Executive Summary

AI has moved from a talking point in the investment committee to a line item in the diligence checklist. The question worth asking about any target is no longer whether AI matters to the business. It is whether the business is positioned to benefit from AI, or exposed to being disrupted by it, and increasingly, whether it is both.

EY's Private Equity Pulse for the first half of 2026 shows this shift happening in real time. Sixty percent of PE firms report increased diligence specifically on AI disruption risk, sixty four percent report increased overall selectivity, and technology's share of global PE deployment by value fell from roughly 30% in 2025 to just 12% in the first quarter of 2026. Capital is not avoiding AI exposure. It is pricing it more carefully.

For Independent Sponsors evaluating lower middle market targets, this means AI diligence can no longer be a single question about whether a company "uses AI." It has to be two separate questions with two separate answers.

Two Different Questions, Not One

The first question is whether the target is AI ready: does it have the data infrastructure, systems, and processes in place to actually deploy AI tools and see a return. The second is whether the target is AI exposed: does its business model, cost structure, or competitive position face disruption from AI adopted by competitors, customers, or new entrants.

A company can be AI ready and still AI exposed. A company can also look unaffected by AI today and be highly exposed within a single hold period. Bain's 2026 Global M&A Report, based on a survey of roughly 300 senior M&A executives, found that AI tool usage in M&A more than doubled year over year, with a third of firms now using it systematically rather than experimentally. That pace of adoption on the buy side is a signal of how quickly the underlying businesses themselves are being reshaped.

The Gap Between Adoption and Integration

Enthusiasm is not the same as capability. FTI Consulting's 2026 survey of 200 fund and operating leaders found that only 36% of respondents say portfolio companies actually use AI in day to day operations, and just 7% describe it as fully integrated. That gap between stated interest and operational reality is worth probing directly in diligence rather than taking at face value.

A separate industry analysis found a majority of practitioners currently rate AI as ineffective for both deal sourcing and portfolio monitoring, and points to infrastructure gaps as a leading contributor. Firms and sponsors who address that data layer put themselves in a better position to capture value as tools mature, though infrastructure is one input among several, including management capability and sector dynamics.

Why Data and Systems Are a Major Factor in Readiness

The gap between AI ambition and AI results often comes down to data quality. RSM's Middle Market AI Survey found that executives cite data quality as the largest barrier to AI adoption, ahead of security, privacy, and legacy systems. A recent midyear industry analysis distilled this into a practical readiness framework: clean data and a modern, connected infrastructure layer are among the conditions that most consistently separate initiatives that work from those that stall.

This has direct diligence implications. A target with fragmented spreadsheets, disconnected systems, and inconsistent records is likely to need real investment before AI can create value, whatever management's stated plans. That is not the only variable that determines outcomes, but it is one diligence can actually verify, which makes it a natural starting point.

What This Means for Independent Sponsor Diligence

For Independent Sponsors, this translates into concrete diligence questions that go beyond the standard financial and operational review. What systems generate the company's core data, and how clean and connected are they. Where is the business exposed to AI-enabled competitors or AI-driven customer behavior change. Where could AI plausibly improve margin or growth within a realistic hold period, and what would it cost to get there.

These questions matter regardless of deal size. EY's data shows increased AI disruption diligence and increased selectivity moving together, which means capital providers are already applying this lens whether or not a sponsor asks the question first.

What Actually Matters Now

The sponsors best positioned for this shift are treating documentation and process as a competitive advantage rather than a compliance burden. Clear diligence records, defined conflict protocols, and transparent reporting are becoming table stakes for accessing the capital that is now flowing into the space.

1. Diligence AI readiness and AI exposure as two distinct questions, not one.
2. Treat data quality and system connectivity as a meaningful diligence item, not a post-close operating concern.
3. Assess disruption risk from AI-enabled competitors and customers, not just the target's own AI usage.
4. Size any AI-related value creation plan against realistic infrastructure costs, not vendor promises.

Data and systems are not the whole story of whether a company benefits from AI, but they are one of the few parts of that story diligence can actually test before close.

For Independent Sponsors, that makes AI diligence less about the technology itself and more about a question private equity has always asked in one form or another: is this business actually built to execute on what it says it can do.

‍

Sources:

EY, Private Equity Pulse, Q1 2026 and H1 2026
Bain & Company, Global M&A Report 2026
Bain & Company, 2026 Midyear Private Equity Report
RSM, Middle Market AI Survey (2026)
FTI Consulting, 2026 survey of fund and operating leaders
PwC, Global M&A Industry Trends: 2026 Mid-Year Outlook