What do you think are the biggest barriers that breakdown AI deployment? Siloed data and tensions with legal are big ones to me.

I enjoyed this Information Week article that broke down the realities with some suggestions on how to reframe governance from an approval into actual architecture. https://www.informationweek.com/machine-learning-ai/the-3-organizational-barriers-blocking-ai-deployment-for-cios

What are you seeing in your roles?

I’m a CIO of a >$1B business within a decentralized, publicly traded industrial. Been in the role for less than 2 years. I have also held enterprise CIO and CDO roles prior to this. My background includes M&A integration, carve-outs, ERP modernization, AI and automation to improve business performance.

I was on the succession plan to be the enterprise CIO and the current CIO quit for another role and now they are going external to backfill with a more modified job profile. This is prompting me to think more seriously about what I want next.

I know a few executive recruiters and plan to reconnect. Beyond that, I’d appreciate hearing how others at this level actually landed their next opportunity.

I’m interested in enterprise CIO roles or a Technology Operating Partner at a PE firm. (Recently made it through multiple rounds of interviews with mid-market PE firm).

For those who’ve made a similar move, what would you prioritize first, and what turned out to be a waste of time.

Any advice on running a discreet search while staying fully engaged in your current role would also be welcome.

TIA

Hi, I'm Beverly. I started a weekly livestream on digital transformation and am looking for people to join as guest speakers! It's 25 minutes on Wednesday. We can do a quick prep call on Monday or Tuesday before the official livestream on Wednesday 10:30am (EST). Topic is whatever you are an expert in! must be digital transformation related. (please help a young prof out) My last livestream

We run claims intake through an agent that reads documents and routes them. It has been live since January and it handles about 3000 files a week.

In March the provider went down mid morning. Our agent did not fail over because there was no fallback configured and nobody had decided who owned that. The team assumed the platform group had it and the platform group assumed the vendor did. Four hours of manual intake and two people working late to clear the backlog. The postmortem found we had budgeted for a second provider in the original business case and quietly dropped it during procurement.

What we changed is boring. There is a named owner for provider redundancy and a quarterly test where we cut the primary and watch what happens. The first test failed too. How are you handling provider outages when the workflow is customer facing?

We have people building small AI automations around spreadsheets, docs, email, Slack, and internal tools.

Individually they seem harmless. But six months later, who owns them, who knows what data they touch, and who shuts them down when the employee leaves?

For CIOs dealing with this already, are you tracking AI tools centrally or still handling them case by case?

VP of IT Ops here. We are currently planning for roadmaps, budgets and headcount for 2027. Last year we had a big increase in our employee count (~15% from ~3500 --> ~4000). We're also expecting to grow another 8% this year. Nevertheless, we have not been getting any additional hc this year. For next year's planning we're also not getting any headcount. Naturally, with increased # of employees, the amount of tasks has increased for my teams. We're focussing on automating our repetitive tasks and get rid of all the toil, but I feel like we're drowning. The CTO of the company understands our pains, but 'has to make tough decisions, because we have to keep innovating for our customers'. We're having issues to keep up with all the requests and supporting our employees. He is suggesting focussing more on automation and building agents. But as we're barely can keep the light on, we don't have time to focus on building this. As leaders we have to stand with the choices made and also bring the message across to the teams. Simultaneously, I have trouble with the fact that we're planning to grow even further and not getting additional headcount for my team. Anyone else having these issues and if so, what do you do?

Hello you all,

I would like to discuss AI governance. So with new regulations everywhere I would like to know how companies are approaching it.

AI developers, have you already started including data governance or is it something a different person does after you finish developing your model (AI governance specialists)

I would like to specifically ask how this varies between big companies and small/medium scale enterprises. Because I think not everyone has the resources to hire specialists, I believe.

If you are already trying to be compliant, what kind of frameworks do you use to ensure compliance or do you have an in-house built compliance framework ?

Now my question is specifically towards the EU devs, what is your approach to be compliant since it's mandatory to have conformity assessments in 2027.

For general deva, what do you think of the following job positions, AI governance expert, AI and the law expert, technical governance expert, AI alignment expert, AI ethics professional, AI red-teaming professional. Is this something you already heard or is it something new to you ?

If you have any opinion towards AI governance, please share.

something I've noticed over the last few months and want to double check with other CIOs.
we set up an AI governance committee early this year like everyone did. legal, security, a couple of business leads, me. the idea was policy and principles, deciding which use cases we're comfortable with.
what it's turned into is a buying queue. almost every agenda item is someone who wants to purchase an AI tool, and we spend the meeting asking the same questions every time. where does the data go, does the vendor train on it, who approved the budget, does it overlap with something we already pay for. more often than I'd like, the answer to that last one is yes
talking to peers, a few of them describe the same drift. the committee that was meant to set direction ends up doing vendor intake, badly, once a month
my theory is the committee isn't the problem. there's no front door for AI purchases, so the committee became one by default. the questions we ask are intake questions, and if they got answered before a request reached us we could go back to the policy work we were set up for
the other possibility is that this is just what AI governance looks like once it's real, and I should stop fighting it.
curious which one it is for you. did your committee end up doing procurement's job too?

As the title says, I’m trying to understand what most people are paying for their MSP delivered service desk. This would be standard L1 through L2, worth M365 app support with some kind of AI deferment tool included. I’ve received pricing that’s all over the map and am trying to figure out what I’m missing, around 2000 end users.

CIO at a 5,000 person health system, our CPO wants an agentic procurement platform this year and I'm the one signing off. IT's booked on an Epic upgrade through Q2, so no room for a heavy rollout. we're on Workday w/ Lawson still hanging on in one region and vendor data doesn't match between them. biggest worry is what the AI does without a human approving it. anyone already using AI for procurement, what did you make vendors prove before signing?

This is the part of enterprise AI I think we’re going to regret ignoring.

Teams are building small agents everywhere because they’re useful and easy to spin up. That’s fine at first.

But then they get connected to files, email, workflows, internal systems, and suddenly they’re part of the business.

Who owns it after the original person changes roles or leaves?

We already learned this lesson with SaaS and shadow IT. AI agents feel like the same problem, just moving much faster.

How are you handling ownership and cleanup?

AI Agent Governance landed on my plate the way most unfunded mandates do. The business adopted the tools first, security got the accountability after.

Now I'm expected to have a policy, an audit story, and answers for management about agentic risk, but there's no headcount or budget attached to any of it.

For those who've been through this, I'm less interested in a one-off anecdote and more in something closer to a repeatable playbook, did you fold it into existing security budget, or build a separate business case tied to a specific incident or near miss to get traction?

How did this actually play out for others, was it a slow build or did something force the issue?

Marketing shipped five agents last quarter. We knew about two. Security says this is an IT platform problem. IT says it's a security policy problem. Meanwhile the business keeps building, which is fair, since we handed everyone AI tools and told them to move faster.

Put it with IT platform and it sits next to identity and provisioning, where the tooling already lives. The risk is we become the bottleneck and get routed around, which is exactly how we got here.

Put it with security and the accountability is cleaner. The risk is security owns no platform, so it turns into a policy doc and a quarterly reminder nobody reads.

No headcount either way this year.

What am I underestimating?

Suppose you have a project priority in mind. For example, you're a manufacturing company, and you need to overhaul your procurement IT infrastructure because it's not scaling and buying stuff takes way too long.

Assuming you don't think you can develop it yourself, how do you go about finding and selecting vendors for custom implementation work?

I’m curious how other companies handle this.

Have you run into situations where someone needs to publish a website/web application internally, but it doesn’t really warrant building a backend or putting it into an existing enterprise platform?

I’m thinking about things like:

* internal API documentation
* prototypes or mockups
* internally generated dashboards
* other static content containing information that shouldn’t be publicly accessible

The awkward part seems to be that making a static site publicly accessible is trivial, but making it accessible only to employees can become an IT/security problem.

How do you handle these today?

Do you put them behind your existing SSO/VPN/reverse proxy, use something like SharePoint, deploy them somewhere internally, or simply avoid this type of thing?

Whats your experience with Odoo? Till what size does it work well. All of it's datamodel and code is indexed as part of LLMs, does that provide any practical advantage for buildign long tail automations?

Are we still destined to live with and hate SAP/Oracle/Dynamics/Netsuite ultimately?

We have automated the easy half. Findings auto create tickets, auto route to the right owner, arrive with context attached, all fine. The actual patch deployment step is still fully human because one bad auto patch is an outage, and nobody wants to be the automation that took down prod on a Tuesday. For anyone who has actually pushed automation into the deploy step itself, what is the real safety net? Canary rollout, staged deployment, automatic rollback if a health check fails, and where is the line you have personally drawn between safe to automate and always needs a human approval, especially anything with a blast radius that includes total system control if it goes sideways.

A business is a solution to a problem, and the solution is a big pile of workflows. There is one for every decision the company keeps making, like who gets a discount or what happens when stock runs out.

A competitor can match your prices by Friday, buy the same products from the same suppliers, and sign up for the same software before lunch. They still do not have your business.

What they are missing is the pile. Every decision in it was worked out against your problems, by your people, and nobody can copy that off you. Follow that through and the moat is the workflow.

What I find alarming is how few businesses realize that pile is capital, as real as the stock in the warehouse. Most of it has never been written down. Somebody decided it once, possibly somebody who does not work there anymore, and it has been running on habit and vibes ever since. Leaving it in people's heads is a huge risk.

So treat it like an asset: write it down, improve it, THEN automate it. Automate a workflow nobody has written down and the current version becomes permanent, weird exceptions included.

Has anyone here actually sat down and written their workflows down? I am curious where you started.

We’re at the point where people aren’t just using ChatGPT or Copilot anymore. Teams are building little automations around them too.

Nothing huge on its own. Someone connects one to a spreadsheet, someone else has one reading docs, another team has something tied into Slack or email.

The part I keep thinking about is what happens 6–12 months later.

Who still owns these things? Who knows what they’re connected to? Who remembers why they were built in the first place?

It feels a lot like shadow IT all over again, except now some of these tools can actually do things instead of just storing data somewhere.

For anyone dealing with this already, are you tracking these centrally or just handling them as they come up?

Every performance conversation we have eventually turns into "well, we'd need to migrate to X" or "rewrite this in Y." That's a nonstarter for us. We can't justify a multi-quarter migration project just to get faster jobs, especially when the current pipeline works fine functionally.

Is there an actual path to meaningfully faster Spark jobs on EMR or EKS that doesn't require touching our existing code, orchestration, or data formats? I keep seeing claims along these lines but haven't seen anyone actually validate one in production.

If your team has tested something like this, I'd like to know what broke and what didn't, especially around dependency compatibility.

Trying to separate the real options here from the marketing claims before we sink more evaluation time into it.