// Topic

AI Strategy

AI strategy is not a board-slide category. It is the set of technical and organizational choices that decide whether AI work improves margin, reduces risk, or increases throughput.

This hub focuses on the operating questions: what to fund, what to stop funding, how to measure progress, and how to keep architecture decisions connected to business outcomes.

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Decision Criteria

Strong AI strategy answers four questions before implementation starts:

  1. Which workflow changes if the system works?
  2. Which owner is accountable for quality after launch?
  3. Which cost or risk metric proves the investment is working?
  4. Which fallback keeps the business running when the model path degrades?

If those answers are missing, the work is still experimentation. That can be fine, but it should be funded and measured as experimentation.

Practical Reading Paths

For budget decisions:

For organization design:

For technical execution:

Failure Modes

  • Funding AI initiatives because competitors announced something similar.
  • Treating vendor selection as strategy while ignoring data readiness and workflow ownership.
  • Reporting activity metrics instead of margin, risk, speed, quality, or throughput.
  • Letting every team build isolated AI tooling without shared evaluation and governance.

References

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