Practical perspectives on AI strategy, PE due diligence, healthcare AI, and governance — from someone who has run AI functions at scale and is accountable for outcomes, not opinions.
A 40-point diagnostic covering data infrastructure, talent capability, governance maturity, risk posture, and operating model readiness. Used in buy-side PE diligence and internal AI audits for healthcare payers, providers, and financial services organizations.
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Most PE due diligence on AI-enabled healthcare businesses still focuses on the technology stack. The real risk is in the operating model — whether AI is actually embedded in workflows or just running alongside them.
Read on LinkedIn →The standard diligence checklist wasn't built for AI. Here's what experienced AI operators look for that most financial due diligence processes miss — from data governance to model drift to human oversight gaps.
Read on LinkedIn →AI trust isn't built with policies. It's built with architecture — verifiable outputs, human oversight at the right points, and governance that's wired into operational workflows rather than documented in a PDF.
Read on LinkedIn →Healthcare AI targets carry risk profiles that don't show up in standard tech diligence. Clinical workflow dependency, regulatory exposure, model bias in care settings, and data fragmentation all require specialized assessment frameworks.
Read on LinkedIn →Revenue cycle is where AI generates measurable, auditable, near-term cash impact in healthcare portfolios. Most PE operators aren't extracting it because they're looking at the wrong metrics.
Read on LinkedIn →The threat isn't that AI replaces clinical workflows. It's that AI-native competitors will systematically disintermediate organizations that are still running on manual processes while calling it a "pilot program."
Read on LinkedIn →A structured methodology for quantifying AI's financial impact across margin expansion, workflow productivity, and cost reduction — built for PE operators and CFOs who need to move beyond "AI improves efficiency" to auditable ROI statements.
View on LinkedIn →AI capabilities are increasingly in the deal thesis — but they're rarely assessed with the same rigor as financial or legal due diligence. Here's what a serious AI diligence process actually looks like.
Read on LinkedIn →A 40-point diagnostic covering data infrastructure, talent, governance, risk posture, and operating model maturity. Used in buy-side diligence and enterprise AI audits across healthcare and financial services.
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