AI due diligence in PE deals: why data readiness belongs in the investment committee

The thesis is written. The foundation has not been checked.

Walk into any IC meeting this year and you will find AI somewhere in the value-creation plan. Faster pricing decisions, automated reporting, predictive churn models, margin improvement through operational intelligence. The ambition is real and, in many cases, the budget is already allocated. What is rarely on the table is a defensible answer to the question that determines whether any of it works: can this company’s data actually carry the plan?

That is not a technical question. It is a capital allocation question, and it belongs in the investment committee.

The readiness gap is not closing

The evidence points one way. Only 8% of businesses say their data is fully ready to support AI at scale (Dun & Bradstreet AI Momentum survey of 10,000 businesses, November 2026). AI use is widespread. Enterprise value from it is not.

This is not early-adopter growing pains. It is the current state of the market. The gap between AI ambition and AI-ready data is wide, and it is not closing fast enough to be ignored at the point of investment.

Why AI amplifies the problem rather than solving it

There is a persistent belief in the market that AI will surface and correct data problems over time. The opposite is closer to the truth. AI does not fix a shaky data foundation. It amplifies it. Errors that sat quietly in one system get distributed across every downstream decision, with the implied authority of a model behind them. Garbage in, garbage amplified.

The specific failure point is almost never inside a single system. A CRM that is internally consistent, an ERP that balances, an HRIS that reconciles: each can look healthy in isolation. The gaps that break AI projects live between those systems, in the joins, the mismatched identifiers, the fields that mean different things in different tools. That is where the model gets confused, where the output becomes unreliable, and where the value-creation thesis quietly stalls.

Plenty of enterprises have deployed AI agents before their data was ready. The consequence is not just wasted budget. It is decisions made on outputs that look authoritative and are not.

What this means for the investment committee

The IC is already comfortable asking hard questions about revenue quality, customer concentration, and management depth. Data readiness deserves the same rigour, for three reasons that map directly to value and carry.

Capital at risk before the build starts

Committing AI budget to a portfolio company without knowing whether the data can support it is a structural bet, not an informed one. You would not start a renovation without a structural survey. The same logic applies here. The cost of discovering the foundation is broken after the capital is in is always higher than the cost of finding out before.

The exit multiple depends on it

Weak data and KPI reporting is one of the biggest finance issues at exit. Many firms cannot accurately reflect their value-creation initiatives in reported EBITDA, which compresses exit value when a buyer’s diligence team cannot verify the numbers. If the AI-driven margin improvement is real but cannot be traced cleanly through the data, a sophisticated buyer will haircut it. The carry impact is direct.

The spread between companies that execute on AI and those that do not is real and growing. The question for the IC is which side of that spread a given portfolio company is actually positioned to land on, not which side the management deck says it will land on.

Regulatory pressure is building

For any portfolio company that operates in or sells into the EU, the EU AI Act brings data governance and documentation obligations, heaviest for higher-risk AI systems. Companies that have not mapped their data lineage face compliance exposure that will surface in the next diligence cycle. Getting ahead of it is straightforward. Getting caught behind it is not.

The case for an independent read

Every major technology vendor now ships some version of a readiness score. The problem is structural: a vendor score always points towards buying more of that vendor. A green score inside one platform is not readiness. It is one system telling you it is happy with itself.

What the IC needs is an independent read across the whole stack, one that scores the gaps between systems rather than the health of any single one, and produces a number that can sit in a deck alongside twelve other portfolio companies on a single comparable scale.

That is what DataTruth is: the independent check on AI investments, commissioned before AI capital is committed. A Truth Score Read compares exports from two or more systems record by record and returns a signed memo within 5 working days of complete exports: the Truth Score™ (a score out of 100 for how far the data can be trusted), a Leverability read on the AI in question (which AI ideas you can use now, after fixes, or not at all), the cross-system red flags and a short fix list. The AI Investment Review goes further, testing every AI bet in the plan, with a remediation roadmap and a board readout. It is the survey before the build.

The memo is written to be understood by an IC member, a CFO, or a CEO without any data-engineering background. The Truth Score is a score out of 100 for how far the data can be trusted, banded A (80 and above, AI ready), B (60 and above, Minor issues), C (40 and above, Significant work needed) or D (below 40, Foundation needed). It is comparable across companies and defensible to a buyer’s diligence team at exit. Clarity before capital.

The practical ask

If AI is in the value-creation plan and the IC has not seen an independent read on the data, the question is not whether to commission one. It is how quickly it can be done before the next capital decision.

To talk it through for one company or across a portfolio, start with a 30-minute call. No vendor agenda, just a straight answer on where to start.