February 6, 2026
Between 50% and 70% of acquisitions fail to deliver their stated strategic or financial objectives. That figure, drawn from Bain & Company's 2026 Global Private Equity Report, has barely moved in a decade. Deals are better structured, markets are more competitive, and diligence processes are more elaborate than ever, yet the failure rate holds.
The reason is not financial modelling. It is not commercial diligence. It is the consistent underestimation of technology as a core valuation driver.
The uncomfortable reality: many deal teams are still treating technical due diligence as a late-stage risk filter rather than a discipline that should shape underwriting, pricing, and the post-close value-creation roadmap from the outset.
We make the case for a different approach, one that is increasingly the standard among the best-performing PE firms and that turns tech DD from a checkbox into a genuine competitive advantage.
Technology companies now account for 31% of all buyouts globally, and the share of "tech-enabled" acquisitions, businesses where software is the primary revenue or margin driver, has grown substantially over the past five years. Yet Bain's research found that only a small minority of buyers perform comprehensive, tech-specific diligence.
This gap exists because many investors still view technology as a back-room cost centre. That framing is a relic of a pre-digital economy. In almost every sector today, technology is the primary driver of EBITDA growth and the gatekeeper of scalability. A business that looks compelling on a revenue and margin basis can be fundamentally uninvestable once its architecture, technical debt, and engineering capability are properly assessed.
"Best-in-class tech due diligence combines a structured traditional process with much deeper technology, AI, cybersecurity, and regulatory scrutiny." — Bain & Company
The practical implication is straightforward: if you are not performing dedicated technical diligence on a technology-enabled acquisition, you are not underwriting the business. You are underwriting the pitch deck.
The nature of the risk has also shifted. According to EY's 2025 PE Trends report, deal teams are under pressure to move faster, with mid-market diligence timelines compressing from six to eight weeks down to approximately ten to fourteen days in AI-assisted processes. Speed creates pressure to cut corners, and the first corner to go is usually the technical workstream.
At the same time, 33% of deal teams now cite operational improvements as the primary driver of their equity story, nearly double the 20% who prioritise buy-and-build strategies. That shift makes technology capability central to the investment thesis, not peripheral to it.
Most investors assume technical diligence is about finding bad code. It is not. It is about determining whether the target's technology foundation can support the growth trajectory embedded in your investment thesis.
Technical debt is a hidden lien on future cash flow. Left unquantified during diligence, it surfaces post-close as margin erosion, unplanned capital expenditure, and delayed product roadmaps. PwC's Deals Trends 2025 identifies scalability, IP ownership, and regulatory compliance as the core technology risk categories that must be documented before any valuation is finalised.
The following patterns are not merely technical concerns. Each one has a direct, quantifiable impact on post-close performance:
The distinction between a scalable platform and a fragile one rarely appears in the financials. It appears in the codebase, the deployment history, and the engineering team's actual working practices, none of which a financial model will surface.
Quantifying technical debt during diligence does more than protect against downside. It creates pricing leverage. A credible assessment of remediation costs gives the buyer a defensible basis for price adjustment, escrow structuring, or deferred consideration. Sellers who have not done this work themselves are at a structural disadvantage in the negotiation. The cost of poor-quality software is rarely visible until after close. Getting ahead of it during diligence is one of the clearest ways to protect deal economics.
Security findings are rarely priced into initial financials. Yet a single incident, a ransomware breach, a regulatory enforcement action, an undisclosed customer data exposure, can pause an acquisition, trigger indemnity claims, and permanently damage customer trust.
Cybersecurity, AI risk and regulatory compliance are the areas requiring deepest scrutiny in modern tech DD. The reason is simple: the cost of discovering a security problem before close is a rounding error compared to the cost of inheriting it after.
Knowledge concentration is one of the most under-assessed risks in software acquisitions. It occurs when critical system logic, undocumented workarounds, or architectural decisions exist only in the memory of one or two engineers. If those individuals leave post-close, the buyer inherits a system they cannot maintain, extend, or integrate.
This risk does not appear in interviews. Engineering leaders will describe well-documented, collaborative codebases with confidence, and often believe what they are saying. The evidence is in the repository.
A git history review reveals what a leadership interview conceals. Commit patterns show whether contributions are distributed across the team or concentrated in a single author. Code review activity shows whether standards are enforced or aspirational. Deployment frequency shows whether the team can ship reliably or whether releases are high-risk events.
Effective technology delivery benchmarking uses exactly these signals to assess engineering team health objectively. In a diligence context, the same data provides a defensible, evidence-based view of key-person risk that no amount of management interviews can replicate. Retention provisions, earnouts, and knowledge transfer milestones can all be structured around documented findings from this analysis. But only if the analysis was done before the term sheet was signed.
Every pitch deck in 2026 claims a differentiated AI strategy. Most of them are describing a product roadmap aspiration, not an operational reality. Distinguishing between the two is now a distinct and non-negotiable workstream in any technology-heavy acquisition. EY's analysis of AI in M&A identifies two separate questions that must be answered:
The NIST AI Risk Management Framework is emerging as the standard reference for assessing AI governance in diligence contexts, covering transparency, accountability, and model risk. Buyers who do not assess this are acquiring AI exposure they cannot price.
The question to ask in every AI-related diligence: is this capability improving margins today, or is it a feature designed to optimise the exit narrative? The answer changes the valuation conversation entirely.
The most significant shift in how leading PE firms approach technical diligence is not methodological. It is about what happens to the findings.
A traditional tech DD report is filed. A value-creation-oriented tech DD report becomes the operating agenda for the first 90 days of ownership. The two approaches produce very different outcomes.
The expectation is now that diligence outputs provide actionable insights from Day 1 of ownership, moving beyond static risk documentation to dynamic execution priorities. That means the deal team's technical findings must be handed directly to the operating partner and the incoming CTO or CIO as a prioritised work programme, not archived as pre-close evidence.
This is where technology delivery performance improvement becomes directly relevant to deal outcomes. The diligence process identifies the gaps. The post-close programme closes them. The connection between the two is what separates investors who create value from those who simply acquire it and hope.
The question is no longer whether you can afford to do deep technical diligence. It is whether you can afford to close a deal without it.
A 30-minute call is usually enough to know whether a Delivery 360 would be useful — and what it would look at in your situation.
