AI Leadership Principles / Strategy

What should our AI strategy be, and where do we focus?

Start from your business strategy and the problems that matter most, not from the technology. Choose a few areas where the value is large, redesign them end to end, decide where AI should drive growth as well as efficiency, and be explicit about what you will stop doing.

Why it matters now

The gap between companies that get real value from AI and those that do not is widening. The difference is rarely access to better models. It is focus: a few important problems, worked through properly, rather than a long list of tools bolted onto the old way of working. Because the technology moves so fast, I treat the strategy as something to revisit every quarter, not a document to file.

Common mistakes

  • Bolting AI tools onto the existing business model instead of rethinking the work.
  • Spreading the budget across many small initiatives so none gets enough attention.
  • Choosing projects because they are visible, not because they are valuable.
  • Writing a strategy once and not revisiting it as the technology changes.

What to do instead

  1. Start from the problems. List the business problems that matter most: a delay, a cost, a quality issue, an unmet customer need. Then ask where AI can help.
  2. Pick a few domains. Choose a small number of areas where the value is large, and redesign each one end to end.
  3. Decide where to grow. Name at least one place where AI should help the business grow, not only save cost.
  4. Decide what to stop. A strategy is also a list of things you will not do. Say it out loud so budget and attention follow.
  5. Revisit every quarter. Review the priorities each quarter against what you have learned and what the technology can now do.

Your first step this week

With your executive team, list your five most important business problems for the next year. For each, ask one question: could AI change how we solve this? Keep the two strongest answers.

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