How I help leaders think clearly about AI

Most AI failures are decision failures, not model failures.

My approach is structured, practical, and designed to cut through the noise.

01

Clarify the business problem

  • What decision or workflow needs improvement?
  • Who owns the outcome?
  • What happens if nothing changes?
  • If the problem isn't painful or frequent, AI is unnecessary.
Outcome: Root cause identification.
02

Decide if AI is actually required

  • What decision or workflow needs improvement?
  • Who owns the outcome?
  • What happens if nothing changes?
  • If the problem isn't painful or frequent, AI is unnecessary.
Outcome: Root cause identification.
03

Review existing AI (if present)

  • What decision or workflow needs improvement?
  • Who owns the outcome?
  • What happens if nothing changes?
  • If the problem isn't painful or frequent, AI is unnecessary.
Outcome: Root cause identification.
04

Define success criteria

  • What decision or workflow needs improvement?
  • Who owns the outcome?
  • What happens if nothing changes?
  • If the problem isn't painful or frequent, AI is unnecessary.
Outcome: Root cause identification.
05

Clear recommendation

  • What decision or workflow needs improvement?
  • Who owns the outcome?
  • What happens if nothing changes?
  • If the problem isn't painful or frequent, AI is unnecessary.
Outcome: Root cause identification.

Ready to clear the fog?

If you need an unbiased partner to review your decisions and direction, let's talk.

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