The AI Diligence Gap Is Real. Here's How to Close It..
AI diligence sits in an awkward gap between commercial and technical assessment. Here's how we've built a structured programme to give investors a clear, comparable view of AI readiness and vulnerability across their portfolios.
Last week we sponsored and attended the Private Equity Wire European Summit, and one panel stuck with me more than any other. "Rethinking Portfolio Monitoring" brought together Nathan Atkinson from Cinven, Michaela Campbell from Hayfin, Mittu Sridhara from Clayton, Dubilier & Rice, and Navpreet Mann from ICG. It was a sharp, honest conversation about how AI is changing the way firms monitor and create value across their portfolios — and about where the gaps still are.
Everyone Agrees AI Matters. Few Have a Systematic Way to Assess It.
Cinven has moved to a use-case-centric approach to AI, with dedicated resource behind adoption and change management. "This can't be a side-of-desk task," as Nathan Atkinson put it. Over at CD&R, Mittu Sridhara talked about mapping AI opportunity across the entire value chain of each portfolio company — front office through to back office — and then prioritising by value and ease of implementation.
Both were clear that frameworks exist, but the landscape changes so fast that the questions you were asking six months ago are already out of date. Cinven maintains an internal AI thesis that has to be updated after every investment committee because new dynamics keep emerging.
Beyond that, four themes came through clearly.
The data problem is also the data opportunity. AI-driven ingestion is solving the unstructured data intake challenge, but the real value sits on top of that — mining years of performance data for cross-portfolio insights and early warning systems that flag risks before they become red flags.
LPs want speed, accuracy and transparency. When the recent market sell-off hit, ICG's deal teams turned around detailed portfolio-level responses within 24 hours because the AI readiness work had already been done. That kind of preparedness is quickly becoming table stakes.
AI diligence sits in an awkward gap. The DD providers firms typically use don't quite answer the full AI question — it's a commercial question with technical dimensions, and the point from both Atkinson and Sridhara was that commercial, operational and technical diligence need to be tightly integrated rather than stitched together at the end.
Build vs. buy is getting harder. One firm had just purchased a dedicated contract analysis tool only to discover a general-purpose model performing at a comparable level days later. The consensus: build only where it differentiates you, and monitor everything because today's vendor may be obsolete tomorrow.
That last point on the diligence gap resonated with me because it's the exact gap we've been working to close.
Our AI Audit for PE Portfolios
We've delivered over a hundred technology assessments for PE and VC investors. Our core methodology is built around the 5Ps: Product, Process, Platform, People, and Protection. For AI, we've extended it into a structured assessment programme designed specifically for investors who need a clear, comparable view across an entire portfolio. We evaluate each company through three integrated lenses.
- AI Readiness: Where does the company actually stand? We score across seven dimensions: strategy and use-case maturity, data foundations, technology infrastructure, talent and operating model, security, financial impact and investor reporting. Every score is substantiated by direct technical evaluation from a practising CTO — not self-reported maturity questionnaires. Our partners review code, architecture and deployment pipelines directly.
- AI Vulnerability: What's the exposure? We assess business model disruption risk, competitive dynamics, customer and market sensitivity, regulatory exposure and operational risk. The vulnerability that matters most to an investor is rarely purely technical. It's whether the people in the organisation have the capability to understand, adopt and lead the changes that AI demands. Where those human capabilities are absent, even modest competitive shifts become existential.
- AI Monetisation: Where can AI create new revenue? We assess current AI-derived revenue, pricing model exposure (particularly the shift from seat-based to usage or outcome-based pricing), product-embedded AI maturity and the optimal monetisation pathway. This is the dimension that most directly informs exit narratives.
The depth of focus across these three lenses naturally varies depending on how the business uses technology and where it sits on its AI journey. A SaaS business with AI embedded in its core product needs a different depth of assessment than a services business just beginning to explore automation.
How It Works
- Phase 1 — Rapid Assessment (2-4 weeks per company): We map systems, data assets, workflows and existing AI usage, produce scored readiness and vulnerability profiles, and identify fast-win opportunities for immediate action.
- Phase 2 — Design: We co-develop a 12-month AI impact plan and a 90-day execution plan with company leadership, including quantified ROI, staffing recommendations and a board-ready summary built for investment committee discussions.
- Phase 3 — Implementation: Scoped per company based on the approved plan. We move directly from assessment to action — our partners step in as fractional CTOs, deliver hands-on AI enablement and run structured upskilling programmes. There's no handoff to a different firm and no knowledge lost in translation.
Every engagement includes a structured handover. Our success is measured by making portfolio companies stronger, not by extending our engagement.
Why This Matters Right Now
As Nathan Atkinson at Cinven put it during the panel, AI diligence is the area with the least maturity in the space. The questions are changing every six months, the traditional DD providers haven't fully caught up, and the consequences of getting it wrong are compounding.
The firms that build a systematic, comparable view of AI readiness and vulnerability across their portfolios will be the ones making better hold-period decisions, telling stronger exit narratives, and delivering the transparency that LPs increasingly expect.
Ready to close the AI diligence gap?
We'd welcome a conversation about how our AI Readiness and Vulnerability Audit can give your fund a clear view across your portfolio.