As the volume of middle-market private equity M&A deals in the U.S. grows, deals are increasingly technology-focused: software M&A alone reached 18% of U.S. PE deal value last year. For the tech-enabled services, healthcare IT, and vertical SaaS businesses prominent in lower middle market investment theses, technology is the load-bearing wall for a majority of these acquisitions.

Yet walk into a sub-$100 million deal and ask who is assessing that wall. Too often, the answer is nobody, or a networked former CTO parachuted in days before close with a two-week window and a checklist. The commercial and financial work streams get institutional processes. The technology workstream gets whatever fits in the budget that remains.

This is not a failure of discipline. It is arithmetic.

The Arithmetic that Forces Skip-or-Skim

The few firms that publish technology diligence pricing put a standard engagement at $25,000-$40,000, with an expanded one at $40,000-$75,000, with practitioner benchmarks for full PE-grade work running to $150,000. On the financial side, a Big Four quality of earnings report reportedly costs $30,000-$50,000 even on a $5 million acquisition, and the largest firms may decline smaller engagements outright.

Now put those numbers against the deals. GF Data shows $10–25 million enterprise value deals trading at 5.9x EBITDA in 2025. That is a company earning roughly $2 million–$4 million a year. A $100,000 technology diligence is 3-5% of the target’s annual EBITDA.

No deal team can justify that line item, so the workstream gets compressed, delegated, or dropped. The pricing was never designed to scale down: it prices rationally for a $500 million platform and irrationally for a $40 million one. Providers built for the top of the market, and the majority learned to make do.

The Bill Arrives After Close

The cost of thin diligence has been documented for a decade. As far back as 2016, a West Monroe and Mergermarket survey found 40% of acquirers had discovered a cybersecurity problem only after closing, citing insufficient diligence time and too few qualified people. A 2019 Forescout study of nearly 2,800 IT decision makers found 53% had hit a critical cybersecurity issue that jeopardized a deal.

When these misses are priced publicly, they are not small: In 2017, Verizon cut its Yahoo purchase price by $350 million after breaches surfaced post-signing.

The quieter version is the broken operating model. McKinsey estimates technical debt at 20-40% of the value of a company’s entire technology estate, and its research places 50-60 % of post-merger synergy initiatives in IT-dependent territory. Buy without understanding either number, and you have bought a different company than the one in the model.

Why This Matters More Below $250 million

Diligence quality is worth more per dollar in the middle market, not less. Pantheon’s analysis of Preqin data across 2006–2020 vintages found midmarket buyout funds delivered a higher median net IRR than large-cap funds (19.3% 13.5%) but with far wider dispersion: roughly 24% for the top quartile against 9% for the bottom.

Wide dispersion means an inefficient market, which is exactly where returns come from and exactly where a missed technology finding does the most damage. In a clustered large-cap market, thin diligence costs basis points. In the middle market, it can move a fund between quartiles.

A Better Standard: Systematic, Sector-Specific, and Expert-Reviewed

The way out is not cheaper artisanal work or faster document processing. It is a different production model, built on three principles.

First, orient every work stream around the investment thesis. A diligence should test the deal’s specific assumptions, not inventory the target’s documents. If the thesis is “buy and integrate three regional platforms,” the technology questions concern integration surface and data compatibility. The best acquirers already invert this: PwC found 60% of successful acquirers now plan the operating model before diligence begins, up from 25% in 2019. When every finding maps to a thesis assumption, the report becomes the first draft of the value creation plan instead of a risk memo that dies at close.

Second, build the evaluation sector by sector, not deal by deal. A healthcare-IT platform and a logistics SaaS business fail in different ways. Generalist diligence treats every deal as a blank page, which is why quality depends on which partner shows up. Sector-specific evaluation frameworks encode the known failure modes and benchmarks, and they compound: every deal in a sector makes the next assessment sharper, and that judgment stays in the system instead of retiring with a partner. This is also what makes rigor affordable.

Third, put domain experts at the end of the pipeline, not the start. Systems should assemble evidence; operators should judge it. The scarce resource is the judgment of someone who has run engineering or product in the target’s sector, and the economics only work when that person reviews structured, evidence-linked findings rather than spending billable weeks in a data room. Expert review is also the honesty check on automation: AI-generated findings are fluent, and fluency is not accuracy. Every finding should trace to its source document, page, and passage, so the deal team can verify rather than trust.

Five Questions for Any Technology Diligence Report

1. Can every material finding be traced to a specific source: a document, a repository, an interview?
2. Which assumption of our investment thesis does each work stream test, and what does the scope leave untested?
3. What sector-specific benchmarks were the findings evaluated against, and where did those benchmarks come from?
4. Who with real operating experience in this sector reviewed the findings, and what did they change?
5. Which findings convert into 100-day-plan items with owners, and which are priced into the bid?

The lower middle market is where technology theses are won or lost, and it deserves the rigor the largest deals take for granted. The firms that close this gap first will not just avoid bad deals. They will underwrite good ones with a confidence competitors cannot match.

Dev Sahoo has spent 18 years in engineering and strategy consulting, including roles at EY-Parthenon, Deloitte, AWS, and Western Union, conducting more than 75 diligence and value creation engagements for private equity and enterprise buyers and sellers. He is the founder of PearlWiz AI, a due diligence platform for lower middle market private equity. Dev is a member of ACG Austin/San Antonio.

The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the views, opinions, or official position of ACG.

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