Executives don't need a shorter version of staff AI training. They need to make good calls on decisions that are expensive to reverse. Here's what that training should cover.
TL;DR
Executive AI training often gets designed as a shorter version of what staff receive. A bit of background, a few prompting tips, a demo, done. That's useful for about an hour, and then the leaders are back to making decisions about AI with no more clarity than before.
What leaders need most is the ability to make good calls on things that are expensive to reverse. This piece covers what I think executive AI training should include, the formats available, and how to judge whether it worked.
McKinsey's Superagency in the workplace research makes a pointed claim: employees are largely ready for AI, and the bigger barrier to scaling it is leaders who aren't steering fast enough. Whatever you think of the framing, it matches a pattern worth taking seriously. Teams often run ahead of their leaders, using AI informally while waiting for direction that doesn't come.
There's also the question of modelling. Teams take their cue from what leaders do, more than from what they say. A managing director who uses AI visibly, talks about where it helped and where it didn't, and sets clear rules gives the organisation permission to engage. One who delegates the whole topic to IT sends the opposite signal.
At Praxes we look at AI readiness across five dimensions, and I'd use the same structure for executive training, plus governance.
Strategy. Where AI creates value in this specific business, and where it doesn't. Which of the organisation's priorities AI can help with, and which ones it won't touch. AI isn't a strategy on its own, and leaders need to be able to say no to good-looking ideas that don't serve the plan.
Data and systems. What the organisation's systems can actually give a model. Many AI ambitions stall because the data is scattered, inconsistent, or locked in tools that don't connect. Leaders don't need to understand the engineering, but they do need to ask the right questions about it.
People. How adoption happens, why training alone rarely changes behaviour, and what leaders must do differently for change to stick. This is where most programmes succeed or fail.
Process. Which workflows should change and who owns redesigning them. A tool that isn't built into the work tends to become another tab nobody opens.
Tooling and vendors. How to evaluate platforms, what lock-in looks like, and which decisions compound. Choosing a platform is easy to do and expensive to undo.
Governance and risk. Data protection, confidentiality, acceptable use, and accountability for AI-assisted decisions. If the organisation operates in Europe, this includes the EU AI Act. The European Commission's AI literacy guidance is a good short read for any leadership team using AI tools. Our guide to building an AI governance framework covers the practical side.
None of that strategic material lands well unless leaders use the tools themselves. I'd always include time for executives to work on their own material: a board pack, a strategy document, a competitor's annual report, a difficult email to a stakeholder.
Two things happen when they do. They see what the tools are good at, which shifts the conversation from abstract to concrete. And they see where the tools fall short, which builds the healthy scepticism they need when a vendor promises too much. MIT Sloan Management Review has explored what AI still can't do for leaders, and that discussion is useful context for a leadership team forming its own view.
| Format | Best for | Trade-off |
|---|---|---|
| Free short courses | A quick shared baseline | Generic, with no link to your business |
| University executive education | Individual leaders wanting depth and a credential | Costly per person, and taken alone rather than as a team |
| In-house leadership session | A leadership team making decisions together | Needs preparation and a facilitator who knows the business |
Free short courses are a sensible start. IBM offers a free three-hour AI course for executives covering what generative AI is and how it creates business value. Assign one as pre-work so the whole team starts from the same baseline.
University executive programmes go deeper. MIT Sloan's AI executive courses range from an on-demand generative AI sprint of around six hours to multi-week programmes on enterprise AI strategy, with tuition from under $2,500 to over $10,000. They suit an individual leader who wants structured depth, but they don't produce a shared plan for your organisation.
An in-house leadership session is the format I'd choose for a leadership team. It uses your strategy, your data situation, and your people as the material, and it ends with decisions rather than notes. The trade-off is that it needs preparation and someone who can be honest about what the organisation is ready for.
Here's how I'd structure one. Start with 45 minutes of hands-on use on each leader's own material. Then spend an hour on where AI could create value in the business, using the strategy and a short list of candidate use cases prepared beforehand.
After a break, take 45 minutes on readiness: what the data, systems, and people situation means for those use cases. Then 30 minutes on governance and risk. Finish with 30 minutes of decisions: which one or two use cases to pursue first, who owns them, what the rules are, and what the leadership team will do visibly in the next month.
That last block is what separates a leadership session from a briefing. If nobody leaves with a decision and a name next to it, it was a briefing.
I'd use these questions as the test. Where are the two or three places AI creates the most value for us, and why those? What's stopping us, in data, systems, or people? What are our rules for AI use, and who owns them? Which tools are approved, and how will we decide on the next one? What will each of us do differently, so the organisation can see we mean it?
If the team can answer those clearly, the training worked. If it can't, the next step is a proper readiness assessment rather than another course.
Delegating the topic to IT. IT can run the infrastructure, but the decisions about value, people, and risk belong with the business. Many mid-sized organisations have no one whose job is to think about AI properly, which is the gap an outsourced Chief AI Officer is designed to fill.
Training leaders only alongside their teams. Leaders should attend their team AI workshop, because their presence signals that it matters. But they also need a separate session where they can ask basic questions and debate decisions without an audience.
Ending without decisions. Awareness on its own fades quickly. A leadership session should close with commitments that someone will check on in a month.
It depends on the goal. A free short course builds a shared baseline, a university programme gives an individual leader depth, and an in-house session helps a leadership team make decisions about its own business together.
Common uses include summarising long reports, preparing for meetings, drafting difficult communications, testing arguments in a strategy document, and analysing competitors' public information. The key is checking output before relying on it.
Enough to use the tools well on their own work. Leaders don't need to become experts, but hands-on use builds the judgement they need when evaluating proposals and vendors.
A half-day in-house session, with a short course as pre-work, gives a leadership team enough to make its first decisions. Follow-up sessions every few months keep it current.
Usually whichever approved, enterprise-grade tool the organisation already licenses, because that's where its data protections apply. The bigger question is how leaders use it.
If you want a clear view of where your organisation stands before your leadership session, our free AI Readiness Diagnostic scores you across the five dimensions above and gives you a report to bring to the table.
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