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 Okhrem to pressure-test 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. Paul Okhrem's relevant public record is B2B software operating experience since 2009 and active leadership roles across two software companies using AI in production. That is first-party operating context, not evidence of a portfolio-company outcome; private-equity buyers should verify mandate-specific references and diligence evidence. 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 commission-free with related-party options disclosed. No platform partner margin, no integrator referral fee, no model-provider sponsorship. Engagement measurement follows 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, customer operations, and engineering are tested against the efficiency assumptions underwritten in the deal model. Tie every hypothesis to portfolio-company-controlled P&L data.
• 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. Commercial incentives are structural and should be disclosed.