Browse Dr. Joe Shepherd's writing on AI governance, PE operations, board advisory, and product leadership.
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Most AI board reports describe enthusiasm and pilots. This template forces management to show how AI labor is changing operating metrics, who owns the outcomes, and what decisions the board now needs to make.
Read →When Microsoft licensing shifted to monthly SaaS, our revenue line fell off a cliff. We rebuilt a $1.2B business by redesigning how we attached services to software. The same structural break is happening again as AI compresses SaaS economics.
Read →The smartest AI diligence questions aren't about the model. They're about whether the portfolio company can absorb AI as labor, govern it, and tie it to accountable operating results on the clock that matters.
Read →If AI is delegated labor, the org chart has to show where that labor sits, who supervises it, and how managers are held accountable for blended output. Most companies aren't even drawing that picture yet.
Read →The trust tier model lets AI governance scale with real-world consequence, so low-risk digital labor moves fast while high-risk systems get the control they deserve.
Read →The documentation and controls required for AI diligence are built over time. If AI is part of your value story, starting when the banker calls is already late.
Read →Non-linear careers look messy from the inside and obvious in hindsight. The operating advantage is the pattern recognition they create across systems, not the novelty of the path itself.
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