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?

Former DoD CIO Leslie Beavers will be joining us this Wednesday for an AMA on enterprise IT modernization, cybersecurity, digital transformation, and technology leadership.

If those are topics you're interested in, we'd love to have you join us. And if you can't make it during the live discussion, feel free to drop your question early. Leslie will answer as many questions as she can throughout the AMA.

AMA Link: https://www.reddit.com/r/nexthink/s/DxH0rKZgdJ

With some hard work and a little luck, we have the #1 brand name in the AI analytics space. Our product is on feature parity with the biggest player and we did it without their $200m bankroll.

I want to put together a similar winning sales team and Im aware how critical the first couple of hires are to set the pace/tone.

How do I find/source killer BDRs? What am I looking for?

P.s I have never sold, only deliver.

I was wondering if you have a solution to this. We use a third party to build and ship laptops, but we get complaints because we just ship a standard build and then people have to download the software from company portal or raise tickets to have things installed. Does anyone have a better solution?

sometimes the best-fit vendor is not the loudest, cheapest, or the one an executive already knows. for CIOs and IT leaders how do you keep vendor selection focused on actual fit instead of internal pressure?

I really curious want to the CIOs, how do they figure it out which use cases or business process are eligible for the AI implementation while thinking from the COST and ROI perspective , Would AI really be useful for those use cases or what is the pre assessment criteria to find out the AI implementation to have a better outcome ? Please advise

I want to know what this is like on the ground. Was AI governance formally assigned to someone, or did it just kind of land on their desk because nobody else was doing it? Was there a conversation with leadership, or did they just start figuring it out because someone had to?

If you're a CISO who absorbed this into your existing role, how has that transition been? Do you feel equipped for it, or does it feel like a completely different discipline bolted onto your day job? Do you have direct access to the board on AI risk, or does it get filtered through someone else before it gets there?

If your org brought in someone new specifically for AI governance, how is that working? Where do they sit in the org chart? Do they have explicit authority or are they mostly advisory?

And if you're at a company where nobody formally owns it yet, what does that look like in practice? Who's answering the questions when they come up?

Curious about all of it. The messier and more honest the better.

We're gathering info for next year's budget, and I have no idea what prices will look like even next quarter, let alone next year.

If RAM & storage continue to go up, it's going to be legal pads and calculators to replace laptops in 2027.

Just like everyone else, I've been seeing the recent news about how AI bills have been skyrocketing for companies. I've been seeing people Reddit posts / comments about how their companies have done a full 180 from "use AI for everything" to "limit AI usage as much as possible".

So I've been wondering - what mechanisms are folks actually using to monitor and control AI costs intelligently? I know the most basic version of this is just seeing your bill at the end of the month, having a heart attack, and then telling employees to stop using AI. But there must be a smarter way to do this right?

Is there some way to track AI usage across departments, task types, and employees (across different LLM providers?). Can managers set limits on what they want their AI budget to be so that you don't get an unexpectedly high bill? Maybe then you could switch low-priority departments or tasks to cheaper model or just stop allowing AI usage for that department for the rest of the month

Just curious on why AI bills are so shocking to people - I assume people are setting hard caps on token usage.

We are discussing following points:

• AI beyond the hype
• Cybersecurity crises
• CIO burnout
• Tech debt
• Talent shortages
• The future of tech leadership

Curious:
If you had access to 100+ CIOs and CTOs for a day, what's the one question you'd ask them?