AI for
Private Equity.

Not advice. Decision leverage. Paul Okhrem is a Prague-based AI decision consultant and fractional Chief AI Officer (CAIO) advising private equity firms, PE-backed CEOs, and portfolio operators on the AI decisions that move enterprise value. PE partners and operating partners hire Paul to argue against the next major AI thesis before it goes to the IC, the LP letter, or the post-close 100-day plan — portfolio-wide AI strategy, vendor commitment, value-creation bets, AI in M&A diligence, roll-up integration. The asymmetry: most PE-facing AI consultants advise on theses they have never had to defend in their own P&L. Paul has two decades of B2B software operating experience and two production-AI software companies under his ownership, generating roughly 30% operational efficiency gains across both. The work in private equity focuses on AI as a value-creation lever — what to bet on at the platform level, what to push down to portfolio CEOs, what to defend at the next exit.

Engagements run vendor-neutral. No platform partner margin, no integrator referral fee, no model-provider sponsorship. Outcomes validated under The Proof Standard™ — the published five-component measurement protocol with client-side validation.

AI is now the most consequential value-creation lever a PE deal team will name in 2026 — and the most over-claimed. LPs are asking for the AI thesis line in the deck. Operating partners are being asked to ship AI-driven margin expansion in the 100-day plan. Portfolio CEOs are being told the platform AI strategy is “already decided” without knowing who decided it or what was committed.

The risk is symmetric. Under-investing in AI at the platform level surrenders the productivity arbitrage that makes the thesis underwritable at exit. Over-investing in vendor commitments before the use cases are stress-tested locks the portfolio into multi-year licensing the next acquirer will not pay for. The decisions that distinguish the two outcomes are made in 2026 — and they are decisions a PE firm rarely has the in-house operator depth to validate independently.

• AI claim validation — what the target’s AI story actually defends in production. Differentiated capability versus reskinned vendor stack. Defensible to the next acquirer two to five years out, not just at close.
• Vendor stack & switching cost — named platforms, named model providers, multi-year licensing exposure. Switching cost in dollars and engineering weeks. Lock-in named pre-close, not assumed.
• Talent risk read — key engineering and AI leadership concentration risk. Retention scenarios. The AI org under stress — what survives a compensation reset, what doesn’t.
• Post-close integration architecture — what AI capabilities consolidate at the platform level, what stays at the portfolio company. Operating-partner playbook, not vendor pitch.
• 100-day AI sequence — the AI plan for days 1–100 post-close. Vendor commits to make or rescind, named owners, milestone gates with measurable thresholds.
• Margin expansion thesis — where AI agents in operations, finance ops, customer ops, and engineering velocity create the 20–40% efficiency gains underwritten in the deal model. Numbers tied to the P&L, not to vendor logos.
• Exit-readiness implications — the AI architecture and contracts the strategic acquirer will assume, will renegotiate, or will reject. Surfaced at close, not at exit.

• The platform-vs-portfolio confusion. Pushing AI decisions down to portfolio CEOs that should be made at the platform level — or vice versa. Both are expensive in different ways.
• Vendor lock-in priced into the deal. Multi-year AI licensing committed pre-close that an acquirer will not assume. Switching cost named, not assumed.
• Generic AI maturity assessments. Frameworks built for SaaS adoption, applied to a manufacturing platform, generating recommendations no one will action. Sector-fit beats template prestige.
• Over-claiming AI in the LP letter. The AI section of the LP update written for narrative, not for what the CFO can defend in a follow-on raise. Numbers tied to the P&L, not to vendor logos.
• The integrator-led AI strategy. The platform AI thesis written by the integrator who will bill against it. Independence is structural, not aspirational.

Recent work under NDA with PE firms, operating partners, and PE-backed CEOs has covered: pre-close AI diligence on a B2B software platform target including vendor stack analysis and 100-day AI roadmap; portfolio AI thesis development for a multi-asset operating partner; exit-readiness AI memo for a CEO heading into a competitive process; roll-up AI integration architecture for a platform acquiring its third add-on. Engagement scope is typically 100–200 hours per engagement, fixed-fee, with the deliverable a decision memo defensible inside the IC, the LP letter, or the buyer’s diligence room.

Specific identifiers (firm names, fund vintages, target identities) remain under NDA. Methodology, baseline construction, validation method, and the decision artifact format are openly published under The Proof Standard™.