Ask a portfolio company for revenue by customer segment over the last quarter. If the company has a strong data program, the team will be able to deliver an answer within a day. In many companies, you may not see the answer for weeks because leaders have to assemble the data from disconnected systems, and the numbers don’t always agree. 

This type of data problem is an expensive, systemic issue across private equity portfolios. As AI has moved from a strategic aspiration to a central part of a firm’s underwriting thesis, a portfolio company’s data foundation has moved from a line item in technology diligence to a determinant of exit multiple. 

Forward-looking firms treat data as both a key area for diligence and a value creation instrument — something to be built deliberately during the hold period, not patched together when a buyer calls. The firms that don’t are leaving measurable money and opportunity on the table.

For deal teams, data maturity is one of the sharpest signals available for evaluating whether an AI value creation thesis is executable or aspirational. For Operating Partners, building a data foundation during the hold period directly drives the exit premium.

How data became an underwriting variable

Historically, data diligence meant verifying financial statements and checking that a company’s legacy tech stack could support basic operations. That was sufficient when the primary value creation levers were operational efficiency, revenue growth, and expansion. It is no longer sufficient in today’s market.

AI has driven this shift. Firms are now underwriting AI opportunity as a core part of the value creation thesis, identifying the specific workflows where automation, prediction, or optimization could meaningfully move EBITDA and pricing that potential into the deal valuation. This forces deal teams to pressure-test the data foundation that determines if these AI opportunities are within reach.

The AI opportunities that generate operational value — demand forecasting, pricing optimization, customer churn prediction, etc. — require clean, structured data to produce reliable outputs. A portco running on disconnected ERPs with years of inconsistent data entry isn’t AI-ready, and it will require months of remediation work to bring data and systems into a state where they will be able to feed high-value AI workflows. This isn’t a fringe problem. In a recent Catalant survey, 62.5% of practitioners identified poor data quality, silos, and fragmentation as one of the top barriers to scaling AI pilots, meaning the gap between AI thesis and reality is often simply a data gap.

Buyers are pricing with the AI-readiness gap in mind. During diligence, they’re not just asking whether a company has an AI strategy but whether the data environment can support an AI strategy. There are a variety of behavioral signals that answer that question, such as:

• How quickly a management team can produce specific metrics
• Whether numbers reconcile across systems
• Whether key definitions are consistent across functions

These signals are becoming as important as the financials themselves. A company that takes weeks to answer basic questions about its own performance is telling buyers a lot about the state of their data and their readiness for AI.

The valuation math 

The premium for AI-ready data environments is already visible. According to GF Data, which tracks multiples across thousands of private market transactions, companies with clean data and independent reporting command an average of 7.4x EBITDA, compared to 7.0x for those without — a 0.4x premium.

The financial return is straightforward. A million dollars deployed into data infrastructure during the hold period, used to build governed data products, document lineage, and establish a single source of truth for key metrics, can have significant returns at exit. Few value creation investments offer this kind of calculable return with a high degree of confidence.

The multiple isn’t about operational efficiency, though clean data enables that, too. It’s about buyer confidence. A company that can answer any diligence question in hours, trace every metric back to a source record, and demonstrate that its numbers mean the same thing to every function in the organization is a company that closes faster and gives buyers fewer reasons to doubt. Better data reduces risk, translating to a stronger price.

Deal teams are also factoring in the idea that AI-ready data environments signal opportunity. A buyer who can see that the data foundation is in place to support AI at scale is buying optionality. They’re paying for the value creation they can unlock on day one, not just the value the previous owner captured.

The hold period is where the work happens

Operating Partners generating data premiums at exit are making data foundation a first-year priority or a first-100-days priority. That urgency matters.

The companies that arrive at exit with AI-ready data environments built them because their Operating Partners understood early in the hold that data infrastructure is a prerequisite to many of the other value creation initiatives on the list. You cannot build meaningful AI on fragmented data with inconsistent data definitions. AI-enabled EBITDA improvements require a data environment that can support them.

This reframes data investments entirely. It stops being exit prep and starts being hold-period value creation with an exit premium attached. The work done to build governed, reusable data products is the same work that makes an AI value creation thesis executable and generates the multiple expansion. It’s one investment with returns at multiple stages of the deal.

One dimension that leaders consistently underestimate is the ratio of unstructured to structured data in a portco’s environment. AI models tend to perform most efficiently on structured data organized in tables that can be queried directly. Unstructured data (emails, documents, PDFs, and other information that lives outside formal data systems) typically requires more preprocessing, more compute, and more specialized expertise before AI can use it, making it more expensive to work with and, in its raw state, a less reliable basis for automated decision-making. As a result, companies whose data estates are dominated by unstructured data generally face higher costs and greater complexity when building AI systems — a challenge compounded by the fact that unstructured data now accounts for an estimated 70 – 90% of all enterprise data.

What AI-ready actually looks like

Buyers are paying for the data foundation that makes AI trustworthy and deployable. For Operating Partners doing diagnostic work on portfolio companies and deal teams evaluating targets, the question is whether data investments have produced the five pillars buyers are actively looking for:

1. Trusted definitions: Every key metric carries the same meaning across every function and report.
2. Documented lineage: Every number can be traced back directly to its source transaction.
3. Single source of truth: Data lives in standardized, governed datasets that every team can use.
4. Governed access: Data access is thoughtfully governed with role-based permissions and clean audit trails.
5. Auditable pipelines: The path from raw data to board-level metrics is fully traceable.

Companies with this level of data maturity command higher multiples and also close faster. These companies move through diligence cleanly, answer questions quickly, and don’t give buyers a reason to pause. This is worth real returns in a compressed timeline, even before other factors are taken into consideration. 

Now is the time to start

PE firms generally view data through one of two lenses: as a liability to manage during diligence or as an instrument of value creation.

Data-forward firms build data foundations into their value creation playbook from the start. They assess data maturity during diligence not just to mitigate liability but to understand how quickly they can begin executing an AI thesis post-close. They deploy data investments early, knowing that the return comes both from becoming AI-ready and from enabling AI-driven improvements. 

While perfect data environments aren’t built overnight, any firm can start making progress quickly. Start with the metrics that matter most, document where those numbers come from, and build from there. 

It takes time to create an AI-ready data environment, which is why now is the time to start.

Data foundations built during the hold period don’t just protect valuation but create it. The firms that internalize that distinction are the ones writing the new PE playbook, and the returns are showing.