Most leaders making AI decisions are not AI experts, and they do not need to be. Leaders make decisions about finance, law and technology every day without being accountants, lawyers or engineers. The same applies to AI. What you need is a clear decision framework, good questions, small tests that reduce uncertainty, and advisers you can hold to account.
A simple framework for AI decisions
For any AI decision, work through five questions:
- Value: what problem does this solve, and how much does it matter?
- Feasibility: can it realistically work with our people, data and systems?
- Risk: what could go wrong, and how bad would it be?
- Cost: what is the full cost, including people's time?
- Reversibility: how easily can we stop or change course?
Decisions that are valuable, feasible, low-risk and easily reversible can be made quickly. Decisions that are high-risk or hard to reverse deserve more scrutiny.
Use small tests to reduce uncertainty
When you are unsure, do not guess. Test:
- Try it yourself on a real task before deciding.
- Run a short pilot with clear success criteria.
- Trial a tool with real users before buying.
A few weeks of testing usually reveals more than months of debate.
Ask good questions of experts
You do not need to know the answers; you need to ask questions that reveal whether advisers do:
- What evidence supports this recommendation?
- What are the main risks, and how would we manage them?
- What would make you change your recommendation?
- What are the alternatives, including doing nothing or something simpler?
- How will we know if it is working?
Vague answers to these questions are a warning sign, whatever the adviser's credentials.
Build enough practical understanding
While you do not need expert knowledge, you do need practical familiarity. Leaders who have used AI tools on their own work make noticeably better decisions: they recognise realistic claims, understand limitations and can judge advice. A few weeks of hands-on use and structured learning is usually enough.
Watch for common decision traps
- Fear of missing out: rushing into AI because competitors are.
- Authority bias: accepting advice because of who gives it.
- Sunk cost: continuing failed initiatives because of past investment.
- Analysis paralysis: waiting for certainty that never comes.
Decisions that should not wait for expertise
Some AI decisions are urgent regardless of your expertise level:
- Setting basic rules on data and approved tools.
- Appointing an owner.
- Training at least one manager.
These are low-risk, reversible and protective.
For more on making sound AI judgements, see how to choose between AI solutions and strategic questions to ask about AI.
Our Leading AI Innovation & Organisational Transformation course gives non-technical leaders the practical understanding and frameworks needed to make confident AI decisions.
Frequently asked questions
Can a non-expert really judge AI proposals?
Yes, with a clear framework, good questions and some hands-on experience. Many of the best AI decisions are business judgements, not technical ones.
When should I bring in an AI expert?
For technical design, complex integration or specialist risk questions. Keep the business decision with you, informed by their advice.
How do I avoid being misled by AI vendors?
Insist on trials with your own data and users, ask for customer references and judge results against clear success criteria.