AI Strategy
Most AI strategies are a list of tools.
Yours should be a list of decisions.
If you’re being told “We need AI” and your response is “What for?” then this is for you and whoever is asking for AI.
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Your board wants to know what you are doing about AI. Your team has already started using it, with or without permission. A vendor has told you their product now has AI in it, and you cannot tell whether that is a feature or a liability.
Meanwhile the honest answer, that some of this will be useful and some of it will not, is hard to say in a board paper.
An AI strategy is not a technology plan. It is a set of decisions about where you will use AI, where you will not, what you need in place before you do, and how you will know if it is working.
Where AI would genuinely help your organisation, ranked by value and effort.
Just as importantly, where it would not, so you can stop being sold it.
What your people and your vendors are already running.
This is almost always more than leadership expects, and it is the thing auditors ask about first.
A short, plain-English strategy document your directors can read in ten minutes and make decisions from. Not a technology roadmap.
What you need in place before the first real deployment: policy, oversight, skills, data readiness, contracts. In order, with effort estimates.
One use case scoped tightly enough to actually start, chosen because it will teach you something whether or not it succeeds.
It is probably not for you if you already know exactly what you want to build and just need it built. That is a delivery problem, not a strategy one, and you would be paying me to agree with you.
Most people who start here go on to AI Governance or ISO 42001. If you want to see how I think before committing, the AI Governance Assessment Pack covers a lot of the same ground.
Tell me what you're facing. You'll get a straight answer on whether this is the right piece of work, and what it would involve.
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