A few months ago, an IT director at a 2,000-person company looked at me on Zoom and said, "I have no idea how I became the person responsible for this." His CEO had told him to make sure everyone gets AI training by Q2. He had three engineers, no curriculum, and a Slack channel full of people asking how to write better ChatGPT prompts.
He's not alone.
A recent Zapier survey of more than 500 enterprise C-suite executives and decision-makers makes that pattern visible in numbers. 77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations.
That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones.
I've spent enough time around AI rollouts to know why this happens. When a new technology is unfamiliar, leaders default to whoever can get it to work. That's usually IT. But there's a quiet assumption in that handoff: knowing how a tool works and teaching a workforce to use it are the same skill. They aren't. Knowing the API and knowing how adults learn a new way of working are different disciplines, and treating them as interchangeable is how you get rooms full of people who watched the demo and still don't use the tool a month later.
The Shelf Life Problem
The other thing the survey surfaced is speed. Among the 78% of leaders who name a barrier to building AI skills, the most common one (18%) is that AI is changing fast enough to make training obsolete almost as soon as it's built. Anyone who has tried to write internal documentation for a model that ships a new version every six weeks knows the feeling.
This compounds the IT ownership problem. If you're responsible for training and the curriculum is out of date by the time it's reviewed, you fall back on what you can sustain: a wiki page, an office hours channel, maybe a recorded webinar that ages poorly. None of that builds real fluency. It builds a small group of confident users and a much larger group of people who quietly stop trying.
What Works Better than Another Curriculum
In the work I do, I get to watch how teams actually learn AI in their daily work. The patterns that hold up under real conditions don't look like training programs. They look like changes to how people already work.
A few things consistently help.
Start from friction, not features. Most rollouts begin with someone explaining what a tool can do. The teams that get further start by asking where work feels slow, repetitive, or mentally draining. AI introduced as relief from a real annoyance gets traction. AI introduced as a capability tour does not.
Embed the learning where the work already happens. A short demo at the start of a weekly standup beats a separate workshop almost every time. So does walking through how someone used AI to prepare for a decision in a project retro, or sharing a rough draft and the prompts behind it in a team channel. The signal these rituals send is that AI is part of how the team thinks, not a separate skill to certify in.
Make imperfect normal. Everyone has tried to use an AI tool, gotten a moderate result, and quietly stopped using it. They worry about looking like they don't know what they're doing. But when a manager shows their team the rough draft AI helped them write, complete with the clunky parts they're still fixing, it tells everyone else that practicing in public is fine. That practice is what actually builds judgement.
Invest in champions, not heroes. Effective adoption rarely hinges on one expert. It depends on a small group of people close to the work who are willing to learn out loud and help others. They don't need deep technical backgrounds. They need credibility with peers and permission to experiment. Over time, they become translators between what's possible and what's actually useful for their teams.
The Piece It Can't Carry Alone
None of this contradicts IT's role. IT teams have to make sure AI tools are deployed responsibly, the data flowing into them is governed, and the systems they touch stay reliable. That's plenty. What IT can't reasonably also do is design and run the human side of adoption, especially in functions IT doesn't sit inside every day.
The survey hints at why so many leaders feel stuck. About three-quarters of them say they're confident their organization already has the right people and skills to hit their AI goals. That confidence is hard to square with the gaps in the same data: unclear ownership, training that goes stale in weeks, employees with tools and no guidance. Something has to give.
The version that works, in my experience, is the one where IT, L&D, and the functions actually using AI build the program together. IT brings the systems view. L&D brings the way adults actually learn. The teams using AI bring the friction that makes the training matter. None of those three can do the job on their own, and the moment one of them is asked to, the program starts to wobble.
If you're the person who keeps getting handed this, it's worth saying out loud: this is a design problem the IT team can't solve alone. Name the gap. Pull in the people whose job is teaching adults to learn. Start with one team, one real friction point, one working example. That's how this actually gets built.