AI Governance > AI Hype: The Simple Playbook

AI is moving fast. New rules are landing, tools are everywhere, and every company feels pressure to do something with AI. The problem: many projects never leave the pilot stage, or they collapse because nobody owns the risks.

Here is a simple playbook you can use to cut through the noise. Three words: Strategy. Ownership. Roadmap. If you cover these bases, you are ahead of most.

Strategy

This is the “why.” Why are you using AI, and how do you know if it is worth it?

• Business goals: What outcome do you want? Save time? Increase revenue? Improve service? Pick a few and be specific.
• Risk policy: Decide what not to do (for example: no sensitive data, always human review for high-stakes decisions).
• KPIs: Choose a few ways to measure success (like percent of customer requests solved, time saved, error rate).

Without a clear “why,” AI projects drift into hype.

Ownership

This is the “who.” Who is responsible for keeping things on track?

• RACI: Spell out who is Responsible, Accountable, Consulted, and Informed for each project.
• Data stewards: Someone needs to care about clean, accurate, legal data.
• Model owners: Someone must be in charge of the AI system itself, keeping it updated and safe.

If nobody owns it, it will fail quietly.

Roadmap

This is the “how.” How do you go from small test to something that scales?

• Pilot -> Scale: Start small, prove it works, then expand. Do not try to “boil the ocean” on day one.
• AI operations: Keep an eye on performance, quality, and safety over time.
• Feedback loops: Ask users what is working, log errors, and improve.

Think of it like gardening: plant small, tend it, grow bigger.

What has changed recently

• New laws: The EU AI Act and UK safety frameworks are now in motion. Even if you are not in Europe, these rules will influence global standards.
• Standards: ISO has published AI governance guidelines, useful templates for structure.
• Best practice: Companies are learning to treat AI governance as part of delivery, not as a side project.

Simple tools that work

You do not need 50 page binders. A few lightweight tools go a long way:

• One page use case sheet: What is the goal, what data do we use, who owns it, how do we measure success?
• Change log: A simple record of tweaks to models or prompts so nothing mysteriously changes.
• Test set: A list of tricky questions or scenarios to check before each launch.
• Ops sheet: Who to call when something breaks, and how to roll back quickly.

Avoid these traps

• Running pilots with no way to measure if they worked.
• Forgetting to assign data owners.
• No plan to roll back if things go wrong.
• Treating compliance as an afterthought, fixing later is expensive.

A 30-60-90 day starter plan

• 30 days: Pick two use cases, write one page charters, and decide success measures.
• 60 days: Run a pilot with a small group, check weekly, and practice an undo drill.
• 90 days: Expand to a wider team, add user feedback, and run a simple internal review.

Final word

AI governance is not about slowing things down. It is about making sure your projects work in the real world, stay safe, and keep improving. Simple structure beats hype every time.

Strategy. Ownership. Roadmap. If you remember nothing else, remember that.