Getting executives to participate meaningfully in AI training is one of the hardest challenges L&D teams face right now. These are busy people who’ve succeeded without AI expertise and often believe their role is to direct AI initiatives, not understand them technically.

But executives who don’t grasp AI fundamentals make poor decisions about AI investments, set unrealistic expectations, and struggle to evaluate what their teams tell them.

Here’s how to design programs that actually engage senior leaders.

Why Traditional Training Fails

I’ve watched executives check out of AI training within 15 minutes. The pattern is predictable:

• Training starts with definitions and history (“What is machine learning…”)
• Executives think “I don’t need to know this, I have people for this”
• They mentally disengage while appearing to pay attention
• Nothing changes in how they make decisions

The content isn’t wrong—it’s just aimed at the wrong level. Executives don’t need to know how AI works. They need to know what it means for their decisions.

Reframing the Value Proposition

Stop calling it “AI training.” Frame it as:

• “Strategic decision-making in an AI-enabled environment”
• “Evaluating AI investments and vendor claims”
• “Leading AI-augmented teams”

The word “training” itself can trigger resistance. Executives train others; they don’t see themselves as needing to be trained.

Program Design Principles

Principle 1: Start With Their Decisions, Not Technology

Map the program to decisions executives actually make:

• Should we invest in this AI capability?
• Is this vendor telling us the truth about what’s possible?
• How should we structure our AI team?
• What’s a reasonable timeline for AI projects?
• When should we build versus buy?

Each session should address one or more of these decision types. Technology concepts appear only when needed to inform decisions.

Principle 2: Use Peer Discussion, Not Lecture

Executives learn well from each other. Structured peer discussions work better than expert presentations.

Format that works:

1. Brief case presentation (10-15 minutes)
2. Small group discussion of the decision involved (20-25 minutes)
3. Facilitator synthesis of key principles (10-15 minutes)

The facilitator’s role is to inject AI-relevant information when it helps the discussion, not to teach a predetermined curriculum.

Principle 3: Bring Real Stakes

Use actual decisions facing your organisation, anonymised if needed. Hypothetical cases feel like academic exercises. Real decisions focus attention.

“Our operations team is proposing a $1.2M investment in predictive maintenance. Here’s what they’ve told the board. What questions should you be asking?”

Principle 4: Make It Brief and Repeated

Two-hour sessions monthly work better than full-day workshops quarterly. Executives can block two hours; full days rarely survive calendar pressures.

Ongoing engagement also allows learning to compound. Each session builds on previous discussions.

Content Architecture

Session 1: What AI Can and Can’t Do

Focus: Setting realistic expectations

Key outcomes:

• Understanding the difference between AI demonstrations and production reality
• Recognising common vendor overstatements
• Knowing what questions to ask about AI project feasibility

Case example: An AI pilot that impressed in demo but failed in production. What went wrong? What should leadership have asked earlier?

Session 2: Evaluating AI Business Cases

Focus: Investment decision-making

Key outcomes:

• Reading AI project proposals critically
• Understanding total cost of ownership (not just software)
• Recognising hidden implementation complexity

Case example: Compare two AI investment proposals. Which is more realistic? Why?

Session 3: Data as Foundation

Focus: Understanding data requirements without technical detail

Key outcomes:

• Knowing what “we have the data” actually means (and doesn’t mean)
• Recognising data quality as a major project risk
• Understanding privacy and governance implications

Case example: A project that stalled because data wasn’t actually ready. What should due diligence have revealed?

Session 4: Leading AI-Augmented Teams

Focus: People and change management

Key outcomes:

• Setting expectations for AI adoption timelines
• Understanding workforce concerns and how to address them
• Creating conditions for AI tools to actually get used

This session often resonates most. Executives understand people leadership; connecting AI to their existing expertise builds confidence.

Measuring Success

Track:

• Decision quality: Are AI investments performing better? Are unrealistic projects getting killed earlier?
• Question sophistication: Are executives asking better questions about AI proposals?
• Engagement with AI strategy: Are they participating meaningfully in AI-related discussions?

Don’t track:

• Quiz scores or knowledge tests (irrelevant for executives)
• Satisfaction ratings alone (pleasant sessions don’t necessarily change behaviour)

External Input

Sometimes internal L&D teams lack credibility with executives on AI topics. Consider bringing in:

• External facilitators with executive experience
• Industry peers who’ve navigated similar decisions
• Board directors from AI-forward companies

Australian Institute of Company Directors has been developing resources specifically for board-level AI understanding, which can supplement internal programs.

Common Mistakes to Avoid

Mistake 1: Technical depth Executives don’t need to understand neural network architecture. They need to understand why AI projects take longer than expected.

Mistake 2: Tool training Teaching executives to use ChatGPT is rarely the point. They have assistants who can do that. Focus on judgement, not operation.

Mistake 3: One-size-fits-all A CEO needs different things than a CFO or COO. Tailor examples and discussions to role-specific decisions.

Mistake 4: Assuming interest Most executives are sceptical about AI training. You need to earn their engagement by demonstrating relevance quickly.

Starting Point

Begin with your CEO or equivalent. If they’re engaged, other executives will follow. If they dismiss it, the program will struggle regardless of quality.

Propose a 90-minute session framed around a real AI decision facing the organisation. Make it discussion-based, not presentation-based. See what resonates, then build from there.

Executive AI fluency isn’t optional anymore. But getting there requires meeting leaders where they are, not where L&D wishes they were.