How ready is your company to run AI at scale?

1. We keep a register of AI use cases, each with a named owner.

2. Every AI use case gets a risk rating before it goes live, and the evidence is kept.

3. We know which regulations and standards apply to our AI use, such as the EU AI Act or ISO/IEC 42001.

4. Our AI priorities are written down and tied to business outcomes, not to technologies.

5. We have decided, explicitly, what we will not use AI for.

6. AI investment is reviewed against results at least every quarter.

7. There is an agreed way to build AI solutions: models, data access, agents and integration.

8. New AI projects reuse shared components instead of starting from zero.

9. Data that AI systems use has clear owners and access rules.

10. People know which AI tools are approved, and for which jobs.

11. When we find shadow AI use, there is an approved alternative to offer.

12. The cost of AI tools and model usage is visible per team or per use case.

13. A team can go from idea to a governed pilot in days, not months.

14. Logging, evaluation and deployment come pre-wired in our starter templates.

15. AI outputs are tested automatically every time a prompt, model or tool changes.

16. Each role has a learning path, from executives to frontline staff.

17. Managers actively encourage their teams to experiment with AI.

18. We measure adoption and capability, not just course completions.

19. People know where to find AI guidance, tools and help.

20. There is one clear way to propose a new AI use case.

21. Leaders talk openly and regularly about what AI is, and is not, for in our company.