A few weeks ago, in these pages I argued that AI is repricing enterprise software faster than most vendors want to admit. Since then, the sharpest pushback I have gotten from peer CIOs has not been about the pricing thesis. It has been about the leaders navigating it. What does this shift actually ask of the people leading their organizations through it?
The honest answer, from where I sit, is uncomfortable. AI is the first enterprise technology in a generation where the leader’s personal experience of the tools has become part of the leadership instrument itself. Most senior IT leaders, including many I speak with regularly, have not yet caught up to what that means. For most of my career, my model for leading technology change was familiar: read deeply, talking to peers, pressure-test with my team, communicate direction, drive execution. That model does not work for AI. I did not figure this out because I was smarter than my peers. I figured it out because I stopped talking about AI and started using it, and the difference in my own judgment surprised me.
The credibility gap most IT leaders don’t see in themselves
The data is more revealing than the conventional commentary suggests. Gallup’s Q4 2025 workplace research found that frequent AI use among leaders had reached 44%, up from 17% in mid-2023. That sounds like progress. But 56% of senior leaders still do not use AI frequently in their own work. And frequent use does not necessarily mean sustained, real-stakes practice with the tools. More than half of the people setting enterprise AI direction are doing so from a distance.
Grant Thornton’s 2026 AI Impact Survey makes the problem visible from a different angle. Of 950 senior business leaders surveyed across ten industries, 78% reported they lacked confidence they could pass an independent AI governance audit within ninety days. The leaders setting direction on AI cannot, by their own admission, explain how their AI decisions get made or who is accountable for the outcomes. Articulation has run ahead of practice across most of the executive population.
I see the same pattern at closer range. In peer CIO conversations, on conference panels and in executive committee discussions inside other organizations, I keep meeting senior leaders who are the most articulate strategic voices on AI in their companies but have not personally used AI in their own work. They have read about it. They have been briefed. They have approved budgets. They have given speeches. They have not lived with it.
I call this the AI credibility gap. It runs from the CEO suite through the C-level and into mid-management. The failure mode it produces is specific: leaders talk fluently about AI strategy without being able to engage with the realities their teams encounter daily. The teams notice. They stop bringing real problems forward because the conversations skim the surface. They stop trusting prioritization because it does not reflect what they are actually experiencing. They start working around leadership rather than with it.
The credibility gap is not a knowledge problem. The leaders involved are intelligent and motivated. It is an experience problem, and experience cannot be briefed.
Why this shift is different from the ones ITDMs have led before
A reasonable objection: senior IT leaders have managed major technology transitions for decades without becoming hands-on practitioners. CIOs led cloud transformations without writing infrastructure-as-code. CFOs led ERP implementations without configuring modules. Why is AI different?
Three things have changed. AI tools are designed for direct human use in a way enterprise infrastructure never was, which means a leader who has not used them is unfamiliar not just with a technology but with a new mode of knowledge work. Second, AI capability changes faster than any leader’s briefing cycle can keep up with, so leaders working from quarterly briefings operate with a perpetually stale model of what the technology can and cannot do. Third, and hardest to communicate to leaders who have not lived it, AI works probabilistically. Knowing when to trust an output, when to verify, when to push back, when to escalate: these judgments accumulate through hours of personal use, the way clinical judgment accumulates in a physician. A leader who has not done that accumulation is asking their teams to do it instead, and to make the resulting calls without leadership cover.
Personal practice, in other words, is now a prerequisite for AI leadership rather than a complement to it.
What actually changed when I started building with the tools
I noticed the credibility gap in myself before I saw it in anyone else. Several months ago, I decided to stop talking about AI as a topic and start using it as a tool. Not the demo-and-show-off way most executives engage with AI, with a Copilot prompt here and a ChatGPT query there, but as a daily instrument in the actual work I do. Drafting strategy documents. Stress-testing arguments before taking them to the leadership team. Working through analysis I would previously have outsourced.
At one point I went further. Coming from a product and supply chain background, I built an inventory contextual model using AI, a working tool rather than a slide, to think through supply, demand, inventory levels, cash flow and downstream customer impact. I did this not because my team could not have built it, but because I wanted to live inside the problem myself. The act of building taught me more about AI’s strengths and limits in a few weeks than two years of vendor demos had. I saw where the model held up under real data, where it broke, where the judgment of an experienced operator was still load-bearing, and where AI genuinely extended what a human alone could see.
The change in my leadership was not what I expected. The efficiency was real but turned out to be the least interesting part. What changed was my judgment. I started understanding what these tools are genuinely good at, where the failure modes hide and where the value sits underneath the marketing layer. That judgment changed how I prioritize AI investments, which vendor demos I find credible, how I push back on enthusiastic recommendations from my own teams, and most importantly, how I talk to my organization about AI. The conversations moved from compliance to engagement. We started moving faster, not because I pushed harder, but because the team trusted the direction more.
You cannot direct an organization’s AI transformation with conviction if your own working life has not been transformed by it.
The advice that actually matters: Pick the work that scares you
If I could give one piece of advice to a peer IT leader trying to close their own credibility gap, it would be the opposite of what most AI-leadership pieces say.
The instinct of senior IT leaders is to start using AI on the parts of the job that are already routine. First-draft emails. Meeting summaries. Scheduling. The parts where the risk feel low and the productivity lift feels visible. That instinct is wrong. Routine work produces routine learning. It gives you exposure to the tools but not to the judgment that changes how you lead.
The judgment that matters develops when AI is sitting next to you at the work where your professional identity is most exposed. The analysis you used to outsource to consultants. The strategy memo where your reputation is on the line. The problem you privately believed only you could solve. That is the work that changes you, because it is the work where you must grapple honestly with what the tool can do that you cannot, and where you can still see clearly what you can do that the tool cannot.
This is uncomfortable for a senior leader. It should be. If you use AI only in the safe parts of your job, you are protecting your professional identity from the encounter that would actually update it. You get to keep believing the tool is a nice supplement to what you already know how to do. If you use AI on the parts of your job where your expertise is the whole point of your seat, the encounter is different. You find out where your judgment still holds. You find out where it does not. You find out how the tool and your expertise combine into something neither could produce alone. That is the learning that changes how you lead.
This is where the ITDM instinct gets in the way most. Many CIOs and IT leaders I speak with have started using AI in IT operations, which feels like home territory and where the productivity gains are visible. That is fine, but it is not where the credibility gap lives. The gap lies in strategic decision-making, board-level analysis, cross-functional trade-off calls and the judgment work leaders were promoted for being good at. Those are the areas where most leaders have never used it.
So, the question I would put to any IT leader reading this: what is the work you are best known for? The work you would not want anyone else to touch? That is exactly the work you should be doing with AI, this month, before you write the next AI strategy document your organization asks you for.
The stakes
The personal practice of the leader, more than strategy or budget or governance, is going to determine whether organizations succeed or struggle with AI transformation. Strategy without lived experience produces hollow direction. Budget without lived experience produces misallocated investment. Governance without lived experience produces over-correction or under-correction depending on which fear is loudest in the room.
The IT leaders I see doing this work quietly, on their own time, with their hands on the tools, are the ones I expect to define the next decade of enterprise transformation. The ones who keep articulating without practicing will find themselves increasingly disconnected from the organizations they lead. The teams will move on. The strategy will drift. And the leaders will not understand why, because the gap they have created is invisible from the seat they sit in. The question is not whether AI will reshape your organization. It will. The question is whether you will reshape yourself first, enough to lead the transformation rather than narrate it.