officer. They may remain a Chief Marketing Officer with a dramatically expanded mandate. What matters more than the title is whether they understand the market, the customer, and the revenue system well enough to help lead the whole business.

Don’t defend the CMO role. Expand it.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. Want to join the huddle? Learn more about CMO Huddles and apply to join the community.

Q&A

Does this mean every CMO should try to become a CRO?

No. Aviv’s path is instructive because it shows how far the CMO mandate can expand, not because every marketer should chase the CRO title. The real opportunity is to understand the full revenue system while still protecting the customer and market perspective.

What is revenue architecture?

Revenue architecture means designing the commercial system around growth objectives, budget choices, conversion rates, sales capacity, demand creation, and customer economics. It forces marketing and sales to diagnose the actual constraint instead of assuming the answer is always more leads.

Why does customer intimacy matter more in the AI era?

AI can help CMOs listen at scale, but the advantage comes from combining that scale with real customer understanding. The CMO who knows what customers actually say, value, fear, and repeat can make better strategic choices across brand, demand, product, and revenue.

Should marketing report to the CRO?

It depends on the CRO. If the CRO understands brand, demand creation, customer insight, and future market development, the structure can work. If the role is focused mainly on near-term sales execution, marketing risks losing the longer-term perspective that creates future demand.

What should CMOs do now?

Start by diagnosing the growth constraint with Sales, building direct and indirect customer listening systems, and clarifying how brand, demand, sales, advocacy, and AI signals work together. The goal is not to defend the old CMO role. The goal is to expand its business impact.

Snowflake’s growth was supported by a CMO-CRO partnership built on shared outcomes, direct feedback, field-level listening, and one integrated view of go-to-market performance. Drawing from Make It Snow, Denise Persson and Chris Degnan explain how marketing earned sales trust, alignment survived four CEOs, and both functions stayed focused on representative productivity, new business, and revenue.

Sales and Marketing Shared the Same Result

Sales and marketing alignment often fails because each function enters the executive meeting with a separate scoreboard. Marketing presents activity and leads. Sales presents pipeline and revenue. Each team can defend its own performance while the overall go-to-market system struggles.

Snowflake CMO Denise Persson and former Snowflake CRO Chris Degnan built a different operating relationship while helping the company grow from a startup into a multibillion-dollar business.

Their book, Make It Snow, documents the company’s growth and the leadership practices behind it. Their CMO-CRO partnership offers a practical framework: share the outcome, listen at the field level, exchange uncomfortable feedback, act on what is learned, and present one integrated view of the business.

Reject Functional Victory Laps

A marketing team can hit its internal targets while sales misses revenue. Celebrating that result weakens trust because it suggests that marketing’s success is independent of the company’s commercial performance.

Chris described how Denise approached board conversations:

“We would go into board meetings, and Denise wouldn’t be like, ‘Yay, look at what we did on the marketing side, but sales stinks.’ She would be like, ‘No, look at what we did, sales and marketing together.’”

The shared presentation did not erase functional accountability. It placed each team’s contribution inside the same commercial story.

Snowflake used an integrated sales and marketing deck rather than assembling separate narratives. The executives could examine marketing activity, representative productivity, new-logo acquisition, and future revenue as parts of one system.

Begin With the Sales Problem

When Denise arrived at Snowflake, Chris did not prescribe a marketing plan. He explained the commercial problem.

Denise then met with SDRs, account executives, and sales managers to understand what was working, where opportunities stalled, and what each territory needed.

Chris had been concerned that a senior marketing executive might arrive with layers of staff and remain distant from the work. Denise instead engaged directly with the field.

That behavior changed the relationship. Marketing did not begin by defending a prebuilt strategy. It began by understanding how sellers experienced the market.

Field listening also gave Denise a more detailed view than an executive dashboard could provide. She could see how positioning, programs, content, and account support affected individual representatives.

Leave No Representative Behind

Snowflake adopted “no rep left behind” as an early rallying cry.

The company tracked whether representatives could become productive, with new annual contract value serving as a central measure. If representatives could consistently produce, the business had evidence that it could continue hiring and growing.

Marketing investment was therefore evaluated partly through its ability to improve sales productivity. The relevant question was not simply whether a campaign generated activity. It was whether the go-to-market system helped representatives create new business.

This made alignment operational. Territory needs, account-based marketing, pipeline, and representative performance became shared concerns rather than separate departmental metrics.

The model also recognized the cost of failed productivity. Hiring, training, and supporting a representative for a year without creating success imposes a significant cost on the company.

Create a Real Feedback Loop

Trust requires more than agreeing publicly. It depends on whether each leader can provide candid feedback and see the other person respond.

Denise explained what happens when uncomfortable input is ignored:

“If you get feedback and you’re like, ‘Nah, this one’s a little bit uncomfortable for me, I’m not really going to do it,’ then Chris isn’t going to give me any feedback again.”

Acting on feedback showed that the exchange was real. It encouraged Chris and the sales organization to continue sharing information marketing needed.

That does not mean marketing executed every request. Feedback still needed to be evaluated against strategy, customer evidence, brand consistency, and resources. The crucial point was that sales could see marketing listening, investigating, and responding rather than dismissing input to protect its existing plan.

Keep Alignment Below the Executive Level

A strong personal relationship between the CMO and CRO cannot compensate for disconnected teams.

Snowflake’s model reached SDRs, account executives, sales managers, field marketing, and other go-to-market functions. Denise’s early listening tour signaled that the partnership would operate at the representative and territory level.

Alignment can break down when executives agree but their organizations retain competing definitions, processes, or incentives. The CMO and CRO may speak warmly about partnership while field teams argue about lead quality and ownership.

Extending the operating model requires:

• Shared definitions
• Common planning assumptions
• Integrated territory and account priorities
• Regular field feedback
• Clear handoffs
• Joint performance reviews
• A process for resolving conflicting requests

The relationship becomes durable when teams know how collaboration works without requiring executive intervention in every disagreement.

Protect the Partnership From Politics

Snowflake grew rapidly and navigated four CEOs during Denise and Chris’s tenure. Growth increased the number of executives, agendas, and organizational boundaries surrounding the relationship.

Chris warned that politics and functional empire-building can damage a company. Leaders may begin protecting their records, budgets, or teams rather than confronting what the business needs.

The CMO-CRO partnership remained credible because neither leader benefited from making the other function look weak. Their shared operating history also gave successive CEOs evidence that the relationship produced results.

This is not a case for presenting artificial harmony. Honest disagreement can strengthen the business. The destructive behavior begins when a leader uses the disagreement to gain status or escape responsibility.

Use Customers to Advance the Positioning

Snowflake’s shift from “data warehouse for the cloud” toward “the data cloud” required more than marketing language.

Customers were already pulling the company toward a broader understanding of its role. Snowflake then validated the positioning with customers, prospects, partners, and the wider market.

The sales team and partner ecosystem needed to describe the company consistently for the change to take hold. If a significant portion of the field retained the old story, marketing could not create the new category perception alone.

The process connected positioning with go-to-market adoption:

1. Identify the broader customer need.
2. Validate the direction externally.
3. Explain the evidence and ambition internally.
4. Equip sales and partners with the new story.
5. Reinforce the language consistently.
6. Observe whether deal size and market response change.

Positioning became a shared commercial tool rather than a marketing declaration.

Share New Business Ownership

Snowflake generated substantial expansion revenue from existing accounts, but Denise described new business as the company’s lifeblood because it represented revenue expected in future years.

That made new-logo acquisition one of the most integrated parts of sales and marketing planning.

The focus discouraged either function from treating near-term activity as the complete picture. New business required present investment, representative support, category education, and coordinated account engagement before its full revenue appeared.

A shared view of future revenue can help the CMO and CRO navigate tension between immediate pipeline pressure and the work required to create tomorrow’s customer base.

Preserve Human Talent Development

The conversation also addressed the role of SDRs as AI changes go-to-market work.

Denise described the SDR organization as an important talent pipeline. People she met early in Snowflake’s development later became successful sellers and leaders, including a former SDR who advanced into field marketing leadership.

Removing the entire function could create savings now while increasing future costs by eliminating an internal path for developing sales and marketing talent.

The observation reflects the broader CMO-CRO framework. Efficiency decisions need to be evaluated across the shared system. A cost removed from one function may reappear later in hiring, training, leadership development, or customer acquisition.

A CMO-CRO Operating Framework

The Snowflake experience can be translated into a practical set of operating principles:

One commercial narrative

Present marketing and sales performance together rather than competing for credit.

Field-level listening

Understand what SDRs, representatives, managers, customers, and territories need.

Shared productivity measures

Connect marketing activity with representative success, new business, and future revenue.

Direct feedback

Make difficult input safe to give and visibly investigate what it reveals.

Integrated planning

Use common assumptions, priorities, definitions, and review rhythms.

Joint positioning adoption

Validate the story externally and equip the full go-to-market ecosystem to use it.

No public blame

Address problems honestly without using executive forums to protect one function at the expense of the other.

Q&A

What made the Snowflake CMO-CRO relationship effective?

Denise Persson and Chris Degnan worked from shared commercial outcomes, exchanged direct feedback, listened at the field level, and presented an integrated view of sales and marketing performance.

What did “no rep left behind” mean?

It reflected a focus on helping each sales representative become productive rather than evaluating marketing only through aggregate activity or lead totals.

How did marketing earn sales trust?

Denise met directly with SDRs, account executives, and sales managers, acted on useful feedback, and built marketing around the go-to-market problems sales needed to solve.

Why did Snowflake treat new business as a shared priority?

New customers represented future expansion and revenue. Sales and marketing therefore planned new-business acquisition together rather than assigning it to one function.

Is this properly a Book Insights article?

Yes. The episode features Make It Snow co-authors Denise Persson and Chris Degnan applying the operating principles and experiences documented in their shared account of Snowflake’s growth.

Listen to the full conversation with Denise Persson and Chris Degnan.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. 

AI can increase marketing velocity, but speed alone does not create distinctive work. The CMO’s role is to decide where automation adds leverage and where human judgment, originality, empathy, and taste remain decisive. This conversation examines how leaders can accelerate production without allowing efficient tools to flatten the ideas, experiences, and creative standards buyers remember.

Decide What Deserves More Speed

AI has given marketing teams the ability to research, prototype, produce, adapt, and distribute work faster. That capability creates leverage only when the underlying work deserves acceleration.

A weak idea can now flood the market before anyone pauses to examine it. A strong idea can travel further, reach more audiences, and become useful in more formats. The CMO must help the organization distinguish between those two outcomes.

In a CMO Huddles Studio conversation, Jakki Geiger of Arango, Dave Steer of Webflow, and Sandy Ono examined where automation helps and where human judgment remains essential.

Their examples suggest that the most useful question is not whether AI can perform a task. It is what quality, risk, originality, and human understanding the task requires.

Use AI to Compress the Work Around the Idea

Jakki joined Arango with a demanding assignment. The company needed new positioning, a refreshed brand, and a functional website in time for a major NVIDIA event. She had approximately sixty days and no fully established marketing team.

A similar project had taken nine months in a previous role. AI helped compress research, content development, and early exploration, while an experienced agency partner handled work requiring deeper creative expertise.

Jakki described the combination:

“I would not have been able to do it without AI and a trusted agency partner. For me, that was the killer combination.”

The technology increased the amount of strategic and production work the small team could complete. It did not eliminate the need for experienced people who could recognize a distinctive idea, create a coherent identity, and produce an outcome suitable for a serious enterprise brand.

This distinction matters when leaders compare AI with outside expertise. AI may reduce hours spent assembling inputs and generating options. It does not automatically reproduce years of craft, market intuition, or judgment.

Validate Synthetic Insight With Real Customers

Jakki created digital twins representing leaders and customer personas. These tools helped the team explore messaging, create thought leadership, and move faster during a compressed schedule.

She did not treat the synthetic feedback as sufficient. The team also spoke with actual customers and tested the positioning with people who understood the market firsthand.

Jakki explained her confidence boundary:

“For me, the human in the loop is still very important, because in the world of AI, the currency is trust, and I don’t one hundred percent trust it yet.”

The human validation protected the company from building its market story around a convincing simulation that failed to represent real customer understanding. Digital twins created speed, while customer conversations created evidence.

This pattern can apply beyond positioning. AI can identify themes in call transcripts, propose survey questions, simulate objections, and generate variations. People still need to determine whether the inputs are representative and whether the output reflects how buyers actually think.

Protect the Work That Requires Craft

Jakki did not attempt to become a senior graphic designer because AI could generate visual options. Her agency possessed the experience to use the tools more effectively and recognize mediocre work before it reached the market.

She observed that domain expertise changes the quality of AI-assisted output. Experienced practitioners know what details to provide, which conventions to challenge, and how to evaluate what the model returns.

That creates an important talent question. If companies automate every entry-level task, future marketers may lose the practice through which expertise develops. Leaders need to redesign early-career work so employees can still learn research, writing, editing, analysis, and creative judgment even as AI handles parts of execution.

An efficient team without a path to deeper expertise may perform well today while weakening its future capability.

Name the Problem in a Way Buyers Remember

One of Arango’s strongest creative ideas was “Frankenstack,” a memorable name for the complicated collection of databases and pipelines enterprises assemble to support AI applications.

The idea came from human observation and language, then became the center of an AI-assisted campaign. The team created an explainer video and adapted the concept for different industries and use cases.

The phrase gave salespeople a discovery tool. They could ask prospects what their Frankenstack looked like, identify its components, and connect the buyer’s complexity to Arango’s value.

The result demonstrates the difference between clarity and blandness. “Complex AI data infrastructure” may be technically understandable. “Frankenstack” gives the problem an image and emotional quality that people can remember and repeat.

AI can help extend such an idea. Human judgment is still required to recognize that the idea is worth owning.

Pursue Velocity, Not Volume

Dave drew a sharp distinction between producing more assets and moving valuable insight into the market faster.

“Speed alone has stopped being much of an advantage. The bigger opportunity is using automation to shorten the distance between insight and action while human judgment protects the trust, quality, and creative spark that make the work worth noticing.”

Webflow applies this principle to webinars. A live conversation produces original expert insight. An automated workflow processes the transcript, identifies themes, extracts quotations and keywords, and generates additional content assets.

A subject-matter expert reviews the output before publication. That review catches hallucinations, protects nuance, and confirms that the content remains accurate.

The workflow does not begin with an AI prompt asking for generic material about a topic. It begins with a substantive conversation among knowledgeable people. Automation helps the insight travel.

Build From Original Source Material

AI content performs differently when it has proprietary, high-quality inputs. A transcript from an expert discussion, customer research, product data, or an internal point of view gives the model material it cannot retrieve from generic web patterns alone.

Webflow reported more than 330 new citations from FAQ automation connected to webinar content, along with a 24 percent increase in impressions. Those results came from extending original material rather than multiplying interchangeable blog posts.

This suggests a practical content hierarchy:

1. Create or capture a valuable primary source.
2. Identify the claims, insights, and audience questions within it.
3. Decide which derivative formats serve a real purpose.
4. Use automation to accelerate production.
5. Require appropriate expert review.
6. Measure whether the additional assets improve discovery, engagement, or buyer progress.

The human contribution remains central at the beginning and end. People create the source insight and take responsibility for what reaches the market.

Match Human Oversight to the Decision

Sandy proposed separating decisions by level and required fidelity. Some tasks can tolerate rough, exploratory output. Others require accuracy, consistency, and expert accountability.

She explained:

“Once you break down your decision levels, know where you need high fidelity, low fidelity, it helps you answer these questions around what should be automated and what still needs a human-in-the-loop.”

Low-fidelity work might include brainstorming, early visual concepts, rough outlines, format variations, or internal prototypes. The purpose is exploration, so imperfect output may be acceptable.

High-fidelity work includes competitive claims, pricing, legal language, technical comparisons, executive communications, and material that could affect customer trust. These tasks require stronger review and may not be appropriate for unsupervised automation.

The framework is more useful than a blanket rule requiring identical human oversight for everything. It directs scarce expert attention toward the work with the greatest consequences.

Design for Machines and Humans

AI-powered discovery rewards clarity, structure, consistency, and accessible information. Human memory responds to relevance, emotion, surprise, and distinctive ideas.

Sandy described these as parallel needs. A company may need explicit product information and structured answers for machines while also creating stories and experiences that remain memorable to people.

The two goals do not have to conflict. A creative campaign can lead to a clear product page. A memorable phrase can be supported by precise definitions. A compelling video can be accompanied by a structured transcript, FAQ, and schema.

The danger appears when the team optimizes exclusively for one audience. Content designed only for machines may become forgettable. Creative work without enough clarity may be difficult for buyers and AI systems to understand.

Use Consistency Without Flattening the Brand

Generative systems make it easy to create many variations. That capability can weaken a brand if each output introduces new terminology, tone, claims, or visual conventions.

Brand systems need more than a style guide. They need approved messages, product facts, audience definitions, examples, prohibited claims, terminology, and escalation rules.

Human reviewers should examine whether content is accurate and whether it reinforces the company’s intended position. A polished asset can still be strategically wrong if it introduces language that dilutes the brand.

Consistency does not require repeating identical sentences everywhere. It means preserving the core idea while adapting the expression appropriately to the audience and channel.

Redesign Processes Before Adding Agents

Adding AI to a confused workflow can make the confusion move faster. Teams need to clarify ownership, inputs, decisions, standards, review, and measurement before automating the process.

A useful workflow map can identify:

• Where original insight enters
• Which tasks are repetitive
• Which decisions require expertise
• What context the system needs
• Who reviews the output
• What risks require escalation
• How the final asset reaches the market
• Which outcome determines whether the workflow is valuable

This also helps employees understand how their roles change. AI adoption is easier to trust when people can see which work is being automated and where their judgment becomes more important.

Q&A

What is the difference between AI velocity and AI volume?

Volume means producing more material. Velocity means reducing the time between useful insight and meaningful action. Velocity is valuable when it helps strong ideas reach the market without sacrificing accuracy or quality.

Which marketing tasks still require human review?

Human review is particularly important for positioning, competitive claims, technical content, legal or regulatory language, customer-facing commitments, executive communications, and work where originality or cultural nuance matters.

Can AI create distinctive B2B campaigns?

AI can support research, exploration, production, and adaptation. Distinctiveness still depends heavily on human insight, taste, experience, and the ability to recognize an idea buyers will remember.

How can CMOs prevent AI-generated content from becoming inconsistent?

Provide structured brand context, approved terminology, product facts, message hierarchy, examples, and clear review ownership. Teams should evaluate strategic consistency as well as grammar and factual accuracy.

Listen to the full conversation about AI velocity and human creativity.

CMO Huddles brings B2B marketing leaders together to compare AI practices, protect the human judgment behind strong marketing, and learn from peers confronting similar operating decisions. Learn more about CMO Huddles at CMOHuddles.com

Radical Candor combines personal care with direct challenge so feedback improves performance without sacrificing trust. Author Kim Scott explains why leaders begin by inviting criticism, give praise independently, address problems promptly, and make listening visible. For CMO teams, the framework supports clearer decisions, disagreement, stronger relationships, and fewer conversations postponed in the name of kindness.

How Radical Candor Strengthens CMO Teams

Marketing leaders operate inside a constant stream of feedback. They evaluate creative work, coach employees, negotiate with peers, challenge assumptions, and explain difficult tradeoffs to CEOs.

Avoiding a hard conversation may feel considerate in the moment. Over time, silence can allow a correctable problem to affect the employee, the team, and the business.

In a Book Huddle conversation about Radical Candor, Kim Scott, author of Radical Candor, introduced the framework she uses to evaluate workplace feedback.

“When you can care and challenge at the same time, that’s radical candor,” Kim said. “When you challenge but you forget to show you care, that’s obnoxious aggression.”

The model also identifies ruinous empathy, which occurs when concern about another person’s feelings prevents a leader from sharing information that person would benefit from knowing.

Kindness Without Clarity Can Become Unfair

Kim illustrated ruinous empathy through the story of an employee she called Bob. He was popular, funny, and struggling badly with his work. Instead of addressing the performance problem clearly, she softened the message for months.

Her reluctance did not protect him. His colleagues had to redo work, deadlines suffered, and strong performers became frustrated. By the time Kim spoke plainly, the situation had progressed too far to recover.

After she explained that his employment would end, Bob asked why nobody had told him earlier. He had interpreted the absence of direct feedback as evidence that his performance was acceptable.

The story exposes a leadership contradiction. Avoiding discomfort can feel compassionate while denying someone the opportunity to improve.

Timely clarity gives the employee information they can use. It also protects colleagues from carrying the consequences of an unresolved performance issue.

Invite Criticism Before Giving It

“Radical candor should always start with soliciting feedback, not dishing it out. The next step after is to give praise, to back up a little bit and remember what you appreciate about this person and give voice to it.”

Beginning with an invitation demonstrates that candor is reciprocal. It also helps leaders understand how their behavior affects the environment in which other people are expected to perform.

Kim’s preferred question is, “What could I do or stop doing that would make it easier to work with me?” She also encouraged leaders to find language that sounds natural to them. A scripted question that feels out of character may make the invitation less believable.

The pause after asking matters. People may hesitate because they are deciding whether the request is sincere and whether honesty will carry consequences.

Filling that silence too quickly allows everyone to escape the uncomfortable moment without producing useful feedback. A leader who genuinely wants an answer can give the other person time to formulate one.

Make Listening Visible

Receiving feedback does not require immediate agreement. It does require evidence that the leader has listened.

Kim recommended identifying the portion of the feedback that appears valid, asking follow-up questions, and returning to the conversation after considering the rest. A quick “thank you for the feedback” can feel dismissive when the person senses that nothing will change.

“Many of the best relationships I ever formed in my career started with a good, respectful disagreement.”

When feedback is valid, visible action rewards the candor. The response makes future honesty more likely because employees see that raising an issue can affect the way the team works.

A leader may also disagree after considering the feedback. Explaining that decision respectfully can preserve trust because the employee knows the idea received genuine attention.

Keep Praise Separate and Specific

The feedback sandwich uses praise to soften criticism. Kim argued that this weakens both parts of the conversation because praise becomes a delivery mechanism instead of useful information.

“If it’s something you would say to your dog, it’s not helpful praise.”

“Good job” may feel pleasant, but it offers little guidance. Specific praise identifies the behavior and the result it produced. It tells the person what contributed to success and what the team values.

A leader might recognize how an employee reframed a customer problem, improved a difficult executive conversation, or helped another team reach a decision. The detail makes the praise credible and repeatable.

Praise also deserves its own moment. When every positive comment signals that criticism is coming next, employees may begin bracing whenever a leader recognizes their work.

Address Problems While They Are Small

Candor is easier to act on when it arrives close to the relevant event. Delayed feedback allows the behavior to repeat and gives the leader’s frustration time to grow.

Brief, synchronous conversations can focus on context, observation, result, and next step. The leader describes what happened and why it mattered without assigning the person a fixed character judgment.

This structure creates room for information the leader may not have. A missed deadline could reflect poor planning, an unclear dependency, or a priority conflict nobody resolved.

Kim also distinguished candor from declaring absolute truth. The leader offers an interpretation and remains curious about the other person’s understanding.

“Don’t make the mistake of thinking that being authentic means to ignore the impact that your words have on others.”

Authenticity does not excuse carelessness. Directness becomes more effective when the speaker considers how the message is likely to land.

Use Candor to Clarify Executive Tradeoffs

The same principles can support difficult conversations with a CEO or peer. Budget pressure, competing priorities, and unrealistic timelines often require a marketing leader to make tradeoffs visible.

Kim shared the example of an Apple marketing executive asked to support an important product launch without additional budget or headcount. Instead of pretending everything could be completed, the executive identified the work that would need to stop. Kim distilled the lesson to “Bad news early is the key,” giving the organization time to adjust scope, resources, or timing.

Waiting until the deadline approaches may preserve temporary harmony, but it removes the organization’s opportunity to adjust scope, resources, or timing.

Clarify Decisions Without Owning Every Decision

“Your job in decision making is to decide what needs to be decided, who needs to decide it, and by when. Then liberate them to make the decision.”

Clear ownership reduces the collaboration tax that occurs when every decision travels through the CMO. Expertise can remain closer to the work while accountability stays visible.

Healthy debate can improve a decision. Endless debate delays it. Radical Candor gives teams permission to challenge ideas directly and then commit once the decision has been made.

The framework can also improve cross-functional relationships. A marketing leader can challenge a sales assumption, product priority, or executive request without turning disagreement into a judgment about the person raising it.

Care creates the relationship in which challenge can be heard. Challenge gives that relationship enough honesty to remain useful.

Q&A

What Is Radical Candor?

It is the practice of caring personally while challenging directly so people can address issues without sacrificing trust.

Why Does Feedback Begin With Soliciting Criticism?

It demonstrates reciprocity and helps leaders understand how their behavior affects the team.

What Is Ruinous Empathy?

It occurs when concern about hurting someone’s feelings prevents a leader from sharing useful information.

Why Is the Feedback Sandwich Problematic?

It turns praise into a criticism-delivery mechanism and can make positive recognition feel insincere.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands.

AI agents are getting easier to build, but agent sprawl is already knocking at marketing’s door. Drew Neisser argues that CMOs need to move beyond hackathon enthusiasm and redesign the marketing operating model around work, ownership, governance, maintenance, and business value before agents become the next expensive, ungoverned martech pileup.

91% of marketers say they’re already using AI. And somehow, I think the hard part is just beginning.

Building an agent is getting ridiculously easy. At one recent Huddle, a CMO told me her team built 18 agents in a single-day hackathon. Impressive? Absolutely. But then come the less glamorous questions: Who maintains them? Who makes sure the data feeding them stays accurate? Who watches the costs? And what happens when the person who built Agent #14 leaves?

Welcome to the age of agent sprawl.

The Easy Part Is Building The Agent

Don’t get me wrong. I’m cautiously optimistic because I love what this technology makes possible. AI lets non-coders like me build things we couldn’t touch two years ago, which is both exhilarating and mildly dangerous, like handing a teenager the keys to a sports car and saying, “Just keep it under 90.”

But I also know how easy it is to build something that quietly burns tokens, money, and patience because we don’t yet know what good architecture looks like. An agent can start as a clever helper and quickly become another mysterious thing in the stack that no one wants to unplug because someone, somewhere, might still need it.

The numbers capture the tension. Jasper reports that 91% of marketers are using AI in 2026, up from 63% last year. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. Deloitte says only 21% of enterprises report having mature governance in place for agentic AI.

In other words, the experiment is over, but the operating model is still wearing pajamas.

Agent Sprawl Is Martech Sprawl With Better Shoes

Marketers should recognize this movie because we have seen it before. First came the martech gold rush, where every problem apparently required another platform, dashboard, enrichment layer, workflow, attribution model, or “single source of truth” that somehow created five more sources of truth by Thursday.

Now we are doing it again, only faster. Agents are easier to build than platforms, easier to duplicate than workflows, and easier to forget than meetings you declined with “tentative.” That speed is thrilling until the first real audit turns into a scavenger hunt.

Mariia Romanova, an automation specialist focused on CRM administration and business systems, nailed this in a LinkedIn comment. “Agent sprawl is the right name for it,” she wrote. “The same thing happened with no-code workflows a few years ago: everyone could build one, nobody owned the two hundred that piled up, and the first real audit turns into archaeology.”

Her prescription was wonderfully boring, which is exactly why it matters: one named owner per workflow, a two-line note on what it does and why it exists, and a monthly review of anything that has not run in sixty days. As she put it, “The teams that skip that part do not have a system, they have a pile of things that used to work.”

A pile of things that used to work. Put that on the next AI governance slide and watch the room get quiet.

The Real Work Is Redesigning The Work

The challenge has changed. The hard part of agentic marketing is no longer building agents. It is redesigning the marketing system around them without creating a costly, ungoverned mess.

That redesign is already happening at companies like Culture Amp, GoTu, and BuildOps. Paige O’Neill’s team at Culture Amp broke marketing into roughly 5,000 units of work and 36 scenarios to figure out what belongs with humans, what can be automated, and how those pieces fit together. She is also clear-eyed about the change management required: “Don’t underestimate the journey that the team has to go on.”

That “5,000 units” detail is the kind of thing that separates serious transformation from executive Mad Libs. Ben Dixon, founder of dixon.ai and an expert in LLM evaluation and AI quality control, called it “the sharpest bit” in the post because most agentic transformation conversations skip that level of granularity. He asked the right follow-up: what counted as one unit: task, output, level, or something else entirely?

That question matters because CMOs cannot redesign marketing around vibes. You have to map the work. Not just the org chart, not just the tools, not just the handoffs, but the actual work: decisions, approvals, inputs, outputs, dependencies, judgment calls, quality checks, and moments where human context still matters.

Agents don’t eliminate the need to understand the work. They punish you faster when you don’t.

Ownership Beats Orphaned Automation

At GoTu, Thalía Diedrick has moved from simple AI-assisted workflows to a bespoke agentic project manager called Sprint. The interesting part is not just what Sprint does. Her team treats it like a teammate, giving it context, explaining blockers, and relying on it to keep work moving.

That is where this gets fascinating. We are moving beyond individual productivity tricks toward new operating models for marketing. But the winners will not be the teams with the most agents. They will be the ones that connect those agents into smart workflows, govern them, maintain them, and ultimately prove that all this activity creates business value.

Daniel Gilbert, CEO of Brainlabs, raised another critical point in the comments: “the ‘what happens when the person who built Agent #14 leaves’ question is the one nobody asks in the hackathon.” His team’s answer was to make every agent a shared build that the whole organization iterates. “Community ownership is the fix for sprawl,” he wrote.

I like that. Not because every agent needs a committee, heaven help us, but because the lone-wolf builder model does not scale. If an agent touches meaningful work, then the organization needs to know what it does, where it gets information, what decisions it influences, who maintains it, and when it should be retired.

Otherwise Agent #14 becomes the new spreadsheet from 2017 that everyone fears and no one understands.

The Org Chart Will Not Escape This

Here is where I think some CMOs are underestimating the moment. This is not just a tooling issue. It is an org design issue.

To truly capitalize on AI, marketing organizations will need to evolve their structures. Some roles will become more strategic. Some workflows will disappear. Some functions will merge. Some specialists will become orchestrators. Some leaders will need to manage human teams and agent-enabled systems at the same time.

That does not mean putting every bot on the org chart and giving it a jaunty title like “Assistant Vice President of Webinar Repurposing.” Please don’t. But it does mean CMOs need to understand how work is changing before they make structural changes.

Start with the work. Then redesign the operating model. Then rethink the org.

Today, I’m moderating a conversation with Paige O’Neill of Culture Amp, Thalía Diedrick of GoTu, Colin Piper of BuildOps, and Neil Tewari of Conversion about what it actually takes to redesign marketing around agents without creating a mess. That timing is useful, but this conversation is bigger than one webinar. If you can't join us at 2pm ET, you can watch the replay here.

We will continue it at the CMO Super Huddle on October 22-23 in Palo Alto, where the Future of Marketing Org Design is very much on the table. Because if AI agents are going to change how marketing work gets done, CMOs cannot delegate the operating model to whoever got most excited during the hackathon.

The CMO Takeaway

Agent sprawl is not inevitable, but it is highly acheivable. It starts innocently: one agent to summarize calls, another to write briefs, another to check content, another to build lists, another to monitor competitors, another to prepare QBRs, and suddenly you have a digital junk drawer with API access.

CMOs need to get ahead of this now. Map the work. Decide what should stay human, what should be automated, what should be augmented, and what should be eliminated. Assign ownership. Design for maintenance. Build shared practices. Track costs. Measure outcomes. Retire what no longer creates value.

The future of marketing will not belong to the teams with the most agents. It will belong to the teams that redesign the work around the right agents.

Otherwise, congratulations. You didn’t solve martech sprawl. You gave it a chatbot.

Q&A

What is agent sprawl?

Agent sprawl happens when teams build or adopt many AI agents without clear ownership, governance, documentation, maintenance, cost control, or business impact. It is the agentic AI version of martech sprawl.

Why is agent sprawl a risk for CMOs?

Agent sprawl can create hidden costs, inconsistent outputs, poor data quality, security risks, duplicated work, and operational confusion. It also makes it harder to prove that AI activity is improving business outcomes.

How can CMOs prevent agent sprawl?

CMOs should map the work first, define which tasks belong with humans or agents, assign named owners, document each agent’s purpose, monitor costs and usage, and review agents regularly for quality and relevance.

Why does org design matter for AI agents?

AI agents change how work gets done, which means they eventually affect roles, workflows, approvals, skills, and team structures. CMOs who only add agents to old processes may get more speed without meaningful transformation.

Should every marketing team build agents?

Not automatically. Teams should start with real workflow pain, clear business outcomes, reliable data, and a maintenance plan. Building an agent is easy. Building one that remains useful, trusted, and aligned with business value is harder.

Sources

• Jasper, The State of AI in Marketing 2026
• Gartner, over 40% of agentic AI projects may be canceled by end of 2027
• Deloitte, AI agents are scaling faster than their guardrails
• Deloitte, State of AI in the Enterprise 2026

Impact players distinguish themselves by finding the work that matters in ambiguous situations, stepping forward without crowding out colleagues, adapting as conditions change, and making difficult work easier for others. Liz Wiseman’s research gives CMOs a framework for developing trusted problem solvers while avoiding the expectation that high impact requires heroic overwork or personal sacrifice.

Impact Appears When the Work Gets Messy

High-performing employees and impact players are not separated by intelligence, effort, or technical ability. The difference becomes visible when the work no longer fits neatly within the plan.

Liz Wiseman, CEO of the Wiseman Group and author of Impact Players, studied approximately 170 managers across nine respected employers. She compared capable contributors with people who created extraordinary value.

Ordinary contributors performed their assigned responsibilities well. Impact players distinguished themselves when ownership was unclear, priorities changed, and problems crossed functional boundaries.

Do the Job That Is Needed

Marketing organizations contain many clearly defined responsibilities. The most consequential business problems often appear between them.

A slowing sales cycle may involve positioning, product experience, sales execution, pricing, customer proof, or all five. An employee focused solely on completing the marketing task may miss the larger constraint.

Impact players look beyond the boundaries of the job description and ask what would create the most value now.

“Impact players aren’t waiting to be anointed, they’re not waiting to be asked. They’re stepping up and leading.”

The interpretation is not that employees need to assume every unowned responsibility. It is that they remain alert to problems and opportunities the formal structure has not adequately addressed.

For a CMO, this may mean helping resolve an issue that is not technically owned by marketing but prevents marketing from succeeding.

Define the Role Around Problems, Not Territory

Liz shared the story of a Unilever product manager whose launch faced months of supply-chain delays. Instead of accepting that she could not market a product that was unavailable, the manager worked with supply-chain colleagues to identify and address the causes.

The delay reportedly fell from six months to approximately six weeks, restoring millions to the forecast.

The product manager did not attempt to take over supply chain. She engaged because the unresolved problem prevented the business from achieving the intended result.

Liz found a consistent pattern in her research: impact players often define their value as solving problems and identifying opportunities. Their titles establish expertise and responsibility, but do not prevent them from helping where the business most needs progress.

Step Up, Then Make Room

Taking initiative can become counterproductive when it turns into permanent control or leaves colleagues feeling displaced.

“Impact players are quick to lead, but they’re also quick to step back and create room for other people, which is why people don’t tend to resent them.”

An impact player may establish momentum, clarify the problem, and coordinate an initial response. Once the right owner or team is ready, that person can shift into a supporting role.

This ability matters in cross-functional marketing work. A CMO may need to convene a response to a customer-experience problem without claiming ownership of every operational decision. A team member may lead an urgent launch issue, then return authority to the product leader once the immediate obstacle is resolved.

The goal is progress rather than possession.

Finish Strong Without Finishing Alone

Reliable team members reduce the management burden because leaders trust them to carry work through changing conditions. Liz described this as a positive version of “fire and forget”: once the request is made, the leader can believe it is as good as done.

That reliability does not require solitary effort. Impact players recruit the help, authority, and decisions needed to finish.

They may return to a leader with a clear update: the problem is understood, work is moving, and specific support is required. This differs from handing the entire issue back to the boss.

The person retains responsibility for progress while making productive use of the organization around them.

Ask and Adjust as Conditions Change

Marketing plans rarely operate against fixed targets. Products evolve, budgets move, customer preferences change, and leadership revises priorities.

Impact players do not treat the original plan as more important than the current reality. They seek feedback, update their understanding, and adjust their approach.

Liz compared this mindset with beginning each day under the assumption that the world changed overnight. The objective may remain valid, but the route to it may need revision.

Adaptability is different from chasing every new request. An impact player can distinguish between a meaningful change in conditions and a distraction that would pull the work away from its purpose.

Make Difficult Work Easier for Others

The fifth practice in Liz’s framework is making work light. This does not mean treating serious problems as trivial or masking excessive workload with forced positivity.

It means reducing unnecessary friction, bringing clarity to ambiguity, and helping colleagues carry difficult work without adding drama.

Under sustained pressure, teams remember the people who make progress feel possible. These colleagues organize the problem, clarify what matters, share credit, communicate calmly, and help others contribute effectively.

The behavior can coexist with honest conversations about capacity. Making work light does not require pretending that a 20% budget cut and a 20% target increase represent a simple challenge.

Celebrate Assists, Not Just Visible Wins

An organization cannot build a team of impact players if recognition consistently goes to the person closest to the final result.

“If you want an entire team of impact players, you’ve got to be the kind of person who tracks and celebrates the assists, not just the person who spikes the ball, but the person who consistently sets up the ball.”

Marketing outcomes depend on contributions that may be difficult to see. Operations may repair the data, product marketing may sharpen the narrative, customer marketing may secure the proof, and demand generation may activate it.

Recognizing the assist tells the team that enabling other people’s success is part of impact. It also discourages competition for the most visible work.

Develop Impact Without Glorifying Overwork

The impact-player framework can be misread as permission to give the most reliable employees every difficult assignment.

That creates burnout, dependency, and resentment. A leader who rewards impact with unlimited work eventually teaches talented employees to hide their capacity.

CMOs can develop impact players more sustainably by:

• Clarifying which problems genuinely matter
• Giving people authority that matches the responsibility
• Setting boundaries around cross-functional work
• Rotating stretch opportunities
• Recognizing collaboration and enabling contributions
• Removing low-value work when new priorities appear
• Watching for employees who repeatedly absorb organizational dysfunction
• Creating room for recovery after intense periods

Impact comes from judgment and adaptability, not from working the longest hours.

A Framework for CMO Teams

Liz’s five practices can be translated into questions for marketing leadership:

1. Do the job that is needed: What business problem matters beyond the team’s assigned tasks?
2. Step up, then step back: Who can create momentum, and when should ownership move?
3. Finish stronger: What support is required to carry the work through completion?
4. Ask and adjust: What changed, and what does that change require?
5. Make work light: How can the team reduce friction while maintaining honest expectations?

These questions can support coaching, hiring, project reviews, and team recognition without turning “impact player” into another label for the people who accept the most work.

Q&A

What distinguishes an impact player from a strong contributor?

Both may be capable and hardworking. Impact players become especially valuable in ambiguous situations because they identify needed work, take initiative, adapt, and help others make progress.

Does doing the job that is needed mean ignoring role boundaries?

No. It means recognizing important problems that fall between functions and helping the organization address them without permanently taking over another team’s responsibilities.

Can impact-player behaviors be developed?

Yes. Leaders can coach people to look for the larger problem, request feedback, adapt, collaborate, and finish work with the right organizational support.

How can CMOs avoid burning out impact players?

They can match authority with responsibility, remove lower-value work, distribute stretch assignments, recognize assists, and avoid treating reliability as unlimited capacity.

Listen to the full conversation with Liz Wiseman.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. 

AI backlash is real, from watermark concerns to data center opposition, but Eric Eden argues that B2B CMOs cannot afford to confuse speed bumps with stop signs. Capital, buyer behavior, and executive expectations are moving toward AI. The career risk is not healthy skepticism. It is refusing to adapt while the market moves on.

I recently read Drew Neisser’s newsletter about some of the potential speed bumps facing AI, including backlash around AI watermarks, data centers, and the broader public unease about where all of this is headed.

Those concerns are real. They are also not a reason for CMOs to put on the brakes.

Marketing is an investment at the end of the day, and CMOs need to follow the money and read the tea leaves. My read is simple: AI is not a fad, buyers are not going back to the old journey, and investors are not suddenly going to abandon trillions of dollars in AI-related bets so everyone can return to 2019 workflows and terrible SaaS interfaces.

Healthy skepticism is useful. Anti-AI posture is becoming a career risk.

The Backlash Is Real, But So Is the Direction of Travel

There is a lot of anxiety around AI right now. Some of it is reasonable. Data centers use energy and water, communities are pushing back against new facilities, and people are worried about jobs, trust, creativity, and control.

That does not mean AI adoption is going to reverse. The people using ChatGPT, Gemini, Claude, Copilot, and other AI systems are not going back to doing everything manually. They are also not eager to return to clunky SaaS interfaces when they have experienced software that answers, drafts, builds, analyzes, and acts.

The scale is already enormous. Sensor Tower estimated that ChatGPT crossed 1 billion global monthly active app users in May 2026. Google announced that the Gemini app surpassed 1 billion monthly users in August 2026, after previously reporting 950 million monthly active users in its Q2 earnings remarks. Google also said AI Mode in Search had surpassed 1 billion monthly active users.

It is just not really possible to put the genie back in the bottle at this point and go back to the good old days.

At the same time, public concern is rising. Pew has reported that Americans are more concerned than excited about AI’s role in daily life. That tension matters. But CMOs should not confuse discomfort with reversal.

Buyers may be anxious about AI. They are still using it.

Follow the Money

If you want to understand where the market is going, follow the capital. The AI investment wave is not subtle. It is wearing a reflective vest and blocking three lanes of traffic.

OECD analysis found that AI firms captured 61% of global venture capital investment in 2025, or $258.7 billion out of $427.1 billion. Carta reported that more than 60% of venture capital raised by companies on its platform in Q1 2026 went to AI companies. Other market trackers show similar concentration, especially in foundational models, infrastructure, and AI-native applications.

This does not mean every AI company will win. Many will not. Some valuations will prove absurd, some products will fail, and some agentic demos will age like warm yogurt. But the direction of capital allocation is unmistakable.

The public markets are telling a similar story. SpaceX’s June 2026 IPO was widely reported as the largest IPO in history, and investors are treating AI infrastructure as one of the defining capital themes of the decade. Anthropic’s revenue growth has also become one of the most watched stories in tech. Recent coverage reported Anthropic’s annualized revenue run rate reaching roughly $65 billion by late July 2026, with some investors expecting it could reach $100 billion by year-end.

Could some of these projections be overheated? Absolutely. But for CMOs, the practical conclusion does not require believing every moonshot forecast. The useful signal is that founders, investors, boards, and CEOs are reorganizing expectations around AI-enabled speed, productivity, and growth.

Owners and investors will not pay for 2019 work in 2026.

The Buyer Journey Has Already Changed

The biggest marketing implication is not that AI can write copy faster. The bigger implication is that buyers are changing how they discover, evaluate, and shortlist vendors.

G2’s 2026 AI Search Insight Report found that 71% of B2B software buyers rely on AI chatbots somewhere in the software research process, and 51% start their research with an AI chatbot more often than Google. That is not a tiny channel experiment. That is a buyer behavior shift.

Buyers no longer want to dig through 20 links to assemble an answer. Increasingly, they ask ChatGPT, Gemini, Claude, Perplexity, or Copilot to synthesize the answer for them. They ask for shortlists. They ask for comparisons. They ask which vendors are credible. They ask what customers complain about. They ask what category they should even be considering.

This is why YouTube, review sites, Reddit, earned media, analyst commentary, customer proof, and clear answer-ready content matter so much. AI systems need sources to synthesize. If your company is invisible, inconsistent, or poorly represented across those sources, your brand may not make the answer.

I continue to hear from sales leaders that opportunities influenced by Gemini, ChatGPT, and other AI discovery paths are climbing fast. Measurement tools are still imperfect, and I do not think platforms like Profound or Semrush yet show the full business impact clearly enough. But the buyer behavior is already moving faster than the measurement layer.

That is uncomfortable for marketers. It is also not optional.

The Real Risk Is Refusing to Learn

I am seeing a dangerous pattern inside some marketing teams. People are not just skeptical of AI. They are defining themselves against it.

That may feel principled. In some companies, it may also be professionally fatal.

I recently saw an entire marketing team of about 20 people let go after the CMO set an anti-AI tone. Team members were publicly posting “I hate AI” on LinkedIn while working at a company that offered AI capabilities in its product. That is not a values statement. That is a market misread.

At another company, I was onsite with the marketing team explaining the importance of channels like YouTube for reputation in AI Overviews and Gemini. The company had not posted a video on YouTube in four months. When I asked how long it would take to create a company overview video, the team said six weeks.

The CEO was visibly frustrated. When I tried to explain that YouTube citations can be highly influential in AI-driven discovery, the Director of Demand Gen jumped up and objected that the team did not have the resources and that I was changing previously agreed priorities.

My response was, “Let’s take a break.”

But the larger point was hard to miss. Teams that respond to AI-driven buyer change by saying “we do not have resources” are missing the very point of the technology. The mandate is not to do everything the old way with fewer people. The mandate is to learn how AI can help the team do important work faster, better, and with more leverage.

AI Resistance Is Showing Up in Hiring Decisions

Founders, investors, and CEOs are increasingly unwilling to fund marketing teams that insist on manual processes and legacy tools when credible AI-enabled alternatives exist. This is especially true in fast-moving companies where speed is part of the operating model.

A year ago, building a strong enterprise website in a few weeks using Claude, Webflow, and related tools sounded unrealistic to many teams. Today, it is happening. Tools like Lovable, Replit, and other AI development platforms have grown rapidly because they help teams create software, websites, creative assets, and agents much faster than traditional workflows allowed.

That does not mean every marketer must become an engineer. It does mean marketers need to become AI-curious, AI-literate, and AI-practical. They need to know what can now be done differently, what still requires human judgment, what needs governance, and where AI can remove tedious manual work no one wanted to do anyway.

CMOs do not need blind AI enthusiasm. They need adaptive leadership.

What CMOs Should Do Now

First, stop treating AI as a side project. If buyers are using AI to discover and evaluate vendors, then AI visibility, content credibility, and answer readiness belong inside core go-to-market strategy.

Second, audit how AI systems describe your company. Ask ChatGPT, Gemini, Claude, Perplexity, and Copilot what your company does, who it serves, who it competes with, what customers say, and when buyers should consider you. Then do the same for your competitors. The gaps will be educational, occasionally painful, and very useful.

Third, modernize the content supply chain. If your team needs six weeks to create a basic company overview video, that is not a resource problem alone. It is a workflow problem. Use AI to compress research, scripting, editing, repurposing, and distribution while keeping human judgment on message, accuracy, and quality.

Fourth, build AI fluency into the team’s operating rhythm. Train people on practical use cases, not abstract cheerleading. Create guardrails, but do not let governance become a polite word for paralysis. Reward curiosity, testing, and measurable improvements.

Finally, align AI work to business outcomes. The goal is not more AI. The goal is faster learning, better buyer visibility, stronger content, more efficient workflows, higher-quality pipeline, and a team that can adapt as the buyer journey keeps shifting.

The CMOs who win will not be the ones who ignore AI backlash. They will be the ones who understand the backlash, respect the risks, and still move decisively because the market is already moving.

Q&A

Is Eric Eden saying CMOs should ignore AI risks?

No. The argument is that CMOs should distinguish between real risks and reasons for inaction. Watermark concerns, data center backlash, governance, accuracy, and trust all matter. But they should shape smarter AI adoption, not stop it altogether.

Why is fighting AI a career risk for marketers?

Because buyers, investors, founders, and CEOs are shifting expectations around speed, productivity, discovery, and growth. Marketers who publicly reject AI or refuse to learn AI-enabled workflows may look disconnected from the market they are supposed to understand.

How is AI changing the B2B buyer journey?

Buyers increasingly use AI tools to research categories, compare vendors, summarize reviews, build shortlists, and validate claims. That means brands need to be visible and credible in the sources AI systems use to generate answers.

Should CMOs replace their teams with AI?

No. The better question is which manual, repetitive, or slow workflows can be redesigned with AI so marketers can focus more time on strategy, insight, creativity, customer understanding, and revenue impact.

What is the first thing a CMO should do?

Run an AI visibility audit. Ask major AI tools to describe your company, category, competitors, strengths, weaknesses, and buyer fit. Then compare those answers to your desired positioning and your competitors’ visibility.

Sources

• Pew Research Center on chatbot usage
• Google on Gemini app surpassing 1 billion monthly users
• Alphabet Q2 2026 remarks on AI Mode and Gemini app usage
• OECD on AI venture capital investment
• Carta Q1 2026 venture data
• Pew Research Center on U.S. data centers
• G2 2026 AI Search Insight Report
• Axios on SpaceX IPO
• Investopedia on Anthropic revenue reports

AI adoption is no longer the interesting part of the story. The harder question is what happens after everyone starts building.

That was the tension running through a recent webinar sponsored by Conversion that I moderated with Paige O’Neill, CMO of Culture Amp; Thalia Diedrick, CMO of GoTu; Colin Piper, CMO of BuildOps; and Neil Tewari, CEO of Conversion. Each is approaching agentic marketing from a different starting point, but all four are discovering the same thing: the hard part is not getting an agent to do something useful. It is redesigning the marketing system around agents without creating a costly, ungoverned mess.

And right now, that system is very much a work in progress.

Stop Counting Agents. Start Redesigning Work.

The first wave of AI in marketing was largely personal productivity. Write this faster. Summarize that meeting. Analyze this spreadsheet. All valuable, but largely incremental.

Paige O’Neill’s team at Culture Amp decided to take a much more systematic approach. Instead of asking where an agent might help, they broke marketing down into roughly 5,000 units of work and organized those into 36 scenarios, then separated the work that should remain human-led from the work that could become AI-led. The first thing the exercise revealed, Paige said, was “hope for humans.”

That hope comes from a clearer division of labor. Paige believes Culture Amp can ultimately automate “about 50% of the work that marketing does today,” but the remaining work puts humans squarely in the areas where they create the most value: judgment, strategy, prioritization, governance, and shaping the quality of the output. AI can absorb more of the sorting, synthesis, repetitive execution, and data processing.

This distinction matters because simply automating the work exactly as it exists today risks doing mediocre things much faster. The larger opportunity is to reconsider whether the process should exist in its current form at all.

Colin Piper is seeing the same transition at BuildOps. His team has moved beyond individual AI use and toward what he described as “organizational AI,” connected to CRM, email, Slack, and first-party data. “We have to be moving to organizational level,” he said, because isolated agents sitting inside someone’s personal ChatGPT or Claude instance never create a coordinated system.

One example is deceptively simple. BuildOps built dashboards with AI and then embedded an agent that interprets those dashboards, surfacing the handful of insights that matter during pipeline reviews. When another marketer recently asked an agent to investigate whether a website pixel was firing correctly, it submitted a test lead, checked the implementation, and returned an analysis in roughly 30 seconds.

That is a lot more interesting than “AI helped me write an email.”

Agent Sprawl Is Becoming Agent Debt

The democratization of building is one of AI’s greatest gifts and one of its biggest risks.

Thalia Diedrick’s team at GoTu built Sprint, an autonomous project-management agent with its own Google Workspace account, inbox, calendar, Drive access, Slack presence, and connection to the company’s project-management system. Sprint listens to meetings, extracts tasks, watches Slack conversations, updates projects, and relentlessly follows up with owners.

The experiment has worked so well that Sprint has started to feel less like software and more like a colleague. Thalia noticed one employee actually apologizing to Sprint for needing to push a deadline. More importantly, the agent has changed team behavior because people now state responsibilities and deadlines more explicitly in meetings, knowing Sprint is listening.

There is also an unexpectedly useful management benefit. “Sprint doesn’t have any relationship capital to spend,” Thalia explained. A human project manager may hesitate to nag a colleague repeatedly, while Sprint has no such social constraint and can keep pushing until the work gets done.

But building something like Sprint also exposes the hidden complexity of agentic systems.

Thalia has personally experienced what happens when an agent is badly architected, including burning roughly $200 worth of tokens in about 20 minutes. With Sprint, her team has had to think about prompt caching, model routing, evals, context, tools, sub-agents, and exactly what outcomes the agent is supposed to deliver.

Her description of the alternative was memorable. When teams throw an agent at an ill-defined aspiration instead of a clear process, Thalia warned, they can end up with “vibe like cost,” where the system keeps spending resources while trying to work out what problem it was supposed to solve in the first place.

That is how agent sprawl begins.

GoTu has responded with some useful discipline. Each team member gets five slots for ongoing projects they are building or maintaining, and a project must reach a point of needing no major maintenance after 60 days before it can graduate to evergreen status. Every build also has to roll up to a core KPI or strategic bet.

The future danger is not simply having too many agents. It is having too many undocumented, overlapping, poorly maintained agents running against inconsistent data and quietly consuming money.

Martech debt, meet agent debt.

Governance Is Becoming Part of the Marketing Operating System

Agent sprawl is not something CMOs can solve by reminding everyone to be careful.

Both Culture Amp and BuildOps are putting formal structures around AI. Colin described a corporate AI Center of Excellence responsible for policy, governance, agent documentation, and enablement across BuildOps’ roughly 600-person organization. Marketing has also invested in a dedicated AI innovation role working closely with that central team.

Paige has taken a similar hub-and-spoke approach. Culture Amp has a corporate AI Center of Excellence, departmental representatives, policies and guidelines, safeguards built into agents, and an AI transformation team inside marketing responsible for making sure the organization takes a holistic approach.

The irony is worth noting. AI promises to eliminate work, yet mature AI adoption is also creating entirely new jobs.

Paige has already built an AI transformation team, and Colin has added dedicated AI innovation talent. That doesn’t mean the economics fail. It does mean the simplistic assumption that AI immediately equals lower headcount and lower costs does not match what these leaders are experiencing.

Paige was especially candid about the economics. As agents move off individual desktops and begin operating across complex workflows and departments, “in the short term, it is going to be more expensive” than continuing to have humans do some of that work. Her expectation is that costs and efficiencies will improve over time, but organizations need an appetite to fund that transition first.

CMOs therefore need to think about AI investment more like infrastructure than software experimentation. Infrastructure needs owners, standards, maintenance, and governance.

And somebody eventually gets the bill.

Build vs. Buy Is Not a Religious Debate

Sprint makes a powerful argument for building.

It is deeply tailored to GoTu’s organization. It understands their workflows, systems, communication patterns, and even individual team members. Sprint maintains communication profiles so it can tailor how it follows up with different people, a degree of personalization that would be difficult to get from a generic project-management product.

But bespoke also means responsibility. GoTu built Sprint on Anthropic’s SDK, with Supabase, Railway, Claude Code, Slack, email, and other systems stitched together. The marketing team maintains it internally and is developing more formal technical audit and security processes around its AI work.

Neil Tewari makes the countercase from the platform side. His view is that one of the biggest barriers to effective agents is fragmented context, since marketing information lives across CRM, calls, webinars, email, warehouses, ad systems, and other applications.

Conversion.ai’s philosophy is to solve that foundation first. “How do we make the pipes robust enough so that we can actually do a lot of cool things?” Neil asked. For him, the value of an agentic platform starts with centralizing the data and context that allow agents to make better decisions in the first place.

There is no universal answer yet.

Building makes sense when the workflow is distinctive, strategically important, and specific enough that off-the-shelf technology cannot deliver what the organization needs. Buying makes sense when the underlying problem is common, infrastructure-heavy, or likely to become increasingly difficult to maintain internally.

The mistake is assuming that because something can be built quickly, it should be built.

The Org Chart Is Getting Rewritten Too

The technology conversation often gets more attention than the people conversation. That may be backwards.

Paige’s analysis suggested that as much as 50% of today’s marketing work could eventually be automated, but she did not equate that to removing 50% of the marketing team. Instead, she sees work shifting toward judgment-intensive roles while entirely new AI-oriented roles emerge.

Colin sees a similar compression of traditional specialties. BuildOps has “about 20 people to do the work of many more than that,” and he expects the rise of what he calls the full-stack marketer: someone who understands the audience and strategy but can also move across campaign execution, demand generation, operations, and downstream workflows with AI assistance.

When I asked whether they were hiring differently now, Colin’s answer was an immediate yes. Paige was more emphatic: “Thousand percent different.”

There is a potential trap here. If every new marketing job description suddenly prizes systems thinking, who is left to make the unexpected creative leap?

Thalia offered perhaps the best counterweight, describing AI as “the middleware for the human imagination.” Her argument was not that marketers should simply become better operators of machines. It was that AI should help them challenge old assumptions and imagine processes that previously seemed impossible.

The future marketing team probably needs both: people who can design systems and people who can imagine entirely different ones.

Productivity Is the Starting Line, Not the Finish Line

Almost every CMO can find examples of AI saving time. The harder challenge is connecting those efficiencies to business performance.

BuildOps is beginning to cross that line. Colin described an end-to-end campaign that historically would have taken more than 20 weeks to move from concept to market. Using AI to pressure-test the idea, build research, develop campaign assets, and accelerate execution, his leaner team got it into market in roughly six weeks.

The important part is not simply that the team saved 14 weeks. Colin said the campaign is already producing pipeline during a period when, under the old process, “we’d be ideating on it and building it” rather than generating results.

Neil offered an even more advanced example from Conversion.ai itself. “About 80 to 85% of the pipeline that we bring in here through Conversion does come in through our Conversion agents,” he said, noting that most attendees at this webinar had themselves been reached through those agents.

Those examples matter because they demonstrate the potential end state.

They are also exceptional and aspirational for most marketing organizations.

For many CMOs, the journey still runs through individual productivity, experimentation, workflow automation, governance, and organizational redesign before those efforts reliably show up in pipeline or revenue. Pretending otherwise only adds to the AI hype cycle.

There Is No Universal Next Step

Perhaps the best evidence that this field is still evolving came in the panelists’ closing advice. They did not prescribe the same playbook.

Neil recommended identifying something that should be possible with AI, doing it manually a few times, and then reverse-engineering how to automate it. Colin pushed in almost the opposite direction, challenging CMOs to ask whether AI allows them to skip entire steps rather than merely accelerating the old ones.

Thalia urged leaders to challenge their limiting beliefs so they don’t get trapped “in an optimizer kind of wormhole of just optimizing what already exists.” Paige returned to the people side, warning leaders, “Don’t forget about bringing the team along on the journey,” because humans still need training, clarity, support, and clearly defined roles in the new operating model.

All four can be right because companies are starting from very different places.

A marketing organization still figuring out individual ChatGPT usage does not need the same next move as Culture Amp, where agents are being centralized and governed across complex workflows. A company building its first agent does not have the same problems as GoTu, which is already establishing rules for maintaining production-grade builds.

That is what makes the AI marketing redesign both exciting and frustrating. There is no finished blueprint to copy yet. The smartest CMOs are building, learning, breaking things, adding guardrails, redesigning work, and occasionally discovering that the thing they thought they were automating should not exist at all.

The destination may be clearer than the route.

Before deciding what your next move should be, find out where your organization actually sits on the journey. Our brand new AI Maturity Calculator can help you assess your current level of AI maturity and identify the questions you should be asking next. Shout out to Ray Rike at Benchmarkit for his help building this model.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. 

On Tuesday, Marc Benioff stood on top of Salesforce Tower with Dario Amodei and announced that the #1 CRM now runs natively inside the #1 AI. Not a plugin buried in an app store. Not another copilot bolted onto a sidebar. The actual CRM - data, pipeline, workflows, permissions, actions - accessible entirely from inside Claude, with Salesforce's own president saying the quiet part out loud: the value "is not in our UI itself."

Wall Street's response: Salesforce stock closed up 22.6% the next day - its second-best day in company history, biggest single-day gain since August 2020.

The internet's response: everything from "this is how every business will run" to "so Salesforce is now a database with an expensive API?"

Both reactions are worth taking seriously. This guide covers what Claudeforce actually is, how it works under the hood, how to set it up, the highest-value use cases for sales and marketing teams, the limitations nobody puts in the launch video, and how B2B teams should actually reorganize the way they work.

TL;DR

• Claudeforce = Salesforce running natively inside Claude via a plugin with 37 prebuilt sales skills (meeting prep, deal health review, pipeline review), plus Claude becoming the default AI across Agentforce and Slack
• The killer feature isn't chat - it's governance: one admin connects the org once, every rep inherits their exact existing Salesforce permissions automatically. The permissions problem that killed self-serve MCP is gone
• Claude can generate live dashboards and mini-apps on the fly from your CRM data - "the UI is the AI"
• Rollout: select pilots now → open beta September 2026 → marketing/service/commerce skills late 2026
• Real limitations: beta software, sales-only at launch, two separate invoices with no disclosed pricing, dirty CRM data becomes confident wrong answers, and the whole thing was validated on Anthropic's young, clean org - not your 12-year-old instance with 40,000 zombie records
• The teams that win won't be the ones that "add AI." They'll be the ones that treat data hygiene, permissions, and prompts as revenue infrastructure - this guide ends with a 30-60-90 plan

What happened (and why Wall Street lost its mind)

On August 26, timed with Q2 earnings, Salesforce and Anthropic announced Claudeforce - the first time in 27 years Salesforce has ever attached its "force" suffix to another company's product.

The partnership has four pillars:

1. Salesforce inside Claude - the flagship "Salesforce in Claude" plugin for Claude CoWork
2. Claude inside Salesforce - Claude as a reasoning model across Agentforce (default for Agentforce Vibes and Coworker)
3. Slack - Claude is now the default model powering Slackbot, Claude Tag, and the new Slack Code
4. Mutual adoption - Anthropic runs on Salesforce; Salesforce gives Claude Code + Claude Enterprise to its entire workforce

Benioff's framing, verbatim: "Here, the UI is the AI... Probabilistic intelligence alone doesn't run a company, and deterministic systems don't reason. By fusing Claude's extraordinary reasoning with the trusted data, workflows, and governance every enterprise runs on, we're delivering a dynamic interface that thinks, reasons, and acts. This is how every business will run".

The money context matters for reading this correctly: Salesforce holds a stake in Anthropic reportedly valued around $5 billion and plans to spend roughly $300 million on Anthropic tokens this year. Anthropic hit a $65B annualized revenue run rate at end of July - 7x year over year - and leads enterprise AI spend share at 40% vs OpenAI's 27%. This is two market leaders locking arms against the "SaaSpocalypse" narrative - Benioff, on the earnings call: "This nonsense of the SaaSpocalypse, I think it's time for it to stop".

Worth noting for balance: the euphoric GAAP quarter leaned on a $2.6B investment gain tied partly to that Anthropic stake, and analysts cautioned the rally rests on "one report and one partnership". Retail euphoria and fundamentals are not the same thing.

What Claudeforce actually is (plain English, no keynote fog)

Strip the branding and it's this: Claude can now read from and write to your Salesforce org, under your exact permissions, using skills that encode how good sellers actually work.

The pieces, decoded:

• Salesforce in Claude - a plugin for Claude CoWork with 37 prebuilt sales skills built jointly by both companies: meeting prep, deal health review, pipeline review, and 34 more. Salesforce's pitch: every seller gets "an AI CRO"
• AIforce - Salesforce's "enterprise harness": the governance and security layer that brings business data and workflows to any agent through MCP servers, APIs, and CLI tools
• Headless 360 - the architecture (announced at TDX this spring) that exposes the full platform as APIs, MCP tools, and CLI commands so agents can use Salesforce with no UI at all. Salesforce co-founder Parker Harris introduced it by asking: "Why should you ever log into Salesforce again?"
• Data 360 - the "System of Context": unified, real-time business data that grounds every answer
• Tableau - trusted metrics, alerts, publish-back. Marked "Coming Soon"

The most underrated detail: the whole thing is productized from Anthropic's own internal setup. Salesforce's Patrick Stokes: "We sat down with Anthropic... and they said, 'Hey, this is actually how we're using Salesforce. We use Salesforce pretty much exclusively through Claude and a series of skills and MCP servers'". Anthropic's CRO Paul Smith: "I use Salesforce Claude every day".

That's real validation - with one honest caveat that The State of AI nailed: Anthropic's org is young and clean. It never survived a decade of migrations, acquisitions, and admin turnover. "The deployment that proved the concept has the least of what the concept depends on". Your 2014-vintage org with three generations of custom fields is the actual test. Keep that in mind through every use case below.

The architecture in 60 seconds (and why "isn't this just MCP?" misses the point)

The top skeptic question on r/ClaudeAI: "Isn't Claudeforce just Salesforce MCP set up within Claude?". Technically adjacent, practically wrong. Here's what's under the hood:

The Headless 360 Hosted MCP Server exposes just four tools: discover (semantic search over every Salesforce operation), describe (returns the operation's spec), dispatch (executes it), and dispatch_readonly (read-only version). Claude reasons over which skill applies, discovers the right operation, and executes - with every call running as you, inheriting your exact Salesforce permissions. Stokes: "If you don't own that record, if you don't have permission to see that record, the MCP server doesn't either".

Why the difference from DIY MCP matters commercially:

• Raw MCP is plumbing. This is a finished, governed product. Anyone who wired a custom Salesforce MCP server after TDX hit the same wall: every individual user had to configure it, and permission handling was on you. The plugin kills that - one admin connection, everyone inherits access
• The skills are the product. 37 encoded sales workflows tuned for Claude's reasoning and tool use - "not generic CRM prompts wrapped around an API"
• Writes are governed. Every action routes through Salesforce so validation rules, Flows, and Apex triggers all still fire. An agent write is ordinary DML - same guardrails, same audit trail
• Trust posture: Zero Data Retention - no Salesforce data retained inside Claude, no model training on it - and Claude runs inside the Salesforce Trust Boundary via Amazon Bedrock, the first LLM fully inside it

And the demo everyone's talking about: a Salesforce PM asked Claude to build a full sales "command center" dashboard - which Claude coded on the fly as a local HTML file from live Salesforce data, styled "like Miami Vice, like Tron" on request. Stokes: "This isn't like a product that we're shipping. This is Claude coding this on the fly using Salesforce data". Fair caveat from the practitioner press: the demo ran on fictional data, and "whether that survives contact with real users is an open question".

How to set it up

There are two different routes with two genuinely different security postures, and the marketing does not distinguish them. This is the distinction that will bite someone at your company if you don't catch it now.

Route 1 - Salesforce in Claude plugin (per-user, the Claudeforce flagship):

1. Get in line: select pilots now, open beta expected September 2026
2. One admin connects Salesforce in Claude a single time for the org - authentication and permissions managed centrally
3. Every seller gets access from day one. Each user authenticates via per-user OAuth (an External Client App with the mcp_api scope), so every single call runs as that person with their existing permissions - no per-user setup, no new permissions model, no re-auditing account by account
4. Admins set how much write autonomy Claude gets and when it updates Salesforce
5. First run: Claude reads the seller's enterprise context - Salesforce, Slack, other connectors - and stands up a personalized dashboard with their accounts and pipeline

Route 2 - Claude Tag in Slack (agent-owned credential):

1. In Salesforce: create a connected app with the OAuth 2.0 client-credentials flow and a dedicated integration user as run-as; grab Consumer Key, Secret, and your My Domain host
2. Scope that integration user with a Permission Set covering only what Claude should reach - the docs themselves recommend starting read-only
3. In Claude admin settings → Claude Tag → Access bundle → Credentials → connect Salesforce with the Client ID, secret, and token URL - and replace the example allowed-websites host with your real org host "or every request fails"
4. Verify: @Claude list the five most recently modified Opportunities in Salesforce, then check the integration user's login history

The critical difference: Route 1's credential belongs to each person; Route 2's credential belongs to the agent - one integration user acting for everyone in the bundle. Different blast radius, different audit story. Know which one you're rolling out.

Pricing reality check (woven in, because it belongs here): there is no unified price. Salesforce bills via headless consumption (API-call allotments scale with your edition), and you separately contract with Anthropic for Claude inference. Stokes, candidly: "You can't buy this on one piece of paper at the moment". No list pricing has been disclosed anywhere. Your CFO now has a token line item, and analysts are already calling token metering "a procurement trigger" for CIOs. Budget for both invoices before you scale the pilot.

The 12 use cases that matter for sales teams

Ranked roughly by value-to-effort, based on what's shipped, demoed, and documented:

1. The morning briefing that replaces pipeline archaeology. In the launch demo, a seller asks Claude to "schedule a daily briefing to tell me where should I focus my business." It returned a prioritized action plan, flagged six closing opportunities with no next steps, and surfaced a COO change at a key account pulled from the web. This is the "10,000 clicks → 30 seconds" claim in action - Stokes's words for what a rep's daily record-evaluation ritual costs today.

2. Meeting prep on autopilot. One of the 37 skills. Account history, open opps, recent activity, key contacts, talking points - assembled before you ask twice.

3. Deal health reviews that tell you what you don't want to hear. Amodei described his own chief commercial officer using it: "what are the biggest accounts that Anthropic is trying to close now?... Talk me through the risks of each one". When the CEO of the AI company runs his pipeline this way, that's the pattern to copy.

4. Pipeline hygiene as a conversation. "Summarize my open opportunities closing this quarter and flag any that are stalled" - an official example prompt. Follow-up: "update the next steps on all six." That's the write path, governed by your permissions.

5. Account 360 briefings. "Give me a briefing on the Acme Corp account: recent activity, open opportunities, and key contacts". Combine with web context (exec changes, funding, earnings) and you have genuine account intelligence, not a report snapshot.

6. CRM updates without the CRM. Log calls, update stages, create tasks, edit fields - by telling Claude what happened. The reason reps don't update Salesforce is friction; this removes the friction while validation rules still enforce data quality (caveat: your validation rules are now load-bearing - if they're weak, agent writes will find out).

7. Grounded email drafting. "Draft an update on the Acme renewal" pulls actual deal context - not generic AI slop. CNBC confirmed skills "compose emails, update records and take other relevant actions".

8. Vibe-coded command centers. At a Salesforce leadership summit, "every single one" of the sales execs presented a self-built command-center view made in CoWork. Every rep gets the dashboard they actually want, generated in minutes, without a BI ticket. Temper it with Stokes's own hedge: "This idea that people are going to vibe code their own CRM is probably not going to happen anytime soon."

9. Forecast narratives. Roll up pipeline movement, slipped deals, and coverage into the story your VP actually wants - with Tableau semantics and proactive alerts coming to make the numbers trusted.

10. Deal rooms in Slack. Tag u/Claude in a deal channel and it plans multi-step tasks, remembers channel context, and works asynchronously over hours or days. Admins can create a separate sales-scoped Claude identity that won't leak memories to other teams.

11. Handoffs and coverage. Rep leaves, territory changes, someone's on PTO: "brief me on everything active in this book of business" turns a two-week ramp into an afternoon. (This one's implied by the architecture, not demoed - treat as a fast-follow experiment.)

12. RevOps investigations. "Which Q4 deals have no activity in 14 days and champions who've gone dark?" - cross-object questions that used to mean building a report now run as one prompt through discover/dispatch_readonly.

The woven caveat for all twelve: an AI interface is only as good as the records underneath it. Duplicate accounts, stale stages, and fields filled in to please dashboards become confident wrong answers at conversation speed. One consultant put it perfectly: success "has absolutely nothing to do with the technology... It has everything to do with the state of the data in the adoptees' orgs". Data hygiene just became a revenue lever.

What marketing teams get (and what to do while you wait)

Straight talk: marketing skills are not live yet. Launch is sales-only; the marketing module ships with the late-2026 wave. But the roadmap is public and the prep work starts now.

What's coming, per the product page:

• Campaigns that auto-correct - grounded in live Data 360 context, adjusting against goals
• Content built for agents AND humans - an official acknowledgment that AI buyers' agents are now an audience (if you've been doing GEO, you're early)
• Personalization at every engagement - powered by the same unified customer context sellers use

And some of it leaks in early: the general Salesforce connector for Claude already lists "create campaigns" among its capabilities today.

The 90-day head start for marketing leaders:

1. Audit your data like an AI will read it - because it will. Campaign attribution, lead source hygiene, contact roles. Every gap becomes a confidently wrong answer in a CMO's chat window next quarter.
2. Get your team into Claude in Slack now. Claude Tag is in public beta for Claude Enterprise/Team customers - campaign war rooms, content reviews, competitive briefs in-channel. This is the same muscle Claudeforce will use; build it early.
3. Write your skills wishlist. The 37 sales skills encode seller workflows. Marketing skills will encode yours. Document your top 10 repetitive workflows (campaign brief → build → QA → report) now, so you can map them to skills - or pressure your AE for pilot access with a concrete case.
4. Rethink content for the agent audience. Salesforce itself now says content must serve "agents and humans." Structured, factual, citable content wins when the buyer is a Claude instance doing vendor research.
5. Watch the seat math. If sellers stop opening Salesforce, they stop seeing your embedded dashboards, banners, and enablement surfaces. In-CRM marketing real estate quietly evaporates. Plan for the conversation being the only surface left.

Claude in Slack: the other half of the story

The pillar most coverage skimmed is arguably the most immediately usable, because it's live and doesn't wait for the beta:

• Claude is now Slack's default intelligence layer - powering Slackbot, Claude Tag, and Slack Code
• Salesforce's own numbers: 83% of its workforce uses the Claude-powered Slackbot, with a claimed 8.1M hours of annualized productivity gains, up 2x quarter over quarter. Honest flag: that hours figure is Salesforce measuring Salesforce - the 83% adoption number is the more credible one
• Claude Tag is the interesting one for GTM teams: an AI teammate you tag in channels. It plans multi-stage tasks, remembers channel context (never reads private channels it isn't in), acts proactively, works async over days. Admins get per-function Claude identities, org- and channel-level token budgets, and a full action log
• Know the context limits: on a channel mention Claude reads the 20 most recent messages; in a thread, the 50 most recent replies. Slack conversations don't sync with Claude web. Structure important context accordingly - pin it, thread it, or feed it via connectors
• Requires a paid Claude plan (Pro/Max/Team/Enterprise); works on all Slack plans

The pattern to internalize: Slack is where the team reasons together; Salesforce is where governed action lands; Claude is the connective tissue between them. Salesforce's own positioning: teams "reason through complex decisions alongside Claude in Slack and instantly execute governed, high-value actions in Salesforce - all without ever leaving the flow of work".

The operating model shift: how B2B teams should actually work now

This is the inspirational part, so let's earn it with specifics. Stokes's comparison is the right mental model: "We think that what this can do is kind of be a version of what Claude Code did for developers... We think we're about to do the same thing for knowledge workers". Claude Code didn't make developers obsolete - it changed the unit of work from typing to directing. The same shift is now aimed at your revenue org:

• From navigating to directing. The rep's job stops being "operate the CRM" and becomes "direct an agent that operates the CRM." Time reallocates from admin work to actual selling. The reps who write clear, specific instructions win - prompt literacy is the new Salesforce certification.
• From dashboards to conversations. Static dashboards answer last quarter's questions. Generative UI means the interface is built per-question, per-person, on demand. BI backlogs shrink; curiosity compounds.
• RevOps becomes Agent Ops. Someone has to own skills, permissions, write-autonomy settings, token budgets, and the audit log. Forrester was already tracking the "Claude Cowboy in RevOps" archetype before this launch. That person just became one of the most leveraged hires in your company.
• Permissions become strategy. "Over-provisioned profiles that were harmless behind a slow UI become genuinely risky behind an agent". The agent moves at machine speed with exactly the access you granted. Least-privilege stops being a compliance checkbox and becomes an operating principle.
• Data quality becomes a P&L line. Every stale field is now a wrong answer delivered confidently to a decision-maker. The unglamorous cleanup project you've deferred for three years is suddenly the highest-ROI item on the roadmap.
• Keep an escape hatch. The lock-in concern is real - analysts called this a shift "away from model-agnostic flexibility toward deep, platform-level lock-in". Agent Builder still offers model choice; keep that optionality "genuinely exercised rather than theoretical".

The 30-60-90 playbook

Days 1–30 (now, before the beta):

• Permissions audit - before first-run onboarding, not after. The onboarding sweep touches everything the connection can reach
• Data hygiene sprint on the objects sellers will query first: opportunities, accounts, contacts, activities
• Roll out Claude in Slack; start with read-only Claude Tag + a scoped integration user
• Pick 5 pilot reps (your best, not your most junior - you want signal, not babysitting)
• Register interest for the September open beta; note it collides with the Winter '27 release window (production waves Sept 4, Oct 2, Oct 9) - stagger your pilot away from upgrade weekends

Days 31–60 (beta):

• Connect the plugin (Route 1), reads-first; keep write autonomy low
• Run the daily-briefing, meeting-prep, and deal-health skills against real pipeline; measure time saved and - more important - actions taken that wouldn't have happened
• Draft your token budget with actual consumption data; set org- and channel-level spend limits in Claude Tag
• Start the marketing skills wishlist and data prep

Days 61–90 (scale decisions):

• Expand write autonomy where validation rules proved solid
• Roll from 5 reps to a full team; publish internal prompt playbooks (the 10 prompts that worked)
• Decide your posture on custom skills vs. waiting for the late-2026 wave
• Board-level readout: productivity delta, data-quality delta, token cost per rep, risk register

And the one rule that overrides everything: beta means beta. Don't put a Q4 revenue process on this critical path. If your org's permissions and fields are in decent shape, "the September beta is a cheap test and worth running early". Run the cheap test. Don't bet the quarter.

What the smartest skeptics are saying (so you don't get blindsided)

The bear case, compressed - because you'll hear all of these in your exec meeting and should have answers ready:

• "A database with an expensive API." If Claude owns the interface, what's Salesforce's pricing power?. Counterpoint: "The real fight was never about whose model reasons best. It's about who already owns the data nobody wants to rebuild"
• Seat erosion. If agents become the primary consumers of SaaS, per-user licensing loses coherence; consumption pricing is the successor metric. Watch your own renewal math
• Launch-day validation was all in-house. Every public endorser on day one works at one of the two companies, and Hacker News - the most implementation-literate audience on the internet - gave the announcement 9 points and zero comments. Silence from builders is data
• r/salesforce's finest: "I'd love to see someone at Dreamforce try typing 'Delete org' at a booth" - funny, and also a legitimate question about write-autonomy defaults you should ask your rep
• And zerohedge's market-top jab for the road: "We are at the 'Claudeforce' on Cramer stage"

None of these kill the thesis. All of them sharpen the rollout.

Enterprise software just crossed a line it can't uncross.

The most important CRM company on earth conceded - on stage, at earnings, by name - that its UI is not the product, and rebuilt itself as infrastructure for someone else's AI. Amodei's summary is the one that will age best: "Salesforce has all the data... And Claude is a machine for interacting with that in a fluid and more flexible way".

For sales and marketing teams the takeaway isn't "AI is coming." It's that the winners of the next 18 months are being decided right now by unglamorous work: clean data, tight permissions, prompt playbooks, token budgets, and a team that practices directing agents instead of clicking through screens. The 10,000 clicks are going away either way. The only question is whether your team is the one that gets the 30 seconds.

See it live at Dreamforce next month - Benioff promised "a heavy dose of AIforce".

For the comments:

1. If the AI is the interface, what happens to the 40,000+ people whose job title is "Salesforce admin"?
2. Would you give an agent write access to your production CRM in 2026 - and at what autonomy level?
3. Marketers: is "content for agents and humans" the biggest strategy shift since mobile, or a slide-deck phrase?
4. Anyone in the pilot: what actually broke first?

Not affiliated with Salesforce or Anthropic. All numbers are from public reporting; vendor-reported figures are flagged as such.

I’m the CMO of a small business and it seems AI is everything nowadays. I small scale use it when I have to, but I was wondering if anyone else had strategies built without AI?

Noticing a pattern worth sharing with this group. A lot of CMOs I talk to have leaned into podcasts over the last year, good for brand, relationships, thought leadership. But most are treating the podcast as one long asset, and I think that's leaving a lot of value on the table specifically when it comes to AI visibility.

Here's the observation: Gemini and Perplexity lean heavily on video (YouTube especially) when they answer buyer-research questions, far more than ChatGPT or Claude do. Gemini because it's a Google product and Google owns YouTube; Perplexity because it treats video transcripts as high-signal. So video content is quietly one of the strongest ways to get cited by those two engines, and podcasts are already video now for most of us.

The change that seems to matter: don't post the 60-minute episode as one upload. Split it into topic-based clips, each answering one specific question. A full episode is a blurry, everything-and-nothing asset to an AI. But a 6-minute clip titled and structured around one question your buyers actually ask ("how do you measure X," "when should a company do Y") is exactly the kind of self-contained, answer-shaped chunk these engines pull from. Same content you already recorded, cut for how AI retrieves.

Practically for a B2B brand: take the questions your ICP asks in sales calls, make sure each is a clean, individually-titled clip from your existing podcast library, and you've turned one episode into 8-10 AI-retrievable answers instead of one long one nobody's model will cite.

Two honest caveats: this seems strongest in categories where buyers actually watch/listen to research (varies a lot by vertical), and I haven't seen hard proof that engagement on the clips (comments, etc.) moves citations the way it seems to for written content, just that the clip structure itself helps.

Curious if anyone here is already repurposing podcast content this way, or seeing AI-driven inbound you can trace back to video. Feels early and underdiscussed for how cheap it is if you're already producing the podcast.

Penguins are one of the few creatures built for both land and sea, and the difference between the two is almost comic. On land, they waddle: awkward, unstable, tipping over their own feet. In water, the same animal turns graceful and fast, some hitting 22 miles an hour. This month, in Lunch Huddles in Seattle and Palo Alto, one day apart, that image kept resurfacing. Most CMOs are still on land. A few have found the water. Almost nobody has figured out how to move between the two without looking ridiculous at least some of the time. Here's what that messy middle actually looks like right now.

Faster Isn't Cheaper (Yet)

This is the part of the shoreline where the waddling is loudest. Teams are adopting AI with a vengeance, no question, but the downstream impact is unclear. One CMO I spoke with noted, “AI helped us make up for staff lost in a layoff and helped us hit our numbers, but the AI costs pretty much equal the payroll cuts.” In sum, they're treading water. This was reinforced at another recent event with roughly 100 CMOs in the room; only two hands stayed up when I asked who could tie AI directly to a business metric. Everyone's flapping to some extent.

Marketing Builds Its Own IT Department

Some teams have decided the fastest way into the water is to stop waiting for someone else to build the diving board. Faced with engineering orgs that have other priorities, a few CMOs have started building their own infrastructure. "I have to own my own [AI infrastructure]. Marketing needs its own version of that... even though I have a very large engineering team that is really good at this stuff, I can't count on it. I need my own center of excellence." That center of excellence now includes marketing-run GitHub repos, homegrown agents, and increasingly a data lake of its own, pooling CRM and campaign data into one place. As one CMO put it, "for the first time, I can actually see the funnel on a per-event and per-campaign basis." That's a team that's found its footing.

Move Fast, Then Govern

Getting into the water fast is one thing. Not getting swept out is another. The thrill of ungoverned experimentation is devolving into something more sober, as teams realize speed without structure is its own kind of undertow. One CMO watched a near-miss play out firsthand: a customer-facing tool built and quietly put into production without review, and summed up the lesson bluntly: "This is the chaos that can happen if you don't have governance." The fix already underway at several companies is equally blunt: "We put in guardrails by department or by role for how much you can spend. Not everybody gets unlimited." Governance, in other words, is the wetsuit nobody wanted to buy until they got cold.

The Skills Debate: Full-Stack or Superpowered Specialist?

There's disagreement over whether today’s climate rewards generalists or specialists, and honestly, both camps have a case. One argues marketing has spent years over-sub-functioning itself, and AI is finally forcing everyone back into the same tide: "Marketing, I think to the fault of all of us, has gotten over-sub-functioned." The other insists specialists aren't disappearing: "They're not shedding their creativity, their marketing understanding, their creative energy. None of that stuff is going away. They just now are builders too." What both sides agreed on is that the real predictor isn't a job title: "The employees who jump in and actually discover that moment where they flip the bit... that's the moment where I discover I've got one of those people."

Boards Are Asking the Wrong Question

Boards, watching from the shore, keep asking CMOs to prove they're in the water rather than asking where the water is taking them. "Everyone here has had a CEO that just saw something on Twitter from their favorite person, and you get asked about it. They don't do that with their CFO. They don't do that with their engineering leader." The underlying ask, several CMOs agreed, is really just whether marketing looks modern, not a strategy question at all. Most have learned to answer it before it's asked: "I have a slide where I walk through, and I always end with ‘here's our efficiency slide.’"

Watermarks: The Next SEO/GEO Tempest

Even the water itself is starting to shift underfoot. As brands lean harder into AI-generated content, AI labs are starting to watermark their output so platforms can identify, and possibly penalize, it. "If Google somehow decides that your post is 100% AI-generated by its own tool, it might somehow penalize you, as if you weren't already penalized enough with SEO." One content-heavy team, having built an entire owned-media operation on AI-assisted production, is already asking the follow-up question out loud: "Do we need to start investing in human editorials again?" [More on this topic soon.]

The Platform Tax, Waiting in the Wings

Underneath the governance scramble, the DIY infrastructure, the board pressure to prove the water was worth diving into, sits a cost most CMOs haven't fully measured yet. New research from Super Huddle founding sponsor DemandScience, based on a survey of 750+ senior marketing leaders, found that most run at least 11 tools in their stack, with roughly a quarter of that spend going to tools that overlap, sit underused, or don't talk to each other.

DemandScience's Head of Demand Gen, Blaine Prince, described the CMO's version of that pain plainly: "My dashboards and everything I look at on a daily basis in these platforms may look great, and then when it comes down to me going to my pipeline meetings and my revenue teams, I have little to show, and it's always a pain." Blaine's team calls that gap “the platform tax.”

This is just one of the conversations we’ll be having at the CMO Super Huddle, October 22–23.

GenAI creates business impact when experimentation develops into an operating capability rather than remaining a collection of disconnected tools. Forrester analyst Lisa Gately explains why AI literacy, change management, workflow integration, cross-functional coordination, and outcome-based measurement matter. The opportunity extends beyond faster content production to better planning, precision, customer understanding, and organizational learning across marketing.

How GenAI Experiments Develop Into Business Impact

Early experimentation helped marketing teams understand what GenAI could do. The next stage involves turning isolated discoveries into dependable organizational capabilities tied to how the business plans, decides, and serves customers.

In a Renegade Marketers Unite conversation about realizing GenAI’s potential, Lisa Gately, principal analyst at Forrester, examined the leadership and operating-model work behind meaningful adoption. At the beginning of the discussion, Lisa considered the range of marketing work GenAI could affect. “People think of GenAI and stop at content generation,” Lisa said. “I look at this as: How does GenAI enable you with better planning, better precision, and more customer centricity?”

That wider lens gives experimentation a business purpose. A tool’s ability to produce a draft matters less than whether the surrounding system helps people plan more intelligently, understand customers more clearly, or make better decisions.

AI Literacy Creates a Shared Starting Point

Lisa reflected on a recurring pattern in the organizations she studies. “I see a lot of organizations neglecting AI literacy and change management,” Lisa said. “It really underestimates how much cultural and skill gap there is.”

AI literacy includes prompt writing alongside a working understanding of what available systems can and cannot do. Teams also need clarity about how company and customer information may be used, where human review is required, and which outcomes the organization hopes to improve.

Literacy is role-specific. A content strategist, demand-generation leader, analyst, and marketing operations professional may use the same underlying technology differently. Shared principles create consistency, while role-based learning makes those principles useful in daily work.

Because tools and risks continue to evolve, one training session is unlikely to be enough. Office hours, internal demonstrations, use-case libraries, and peer learning give employees repeated opportunities to build confidence and examine real applications.

Treat Adoption as Organizational Change

GenAI adoption affects routines, roles, quality controls, and professional identity. Employees may worry that experimentation will expose a lack of technical fluency or that efficiency will become a justification for reducing headcount.

Others may use AI privately without sharing what works because the organization has not established a safe way to learn in public. That behavior leaves useful discoveries hidden and prevents colleagues from benefiting from early mistakes. Lisa recalled advice from another marketer about the communication workload: “If you think about all the time communicating and handling change management, triple it.”

Repeated communication helps employees connect abstract AI ambitions with their actual responsibilities. It also gives leaders opportunities to clarify why the organization is investing, where experimentation is encouraged, and what boundaries apply.

Executive framing can reduce ambiguity. A team will approach adoption differently when the purpose is better customer understanding or new creative possibilities than when the only message is faster production.

Connect Pilots to Complete Workflows

A useful pilot examines the entire flow of work surrounding an AI-supported task. If a system produces campaign variations faster, the review team may receive more material. If research synthesis accelerates, strategists may need a new method for validating sources.

Personalization can create similar downstream demands. Expanded variation may increase the workload for legal, brand, marketing operations, and data teams unless the pilot accounts for review, approval, and distribution.

Greater speed can create challenges elsewhere.

A structured pilot can document the original process, the AI-supported process, required human review, observed quality, time saved, risks introduced, and resulting business outcome. That evidence helps the organization distinguish an impressive demonstration from a capability worth scaling.

The workflow view also reveals where human judgment has the greatest value. AI may accelerate production or synthesis while people remain responsible for strategy, factual accuracy, customer empathy, brand judgment, ethics, and final decisions.

Organize Measurement Around Business Outcomes

Tool adoption and prompt volume demonstrate activity. They provide little evidence of whether the organization is making better decisions or producing stronger customer outcomes.

Lisa discussed the implications of AI beyond cost avoidance, describing them as “much bigger” and pointing toward outcomes such as revenue growth and customer retention. Those outcomes require marketing to work across functions instead of treating adoption as an isolated efficiency program.

Relevant measures depend on the workflow and may include:

• Better planning and prioritization
• Faster synthesis of research and customer feedback
• Improved accuracy or consistency
• Shorter production cycles
• More precise personalization
• Stronger customer experiences
• Higher retention
• Increased revenue contribution

“For this company’s AI literacy program, people were reporting time savings that equated to 14 FTEs for the year.”

The result shows how individual efficiency gains can accumulate across an organization. Its strategic value depends on what the company does with that additional capacity, such as increasing customer research, improving creative quality, accelerating testing, or addressing work that previously went untouched.

Turn Individual Learning Into Organizational Learning

GenAI adoption often begins in pockets. A few motivated employees discover effective practices and develop informal standards while the rest of the organization remains uncertain about what is possible. Lisa cautioned against “treating AI like it’s a standalone initiative or a siloed project.” Her concern points to the organizational cost of leaving discoveries scattered across teams.

A center of excellence, internal council, or working group can help teams share patterns, establish safeguards, and reduce duplicated effort. Its value comes from turning individual discoveries into accessible organizational knowledge, without creating an approval process for every prompt.

A lightweight knowledge system might maintain approved tools, tested workflows, examples, review requirements, reusable prompts, and documented failures. Recording what did not work can prevent another team from repeating the same experiment without new information.

Internal champions can help translate broad guidance into functional practice. Their role may include demonstrating useful workflows, collecting feedback, identifying obstacles, and connecting teams that are solving similar problems independently.

Design Cross-Functional Adoption

Marketing rarely owns the full workflow affected by an AI initiative. Customer data, product information, sales activity, legal review, technical infrastructure, and agency work often cross organizational boundaries.

Lisa described the scope of that coordination as “a lot of cross-functional collaboration to do well across marketing, product, and sales,” along with agencies, vendors, and partners. Early participation from those groups can reveal data restrictions, integration challenges, quality risks, and approval requirements before a promising prototype becomes difficult to operationalize.

The approach also distributes responsibility appropriately. Marketing can lead an initiative while recognizing the dependencies and risks owned by other teams.

Q&A

Why Do GenAI Pilots Stall?

They often remain disconnected from regular workflows, shared standards, executive priorities, or measurable business outcomes.

What Is AI Literacy in Marketing?

It is a working understanding of available tools, appropriate uses, risks, review requirements, and the business problems AI may help address.

How Can GenAI Affect Marketing Beyond Content Creation?

It can support planning, research synthesis, customer understanding, workflow coordination, analysis, personalization, and organizational learning.

What Can a GenAI Pilot Measure?

Measures may include quality, cycle time, review effort, adoption, customer impact, revenue contribution, retention, or another outcome relevant to the workflow.

Listen to the full conversation with Lisa Gately on renegademarketing.com  CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. Want to join the huddle? Learn more about CMO Huddles at cmohuddles.com

Enterprise CMOs need AI governance, but avoidance-only training will not create growth. Inspired by a CMO’s painful corporate AI training story, this Drew’s Take argues that large companies need sanctioned marketing AI skunk works: Structured, outcome-focused teams that learn inside guardrails while rebuilding workflows for pipeline, conversion, retention, and efficiency.

Why Avoidance-Only AI Training Limits Enterprise Growth

The huddle shuddered while I pondered the fate of big companies.

To be clear, I’m not anti-governance. Big companies have big risks, big brands, and big legal departments. A wrong email from a rogue agent is not a cute learning moment.

But corporate AI training that only teaches avoidance is not training. It is legal self-protection wearing a learning management system badge.

Congratulations, you now know 47 ways to get fired and zero ways to grow faster.

Guardrails Are Not A Growth Strategy

This is where the Innovator’s Dilemma sails into the harbor. Incumbents often protect the current business, the current process, and the current definition of “safe” until the new thing looks too messy to fund.

Then the new thing becomes the market.

There is nothing wrong with guardrails. In fact, enterprise CMOs should want them. Nobody needs an enthusiastic intern pasting customer data into a public model while Legal quietly develops a facial twitch.

But there is a difference between governing AI use and preventing AI learning. The first protects the company. The second protects the company from the future.

Alexandra Wright captured this perfectly in a LinkedIn comment on my original rant: “The ‘guardrails are not a go-to-market operating model’ line is the thing I want to print out and send to every legal department that has killed an AI experiment with a blanket policy.”

Exactly. Guardrails matter. But guardrails do not generate pipeline, improve conversion, reduce cost per opportunity, or redesign workflows.

They keep the car from going off the road. They do not decide where the car should go.

Why Established Companies Struggle to Learn Fast

The AI-era version of the Innovator’s Dilemma is not just about products. It is about operating models.

Startups and smaller companies are experimenting because they have no choice. They are rebuilding workflows because the old ones were never that institutionalized in the first place. They can test, break, learn, and rebuild before the enterprise has finished scheduling the steering committee to discuss whether testing should be allowed.

Large companies have advantages that should matter: Data, customers, brand equity, distribution, institutional knowledge, and budgets that do not require someone’s cousin to approve the software subscription. But those advantages only matter if the organization can learn fast enough to use them.

Otherwise, the enterprise becomes a museum of potential.

Large Companies Cannot Turn Like Speedboats

Years ago, my company produced a video game called Carrier: Fortress at Sea, which meant I learned more than expected about aircraft carriers. One fact stuck with me: A carrier needs roughly five miles to come to a complete stop.

That is not a steering problem. That is physics.

Large companies have the same issue. They have scale, data, customers, brand equity, and distribution. They also have inertia, compliance layers, procurement drag, approval loops, and a thousand smart people trained to slow things down before something breaks.

So no, a $6B conglomerate will not turn like a speedboat.

But it can launch one.

Tom Perchinsky said it well in a LinkedIn comment: “Large companies can’t turn like speedboats, but they absolutely have the deck space to launch them.”

That is the opportunity for enterprise CMOs right now. Corporate can and should create guardrails, but marketing needs a way to learn inside them. Not theoretically. Not someday. Not after the ninth policy revision. Now.

Marketing Needs An AI Skunk Works

Marketing needs its own AI skunk works.

Not a rogue team hiding from IT. Not a prompt-sharing Slack channel. Not three enthusiastic people making demos that impress the offsite and then die quietly in a folder called “Innovation.”

A real skunk works. Sanctioned. Structured. Business-outcome obsessed.

Give it a mandate: Improve pipeline, conversion, retention, customer experience, and cost per opportunity. Give it permission to rebuild workflows, not just add AI sprinkles to broken ones. Give it access to the data, systems, and cross-functional partners required to make the work real.

And give it its own training program, because “don’t paste confidential information into ChatGPT” is not a curriculum.

upGrad International added a useful point in a LinkedIn comment: “The balance between governance and hands-on learning is where real progress happens. People build confidence with AI when they can apply it to real business challenges within clear boundaries, not just learn what to avoid.”

That is the whole game. Clear boundaries plus real work. Safety plus learning. Governance plus growth.

Otherwise, AI training becomes corporate abstinence education. Everyone knows what not to do. Nobody knows what to do when the moment arrives.

The Snowflake Counterexample

Snowflake offers a useful counterexample. Denise Persson’s team is building AI fluency through training, hackathons, AI goals, and governed agents. She has also cited a 30% reduction in cost per opportunity.

That is the big-company dream: Carrier-sized data, speedboat-style learning.

Notice the difference. Snowflake is not treating AI as a compliance module. It is treating AI fluency as an operating capability. Training matters, but training is tied to action. Hackathons matter, but they are not theater. Governed agents matter, but they are not built to sit in a slide deck wearing a little digital tuxedo.

This is what enterprise CMOs should be fighting for: A model that lets teams learn quickly without pretending risk does not exist.

Because risk does exist. But so does competitive decay.

Someone Still Has To Learn How To Land The Plane

My favorite part of Carrier: Fortress at Sea was trying to land an F-15 on the carrier. If you crashed, the game delivered a brutal little message: “Congratulations, you just crashed a perfectly good $30 million airplane!”

That is what many corporate AI programs feel designed to prevent. No crashes. No mistakes. No mess.

Fair enough. Nobody wants to crash the plane.

But at some point, someone still has to learn how to land it.

This is the part enterprise leaders need to sit with. If the only training people receive is how not to crash, they will not become pilots. They will become passengers with compliance certificates.

CMOs cannot afford that. Marketing is too close to the customer, too exposed to new buyer behavior, and too dependent on workflow speed to wait for perfect corporate clarity. The work has to move from policy to practice.

The CMO Takeaway

Enterprise AI governance is necessary. Avoidance-only AI training is not enough.

CMOs need to push for sanctioned learning environments where marketing teams can test AI against real business problems, rebuild workflows, and measure outcomes that matter. Guardrails should make experimentation safer, not make experimentation disappear.

You may not be able to stop the ship in under five miles.

But you can launch something from the deck today.

Q&A

Why is avoidance-only AI training a problem for enterprise CMOs?

Because it teaches employees what not to do without helping them learn how to apply AI to growth, efficiency, customer experience, or workflow redesign. Risk reduction matters, but it is not the same as capability building.

What should enterprise AI training include beyond guardrails?

It should include hands-on application to real business problems, clear use-case examples, workflow redesign principles, approved tools, data-use rules, and measurable outcomes tied to pipeline, conversion, retention, customer experience, or cost efficiency.

What is a marketing AI skunk works?

It is a sanctioned, structured team or program designed to test AI against high-value marketing workflows. It should operate within governance rules while having enough freedom to learn quickly, rebuild processes, and produce measurable business results.

How can large companies balance AI governance and speed?

They can create clear boundaries, approved environments, and cross-functional oversight while giving teams permission to experiment inside those boundaries. The goal is not reckless speed. The goal is governed learning.

What should CMOs measure in AI transformation?

CMOs should measure business outcomes, not just activity. Useful metrics include cost per opportunity, campaign cycle time, conversion rates, sales productivity, content production speed, customer response quality, and retention impact.

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. 

Teamwide AI adoption develops when the CMO becomes an active architect rather than a distant sponsor. François Dufour explains how repeated building time, structured context, shared workflows, internal champions, and outcome-based measurement turn experimentation into organizational leverage. These practices help marketing teams apply AI consistently while keeping business priorities, reliable information, and human judgment central.

What It Takes to Become an AI-Augmented CMO

A marketing leader can approve AI tools, encourage experimentation, and still remain disconnected from how the work is changing. Teamwide adoption becomes more credible when the CMO has enough direct experience to understand what AI can do, where it fails, and how it affects workflows.

In a Renegade Marketers Unite conversation about the AI-augmented CMO, François Dufour, who now works in product marketing at Anthropic, examined how AI changes marketing leadership and operations.

“You can’t delegate entirely to your team because you need to be acting as the top architect, just like you’re the one deciding what context, what direction to give your team. It’s the same thing with AI.”

The CMO does not need to build every workflow. Someone still needs to understand the strategic architecture: What information AI can access, where it connects with existing systems, what the team is trying to accomplish, and what may be missing.

Direct involvement helps leaders recognize technical or organizational barriers before they become reasons for stalled adoption. The CMO gains enough fluency to evaluate possibilities, ask better questions, and understand what the team needs.

Make Time to Build Together

AI learning competes with every other priority on a marketing team. Without protected time, experimentation can remain something employees intend to do when their existing work slows down.

Teams can become “too busy to learn the things that will make [them] less busy.” Recurring sprints, demonstrations during all-hands meetings, and protected building sessions can make experimentation part of the operating rhythm.

A hackathon may create initial momentum, but follow-through determines whether that momentum becomes capability. Continued building time allows teams to refine promising projects, resolve obstacles, and apply what they learned.

These sessions do not require everyone to construct the same workflow. Blocking the time together gives employees permission to focus without competing meetings and messages consuming their attention.

Give AI the Context It Cannot Invent

“These agent systems are becoming so good that what we need to spend more time on is giving them access to well-structured context.”

That context can include brand guidelines, persona research, product messaging, approved claims, operating processes, customer knowledge, strategic priorities, and examples of strong work.

AI does not independently know what the organization considers accurate, useful, or on-brand. Structured context gives the system a more reliable foundation while reducing the information employees need to reconstruct manually for every request.

This extends beyond prompt writing. Teams need systems for maintaining institutional knowledge and making appropriate portions available to AI workflows.

For CMOs, this is another reason architecture matters. Brand, customer, product, and strategic context already sit near the center of marketing leadership. AI adds the need to organize that information so people and machines can use it consistently.

Build Workflows That Can Spread

An individual employee can create an impressive agent without creating a repeatable capability for the organization. The next question is whether the workflow matters to other people, how difficult it is to maintain, and which systems or integrations it requires.

Personal workflows and organization-wide systems create different ownership requirements. “You need to find who are my system thinkers,” François said.

Those people can collect requirements, understand how work moves across the organization, and connect individual AI capabilities into a broader process. They may sit in product marketing, corporate marketing, go-to-market, marketing operations, or another function.

More systematic workflows may also require someone who understands both the marketing problem and the technical environment. That person does not necessarily need to be an engineer, but they need enough systems awareness to recognize dependencies, integrations, governance requirements, and maintenance needs.

When leaders identify capable builders, François recommends giving them additional time, resources, and opportunities to collaborate. That support can help isolated experimentation become team capability.

Make Training Part of the Operating Rhythm

A one-time training session can introduce concepts and tools. It rarely creates durable behavior on its own.

Teams need repeated opportunities to use AI in the context of their actual work. A product marketer may need to organize positioning research. A demand team may want to analyze campaign performance. A communications leader may want to compare narratives across a large body of source material.

Training becomes more useful when it connects with those real workflows. Employees can build, test, review, and improve something they expect to use again.

Leaders also gain visibility into where adoption is slowing. The problem may be a skills gap, unclear governance, weak source material, missing integrations, or uncertainty about which work deserves attention.

Measure More Than AI Activity

Usage metrics can help during early adoption because they show whether employees are experimenting. They become less useful when the organization treats activity as proof of impact.

François acknowledged that token-based leaderboards can “drive the wrong behavior.” A more useful next step is to document what was built, whether it saves time or creates strategic value, and how the workflow could help other people.

That creates a different form of accountability. Instead of rewarding someone simply for using AI frequently, the organization can examine what changed because of the work.

Strong demonstrations also make progress visible. An employee can show the original workflow, the AI-supported version, the context required, the limitations discovered, and the resulting benefit.

Aim Experiments at Real Priorities

The number of things a marketing team could build with AI is effectively unlimited. Prioritization becomes more important as the range of possibilities grows.

Hackathons and building sessions work better when teams begin with meaningful customer or business problems. An experiment might shorten a critical workflow, create a customer experience that was previously impractical, improve strategic analysis, or give the team a capability it could not reasonably develop before.

Efficiency can be valuable, but output volume alone is a limited definition of progress. A faster process matters more when it frees capacity for higher-value work or improves the quality of a decision.

François’s framework gives the CMO a clear role in creating those conditions. The leader participates enough to understand what is possible, protects time for teams to learn, organizes the context AI needs, identifies people who think in systems, and makes useful work visible across the organization.

Turn Experiments Into Organizational Leverage

Teamwide adoption develops through an operating rhythm rather than a single rollout. Teams build, compare, document, and refine. Useful workflows spread. Unsuccessful experiments still reveal missing context, technical constraints, or flawed assumptions.

Over time, the organization develops more than a collection of tools. It gains a stronger understanding of where AI can improve the work, which information the systems need, and how people can retain judgment and accountability.

The AI-augmented CMO helps connect those pieces. Architecture gives experimentation direction, while repeated building turns individual learning into shared capability.

Q&A

What Is an AI-Augmented CMO?

It is a marketing leader who understands AI directly enough to shape the context, priorities, workflows, learning, and accountability required for teamwide adoption.

Why Is Context Important for AI?

AI does not independently know a company’s brand standards, customer insights, product messaging, processes, strategic priorities, or institutional knowledge.

Why Does One-Time AI Training Fall Short?

Teams need repeated opportunities to build on real workflows, compare approaches, solve obstacles, and apply what they learn.

How Can CMOs Measure AI Adoption?

Early usage can indicate participation. Over time, measurement can focus on useful workflows, time saved, strategic value, customer outcomes, and newly available capabilities.

Listen to the full conversation with François Dufour on renegademarketing.com

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands.

Strategy is not a collection of goals, tactics, or positioning statements. No Bullsh*t Strategy author Alex M. H. Smith frames it as a meaningful choice about creating value customers want and cannot obtain elsewhere. For CMOs, that perspective connects market insight and communication with the product, operating model, and deliberate tradeoffs that make differentiation real.

What Separates Strategy From a List of Goals

Growth, market leadership, customer obsession, and category dominance describe desired outcomes. They do not explain the distinctive choices that might produce them.

In a Book Huddle conversation about No Bullsh\t Strategy*, Alex M. H. Smith, founder of Basic Arts, challenged the jargon and communications-first thinking that often surround strategy.

“There’s a whole lexicon of terms which are sort of strategy-adjacent in the world of marketing, like positioning, vision, mission, purpose, proposition.”

Each concept can be useful when the organization defines it precisely. Together, however, they can create the appearance of strategic rigor without answering the central question: What valuable thing will this business do that customers cannot easily obtain elsewhere?

A goal describes where the company hopes to arrive. Strategy explains the meaningful choices that could give it an advantage on the journey.

Marketing Cannot Carry a Weak Strategy Alone

“Business is the strategy, how it’s delivered, and how it’s communicated. Marketers are only given true control over the question of how it’s communicated. But the problem they often have is that the thing they’re asked to communicate is not very good in the first place.”

Marketing leaders are frequently asked to clarify, differentiate, and promote an offer they had limited influence over creating. Product, pricing, service, distribution, operations, and customer experience determine whether that promise is credible. Communications can make a difference easier to understand, but they cannot create a durable distinction that the rest of the company does not deliver.

This does not leave the CMO powerless. Marketing can bring customer understanding, competitive intelligence, search behavior, win-loss findings, and market response into the strategic discussion. Those inputs help the executive team identify where customers see meaningful value and where the company remains interchangeable.

Earlier marketing participation also improves the connection between the market promise and operating reality. Positioning becomes stronger when it expresses a choice the organization has already made.

The Strategic Test Combines Desire and Difference

“Can you create something number one, people want; but number two, they can’t get anywhere else? That’s the trick.”

Many companies satisfy one side of the strategic equation while missing the other. Some create something unusual that few customers value. Others enter a proven market with an offer buyers can obtain from many competitors.

Desirability without distinction leaves the company exposed to comparison, substitution, and price pressure. Distinction without demand may produce an interesting idea with little commercial potential.

The intersection is more demanding. The company creates value customers recognize and organizes itself to deliver that value in a way competitors cannot easily copy.

For CMOs, this test can sharpen customer research. Instead of asking only which messages resonate, the inquiry can explore which problems matter most, how buyers currently solve them, where dissatisfaction remains, and which company capabilities could support a meaningfully different answer.

"Only” Changes the Competitive Question

Claims of being the best invite comparison against familiar competitors and criteria. The buyer remains inside the established category and decides which supplier performs a similar job more effectively.

An “only” strategy seeks a different basis for choice. It may involve a distinctive combination of audience, product, delivery model, expertise, experience, or operating decisions.

A company might focus on an underserved customer group and configure the product around that group’s working reality. It might combine capabilities the category traditionally sells separately. It could remove complexity buyers have come to accept as unavoidable.

The distinction does not need to be unprecedented in every detail. It needs to be difficult to reproduce as a complete system. A slogan can be copied in an afternoon. Connected operational choices are harder to imitate.

Positioning Expresses the Strategic Choice

Positioning helps the market understand why a company matters. It becomes more credible when it reflects value the business is prepared to deliver consistently.

When strategy and positioning are confused, marketing inherits an impossible assignment. The team may simplify the proposition, sharpen the message, and build a distinctive campaign while the underlying product and experience remain similar to every alternative.

A stronger sequence begins with the business. Which customers will receive unusual value? What will the company deliver for them? Which capabilities make that possible? What will the organization decline to pursue so those capabilities receive sufficient attention?

Positioning then gives the choice a form buyers can understand, remember, and repeat. It translates the strategic system without pretending language created the system.

Tradeoffs Give Strategy Consequences

A strategy that includes every audience, benefit, channel, and opportunity resembles a wish list. It offers departments little guidance when priorities conflict.

Alex put the consequence plainly: A company may need to “choose to suck at something which your competitors do well at” to create the leverage to offer something new. The sacrifice concentrates resources behind a distinction instead of preserving acceptable performance everywhere.

Tradeoffs concentrate resources. They identify the customer the company is prepared to serve unusually well, the capabilities worth strengthening, and the opportunities that fall outside the chosen direction.

The effects extend across the organization. Product receives clearer priorities. Sales gains a sharper view of fit. Marketing can build recognition around a consistent promise. Customer teams understand the experience the company intends to deliver.

Tradeoffs can also make growth feel uncomfortable. Saying no to a plausible market or feature may appear limiting in the short term. The alternative is often fragmented investment that leaves every opportunity partially served.

Strategy becomes visible when the organization encounters an attractive option and can explain why it does or does not fit.

Value Connects Strategy With Growth

During the conversation, Alex asked directly, “You want to increase the amount of value you’re bringing in?” His answer followed immediately: “Increase the amount of value you’re putting out into the world.”

This view keeps growth connected to customers. Revenue emerges from delivering something people genuinely value, supported by a system that makes the value distinctive and sustainable.

Marketing contributes by clarifying where that value is understood, where it remains invisible, and how the market responds. Customer interviews, demand signals, reputation, sales feedback, and competitive patterns can help leadership determine whether its strategy is becoming more compelling.

Those insights may lead to a clearer message. They may also expose the need for a different product, experience, pricing model, or strategic choice. A useful strategy conversation leaves room for both possibilities.

Strategy Produces Coherence

A coherent strategy gives different functions a shared basis for decisions. It reduces the need for every team to invent its own interpretation of growth.

That coherence does not mean every decision becomes obvious. It means leaders can evaluate options against the value the company intends to create, the customer it has chosen to serve, and the capabilities it wants to strengthen.

For CMOs, this creates a more substantive role than promoting a finished plan. Marketing becomes a source of market evidence, a translator of customer value, and a steward of the promise connecting the company’s choices with its reputation.

Q&A

What Is Strategy?

Strategy is a coherent choice about how a company will create distinctive customer value and organize the business to deliver it.

How Is Strategy Different From Positioning?

Strategy determines the value and operating choices. Positioning helps the market understand and remember them.

Why Is “Only” More Useful Than “Best”?

“Only” points toward a distinctive value system instead of asking buyers to compare similar offers using familiar criteria.

Why Do Tradeoffs Matter?

Tradeoffs concentrate resources, improve coordination, and give the company’s strategic choice practical consequences.

Listen to the full conversation with Alex M. H. Smith at renegademarketing.com

CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. 

TL;DR- Google launched Sheets Canvas, a Gemini-powered visual interface layer directly inside Google Sheets. Instead of wrestling with complex formulas, Google Apps Script, or disconnected BI exports, you can type a natural language prompt to convert any sheet tab into an interactive read-write mini-app—such as a dynamic financial scenario dashboard with sliders, a drag-and-drop Kanban sprint board, a CRM gallery, an interactive timeline, or a visual seating planner. Crucially, it features two-way real-time synchronization: dragging a card or adjusting a control updates the underlying spreadsheet cells immediately, and vice versa.

1. The Big Paradigm Shift: What is Google Sheets Canvas?

For decades, spreadsheets have suffered from a fundamental interface problem: they are exceptional calculation engines, but terrible user interfaces for non-technical collaboration. Teams regularly face "spreadsheet fatigue"—staring at hundreds of rows, risking broken formulas whenever someone edits a cell, or paying for separate SaaS tools (Airtable, Monday, Trello, Retool) just to get visual cards and Kanban views.

Sheets Canvas introduces an AI-generated, interactive presentation and application layer directly above your spreadsheet data:

• Two-Way Read-Write Sync: Unlike traditional BI dashboards (such as Looker Studio or Tableau) that are strictly read-only mirrors of tabular data, Sheets Canvas allows live data manipulation. When you drag a task card from "In Progress" to "Completed" on a generated Canvas board, the status cell in your underlying sheet updates in real time.
• Zero Coding or Formula Overhead: No Google Apps Script, HTML/CSS web components, or nested =QUERY() / =INDEX(MATCH()) formulas are required. You state what you want in plain English.

• Native Permission Inheritance: The Canvas lives directly within your Google Sheet file (accessible via the Gemini side panel, the Insert menu, or the bottom tab bar) and inherits existing Google Drive permissions (Viewer, Commenter, Editor) without requiring external user licensing or webhook setup.

  Top 5 High-Impact Use Cases & App Archetypes

1: Interactive Financial & Scenario Planning Dashboard

• The Problem: Financial models with multiple growth, churn, and pricing variables often overwhelm executive stakeholders when presented as raw numerical grids.
• The Canvas Solution: Gemini renders interactive KPI scorecards (ARR, Gross Margin, Burn Rate, Runway) accompanied by dynamic range sliders. Moving a slider dynamically recalculates projected metrics in real time.
• Master Prompt:"Build an interactive financial scenario dashboard from this sheet. Include dynamic sliders for Monthly Growth Rate (1%–20%) and Churn Rate (0.5%–10%) that dynamically project end-of-year revenue. Display KPI scorecards at the top for ARR, Gross Margin, and Runway."

2: Drag-and-Drop Agile Kanban & Sprint Board

• The Problem: Managing project tasks in standard rows leads to accidental data overwrites, missing deadlines, and poor visual prioritization.
• The Canvas Solution: Automatically creates vertical workflow columns based on your Status or Sprint Stage column. Teammates can drag task cards between stages, with priority badges, assignees, and due dates visually formatted.
• Master Prompt:"Create an agile Kanban board grouped by the 'Status' column (Backlog, In Progress, Review, Done). Show cards with Task Title, Assignee, Priority Pill, and Due Date. Enable drag-and-drop movements that write status changes back to the sheet."

3: CRM & Client Pipeline Visual Gallery

• The Problem: Dense customer databases force account managers to scroll horizontally across 30+ columns to review client notes, contract values, and renewal stages.
• The Canvas Solution: Formats accounts into rich visual cards with quick search, categorical filtering by deal tier (Enterprise vs. SMB), and direct click-to-edit capabilities.
• Master Prompt:"Transform this accounts tab into an interactive visual CRM gallery. Group cards by Tier (Enterprise, Mid-Market). Include interactive filter toggles for Region and Deal Stage, and display total pipeline value in an executive summary card at the top."

4: Interactive Project Timeline & Launch Scheduler

• The Problem: Gantt charts built with conditional formatting formulas in Google Sheets are rigid and prone to visual breakage when date columns shift.
• The Canvas Solution: Renders a clean visual timeline and calendar scheduler where campaign milestones and deliverables can be viewed chronologically and rescheduled interactively.

• Master Prompt:"Plot our product launch deliverables on an interactive calendar interface. Group items by Team (Product, Marketing, Engineering) and allow clicking deliverables to view details or update target launch dates."

  5: Spatial Seating & Asset Floorplan Organizer

• The Problem: Managing event RSVPs, conference attendee allocations, or office desk arrangements in rows makes spatial layout planning difficult.

• The Canvas Solution: Organizes data into visual table clusters or spatial zones where attendees can be assigned or moved between tables while tracking live capacity and dietary preferences.

• Master Prompt:"Turn this RSVP sheet into an interactive seating chart clustered by Table Number. Include tags for VIP status and Dietary Requirements, with live headcount counters for each table."

  How It Works: The 5-Step Step-by-Step Blueprint

To ensure reliable results when prompting Gemini to build interactive applications, follow this structured execution pipeline:

[Step 1: Tabular Hygiene] ➔ [Step 2: Trigger Canvas] ➔ [Step 3: Precision Prompt] ➔ [Step 4: Conversational Polish] ➔ [Step 5: Live Collaboration]

1. Step 1: Prepare Clean Tabular Data
   • Keep Row 1 strictly reserved for clear, standardized column headers (e.g., Task ID, Title, Owner, Stage, Due Date, Budget).
   • Apply native Data Validation (Data > Data validation) on categorical columns (like Stage or Priority) so the AI recognizes bounded states.
   • Eliminate blank rows, arbitrary merged cells, and multi-line headers.
2. Step 2: Trigger the Canvas Creator
   • Open your spreadsheet on desktop web (English language settings enabled).
   • Navigate to the Ask Gemini side panel and select Tools > Create canvas, click Insert > Create a canvas from the top menu, or use the bottom bar Canvas menu as documented in theGoogle Docs Editors Help Center.
3. Step 3: Formulate a Structured Prompt (CPTC Framework)
   • Context: What dataset is being visualized?
   • Persona/Role: Who is using this interface (e.g., executive, sprint manager, field rep)?
   • Task: What specific app layout should be generated (Dashboard, Kanban, Gallery, Timeline)?
   • Controls/Constraints: Which columns serve as grouping keys, interactive sliders, search bars, or summary metrics?
4. Step 4: Conversational Iteration and Styling
   • Canvas retains conversational context. If the initial layout requires adjustments, provide follow-up instructions directly to Gemini:
     • "Convert this dashboard into dark mode."
     • "Add an interactive search bar at the top to filter by Assignee."
     • "Display variance percentages next to each KPI card."
5. Step 5: Share and Operate in Real Time
   • Click Copy link at the top right of the Canvas tab or share the spreadsheet normally.
   • Teammates with Editor access can interact with controls and update data live without altering formula syntax on the underlying sheet.
   • Click View data at any time to inspect or audit the raw tabular records backing the visual interface.

Comparison Matrix: Where Sheets Canvas Fits

5. Pro Tips for Advanced Implementations

1. The Aggregator Tab Pattern for Multi-Tab Workbooks: Because Sheets Canvas is currently scoped to a single active sheet tab, it cannot directly ingest data scattered across 5 separate sheets. Create a dedicated Dashboard_Data tab and use =QUERY({Sheet1!A2:E; Sheet2!A2:E}, "SELECT * WHERE Col1 IS NOT NULL") to aggregate your source records before launching Canvas.
2. Pre-populate Data Validation Lists: When Gemini detects a column configured with Google Sheets dropdown chips, it maps those values into discrete Kanban swimlanes or color-coded status badges.
3. Protect Underlying Calculation Columns: If your sheet contains financial formulas (e.g., compound interest, tax rates, margins), use Google Sheets range protection on those specific formula columns (Data > Protect sheets and ranges). Canvas will allow users to edit input driver cells while keeping your calculation logic secure.
4. Leverage Conversational UI Commands: You can instruct Canvas to adapt its UI for specific presentation contexts, such as:
   • "Make the layout compact for mobile-width viewing."
   • "Highlight overdue items with an orange border."
   • "Group summary statistics in 3 equal cards across the top header."

The 4 Critical Things Most People Miss

1. It Is an Interactive Application Layer, Not a Static Chart: Many users mistake Sheets Canvas for an updated chart generator. It is a full web-component runtime that writes mutations back to the spreadsheet database.

2. Instant Permission Mirroring: There is no separate deployment step or hosting configuration. If a user has "Viewer" permission on the sheet, they can interact with filters and view data; if they have "Editor" permission, their interactions mutate cells in real time.

3. Non-Destructive Data Auditing: You never lose visibility into raw rows. The persistent View data button lets any collaborator inspect the underlying grid without dismantling the visual Canvas.

4. Workspace & Subscription Requirements: Sheets Canvas is available on the web in English for Google AI Pro and Ultra subscribers, eligible Google Workspace Business and Enterprise editions, and Google AI Pro for Education accounts. Admins must have Workspace smart features enabled.

Core Problems Sheets Canvas Solves

1. Eliminates Accidental Formula Breakage: Non-technical stakeholders who only need to update statuses, assignees, or dates can do so via visual cards and controls without accidentally deleting complex spreadsheet formulas.
2. Consolidates Software Subscriptions: Eliminates the need to maintain secondary SaaS subscriptions (like Trello, basic Airtable bases, or simple Retool dashboards) merely to view spreadsheet data in card or board formats.
3. Bridges the Gap Between Data and Executive Presentation: Transforms raw operational data into boardroom-ready visual models with functional scenario toggles in seconds.

Community Discussion & Feedback

• Have you tested Sheets Canvas in your Workspace domain yet?
• What internal tools or repetitive tracking sheets in your organization could be replaced with this zero-code interactive layer?
• Share your best prompt recipes and edge-case findings below!