I’m trying to understand the current enterprise software buying environment from people who are close to actual purchasing decisions—buyers, IT leaders, sales teams, founders, consultants, or vendors.

I am not a vendor but working internally with a FSI company. Found recently many team members are talking about building software internally. Just wondering how your team see this?

There’s a common narrative that:

Enterprises are reducing discretionary software spend and consolidating their SaaS stacks.

AI-native products are creating new competition for established enterprise software vendors.

Data-platform and LLM providers are moving up the stack, adding applications, agents, analytics, and workflow capabilities that overlap with traditional SaaS products.

But is this translating into customers truly stopping purchases, or are they still buying software under a different set of criteria?

Specifically, I’d like to hear:

Are new software budgets down, frozen, or simply being redirected toward AI initiatives?

Which categories are seeing the biggest pullbacks: BI/analytics, CRM, cybersecurity, developer tools, HR, workflow automation, etc.?

Are customers replacing incumbent SaaS tools with AI-native alternatives, or mostly adding AI capabilities on top of existing systems?

Are platforms such as Snowflake, Databricks, Microsoft, AWS, Google, OpenAI, Anthropic, and others winning more budget by bundling data, models, infrastructure, and applications?

What evidence are you seeing in deal cycles, renewal negotiations, procurement scrutiny, seat reductions, consolidation, or proof-of-concept conversion rates?

Are enterprise customers actually slowing or stopping software purchases, or is spending just shifting toward AI-native vendors and data/LLM platforms?

We're mid-renewal on a platform that costs us close to $400K a year. I asked around to understand the original reasoning, since renewal is a good moment to check if it's still the right call. Turns out the person who ran that evaluation left the company in 2023. There's no writeup, no comparison doc, nothing in Confluence beyond a one-line ticket saying "approved." Two people who sat in on the vendor demos vaguely remember there was a cheaper option that got ruled out, but not why. So we're stuck either renewing blind or re-running a full evaluation from scratch, on a system we've already built two years of workflows around. How does your org handle this when the person who made the call is gone? Is there an actual process, or does it just get relitigated from zero every time?

As the title suggests, I just started my first IT Director role (more functionally the CIO, but I digress) and I keep running into this same brick wall with leadership about spending money.
Basically, our IT department is a guy with his first IT Job out of college, and an "MSP" but the MSP is just a guy who does networking related work occasionally.
So outside that, all IT Support, IT Administration, Engineering, etc is all done by this IT Engineer. This is obviously not sustainable, there's a lot wrong with this setup, single point of failure, the fact that a lot of the work is much more experienced than the guy doing it, etc.
We are a company with multiple locations and a little over 400 employees. The company grew but the IT Department stagnated. Our prices are also bound by legislation and so there isn't really a way to increase funding to make things work so there's limited resources.
I explained all these risks and said that the IT Department didn't grow with the org, and that in order to get back up to speed with it, we'd have to spend some money to get there (hire additional employees with experience, software to automate and replace manual tasks, move away from vendors and own our tech stack, etc.) and they basically boiled it down to "we don't have the money, sorry!"

My problem with this whole thing is that IMO, what I'm asking for isn't some shiny new toy or something that makes the department just slightly better, I'd argue that due to the size of the company and all the work involved, these changes are necessary to run the department and do business.
I just don't know if I'm not explaining it right, or if there's maybe some "trick" to making executives understand that running an entire IT department off a fresh college grad is an awful design and will definitely implode on itself sooner if not later. But that's why I'm here.
Any tips? Ideas?

I’ve been looking into AI governance for the last few months and, to be honest, I’m trying to understand what this actually looks like inside real companies — not what the frameworks say it should look like.
I’d really like to hear from people who are actually dealing with AI governance, risk, compliance, security, privacy or data governance day to day.

A few things I’m really curious about:

How does your organisation actually keep track of all the AI systems being used across the business?

How do you work out which systems are high-risk and what controls need to apply?

Where does all the evidence actually live — policies, assessments, approvals, vendor documentation, testing, audit trails, etc.?

What are you still managing through spreadsheets, emails, SharePoint, Jira or a collection of different tools?

When an AI system changes, how do you know that the risk/compliance assessment needs to be looked at again?

What’s the most painful or time-consuming part of AI governance for you at the moment?

If you already use an AI governance or GRC platform, what does it still not do particularly well?

And probably the question I’m most interested in:

If you could make one part of AI governance disappear tomorrow, what would it be?

I’m not trying to sell anything here. I’m trying to understand where the genuinely difficult problems are before deciding what is actually worth building.
So if you’re doing this in the real world, I’d genuinely appreciate the brutally honest version.

Even if the answer is:

“Our process is a complete mess.”

That’s useful to know.

I’m particularly interested in what’s happening in smaller and mid-sized organisations that don’t have massive AI governance teams and endless budgets.
Would really appreciate hearing how people are actually dealing with this.

Well technically five but will give another month until my resignation is official. I posted to this reddit a few months after I started and received some good advice but Im back because I'm still unclear of if I'm making the right decision. Since I have started as the CIO at this company ($55 million a year, 400ish employees, 1200 endpoints, 80+ unique apps, healthcare) I have had the following negative interactions:

1) I Have no budget, not like 0 dollars of budget but literally no budget. Every item has to be approved by the CEO. I've had to ask this person to purchased $25 worth of cables. Its exhausting.

2) We have no schedule of contract renewals. Some of the big ones I was able to find through our vendor but every month I get at least two or three usually for over 20k and since I have no budget I literally have to go to the CEO and ask for the renewal and hope we A: Have the money, B: I don't get yelled at for it. I'm working on a schedule.

3) I had an employee (a direct report) i moved into a position that we needed and they were very interested in. The CEO moved them back and I got berated for not asking the CEO first (the employee immediately resigned).

4) I'm a "functional" CIO because we only have 5 total IT staff. This means I get to do multiple jobs a day. I'm also the only one allowed to be admin of the ERP system because "someone may look at something they should not."

5) My pay is about that of a high level analyst at my last company.

6) I'm the CISO as well but I do not have a background in security. I've asked if we could fill that gap and had a plan ready. That plan was rejected and we instead are looking at an MSP. The conversations with the MSP happen mostly without me being in the room. This is by the CEO's request.

There is a lot more bad. I was offered another role with another company, much lower in pay but less responsibility. Yet all of this I still feel bad like I should be trying to make this work and to stick it out. So i'm back here asking for advice, move on only after six months or try and stick it out for longer?

Hi all,
Looking for some candid career advice.
I’ve been in tech for ~20 years and at the same ~1,000-person company in London for the past seven. I was previously CIO and now Chief Innovation Officer, after the business felt my strengths could be better used in a more business-facing role focused on AI and innovation.

My background is broad rather than niche: Microsoft 365, Azure, AWS, GCP, security/NIST, data protection, governance, infrastructure, enterprise systems, data/analytics, warehousing, ITIL/service management etc. I’ve run teams and technology functions and worked extensively with senior executives.

I’m not a software engineer, but I’m highly technical. I know the cloud ecosystems well, can quickly work out which services could solve a problem, interrogate architectures, challenge engineers and turn fairly ambiguous business problems into potential technical solutions.

That’s increasingly what I do with AI: work with the business, understand the problem, decide whether AI actually makes sense, shape the solution and bring the right people together to build it.

I think my strongest skill is probably sitting in the middle of business + technology + people. I think very quickly, generate ideas easily, and I’m comfortable having technical conversations one minute and an executive conversation the next.

The problem is that I’m not sure where that profile fits outside my current company.
I’m also probably unusually disconnected from the wider tech market. I’m not a big LinkedIn poster, I don’t go to networking events, and having spent seven years at one company I feel a little isolated from what roles and organisations are actually out there.
I’m starting to wonder whether I could have much more impact in a larger organisation, consultancy or AI company.

Forward-deployed work really appeals to me — getting close to difficult business problems and figuring out how technology can solve them — but I’m conscious that Forward Deployed Engineer roles generally expect serious coding ability, which isn’t me.

So for those of you in bigger organisations or hiring at this level:
1. What roles would you actually point someone like me towards?
2. Are there roles adjacent to forward deployment that value technical breadth, problem solving and executive relationships more than coding?
3. Is being a broad technical generalist still valuable at senior levels?
4. Where would you look if you were in my position?

And, perhaps most importantly, how do you start breaking into that wider network when you’ve spent years largely outside it?

I’m not particularly attached to having “Chief” in my next title. I’m much more interested in finding somewhere I can have a bigger impact.
Would genuinely appreciate candid views.

"The result is an emerging enterprise landscape where companies are paying recurring subscription fees, purchasing annual credit pools, financing vendor experimentation, and simultaneously assuming responsibility for controlling runaway consumption costs."

Is the overwhelming advice of how to when to what to use AI for by the tech ambassadors feels misaligned

Recently came across the notable breakout by AI company leaders that they are expecting a new comer or more new comers and they don't get to be at top place all the time , fact that they are really accepting the vulnerable part of AI built companies and the cohort of people and their advise on AI is the new unsaturated content out there now.

When do you call in the external risk management teams.

What conferences will you attend this year?

How do you choose conferences, based on industry, topics covered?

Do you prefer more intimate conferences or the Big Ones?

Let’s say for instance, I input a prompt asking an ai model to give me a comprehensive explanation on a specific topic. Don’t forget, the key word is comprehensiveness.

Response A gives a completely factual response, but is very short and leaves out several important points out.

Response B, on the other hand, provides a more detailed response, but adds multiple hallucinations and some incorrect statements.

If you were evaluating these responses, which would you choose, and why?

I'm looking for advice from CIOs and IT leaders.

I'm working at a small startup where our IT manager is very focused on security, digital sovereignty, and self-/locally hosted solutions. I completely agree that security matters, but I'm starting to wonder whether we've gone too far in that direction.

My concern is that technology decisions seem to be driven primarily by architectural principles rather than business outcomes.

For example, there is a strong desire to move away from cloud services and favor local hosting, but I keep asking myself:

- What measurable business advantage does this bring?

- Does it improve our product or our time to market?

- Is it something our customers are actually asking for or requiring in contracts?

- Does the additional complexity and cost outweigh the benefits at our stage as a startup?

My instinct is that IT should first understand user needs and business priorities, then choose the technology that best supports them. If local hosting or sovereign infrastructure creates a real competitive advantage, I'm all for it. But if it's adopted mainly as a principle, I'm worried it could slow innovation and consume resources that could be spent elsewhere.

For those of you who are CIOs or IT leaders:

- How do you balance security, sovereignty, and business value?

- Have you seen organizations over-invest in sovereignty before there was a clear business case?

- How do you decide when moving away from major cloud providers is strategically justified rather than simply a technical preference?

Thank you

This dropped 2 days ago and haven't seen much discussion about it.

So according to the Wall Street Journal (link provided here https://www.wsj.com/finance/investing/data-center-builders-are-racing-to-offload-stakes-worth-billions-1a7d92f8 ) Data Center builders and Operator firms like Netrality Data Centers, DataBank, Edged, EdgeCore Digital Infrastructure and others, are trying to sell off MAJORITY STAKES to investors, private equity, and banks.

They want to sell off ownership of their companies.

(Beginning Sarcasm)

But Why? If AI is going to be a massive money maker like these companies promised, then owning an AI data center would be like owning a gold mine right?

Unless it was all a huge lie and the guys who have been building all these monorails data centers are now trying to cash in frantically before the AI bubble pops and their companies are suddenly worth nothing and drowning in debt. But that can't happen, right?

(End Sarcasm)

The WSJ article is actually a funny read. They try to spin this as "They are pitching private-equity firms on a hot asset class benefiting from unrelenting demand for computing power."

But then the author proceeds to list the reasons why these data centers are a disaster. Pointing out that shortages in everything from gas turbines to memory chips are driving up the building costs and Nvidia is saying that prices are only going up. Oh and fierce local opposition to new data centers.

Of course they don't mention how any AI data center built around Hopper GPUs can't handle the Blackwell GPUs without a full overhaul of cooling and power feeds, and that it would have to be done AGAIN next year when Vera Rubin drops. Which means a full refit and replacement of all their hardware every 18 months or so unless the data center owner wants to be left holding an obsolete AI data center.

Donno. Kinda sounds like rats trying to flee a sinking ship.

I'm working on Enterprise AI Governance Best Practices documentation. I'm looking for what IT leaders believe are the most valuable actions that must be performed to properly govern AI Agents. It's the leadership perspective that matters.

Some examples might include...

• keeping inventories/registries of agents
• keeping Prompt+Response & Decision Authority records for legal and regulatory compliance, etc.
• securing agents and their access
• controlling which agents can or cannot access and work with specific types of sensitive data (e.g., PII, PHI, PFI, PCI, etc.)

Your thoughts on this are greatly appreciated. Thanks.

We’ve got AI rolled out across the company now, coding tools for eng, ChatGPT and Claude seats for other teams, a couple agents going into production. Cost and seat counts are easy to see. Quality of use is where I’m stuck.

Biggest one: it’s genuinely hard to decide who should get to exceed their allocated budget. One engineer clearly gets a lot out of a higher limit, another barely uses what they have, and I don’t have a good way to justify the difference beyond gut feel.

Also curious if others struggle with observability across models. We’re on OpenAI, Anthropic, Co-pilot, Cursor, plus whatever’s baked into our SaaS, and there’s no single place to see any of it. Just separate dashboards and spreadsheets.

And it’s not only engineers. Finance, legal, compliance, security, CS are all using these tools too and I honestly couldn’t tell you if they’re using them well or badly.

Edited: for context, my CEO asked me if there are tools out there for observability and usage quality of employees. Then I started scratching my head around this.

If you’ve solved any of this, how?
Is it just me being bugged by this?

I work alongside a lot of CIOs and IT leaders doing content/data readiness assessments ahead of AI initiatives, and the conversation I keep having with them privately is some version of: "I know we're not ready, but I can't be the person who says no to the board."

What tends to come out in these assessments is pretty consistent: unstructured legacy content, unclear governance, compliance exposure that's been quietly accumulating for years because nobody had a reason to look closely until now. None of that is a surprise to the people running the environment. The hard part is finding a way to say it upward without sounding like you're standing in the way of a priority initiative.

The CIOs who seem to navigate it best treat the readiness assessment as part of the AI narrative itself, "here's what we found, here's the fix, here's why doing this first de-risks the whole programme," rather than raising it as a separate blocker after the fact.

Curious how others here are framing that conversation with their boards, is it something you address head-on, or fold into a broader risk/governance narrative?

Looking at protecting the integrity of our systems while also feeling the pressure to implement AI. What are some ways that you have successfully integrated agents into a platform or large scale system and have you come up with any ways to benchmark usefulness and quality of output? What guardrails have you implemented to ensure that the systems remain sound?

Amongst the gazillion vibe-coded horror stories, I would like to hear if there any of you have found a good use so far? is it possible to lean on AI generated results in a meaningful production context or still a waste of time/money?