Technology has moved from the sidelines to the center of the table for middle-market M&A.
Ten years ago, diligence teams gave IT a polite nod. Today, technology can make or break the investment thesis. Whether it’s supporting bolt-on acquisitions, enabling automation, or accelerating time-to-value post-close, the digital backbone is now a core driver of private equity outcomes.
This shift is reshaping how sponsors underwrite deals. As we explored in Technology Assets: Private Equity’s Next Frontier for Value Creation, technology is increasingly treated as a value-creation lever, not just a cost center.
But recognizing tech’s importance is only step one. The bigger question is: How do you assess it rigorously, efficiently, and with commercial clarity, especially in a world racing toward AI?
Tech Diligence: The Old Model vs. What’s Needed Now
Traditional technology due diligence asks:
- Are the systems running?
- Is the security baseline adequate?
- Are there any obvious gaps?
These are valid questions. But they’re rooted in a defensive posture—focused on downside protection, not strategic potential.
Today’s M&A environment demands more. Sponsors must be able to evaluate how technology capabilities, or limitations, will impact integration timelines, digital transformation goals, or the ability to scale AI responsibly. It’s not about inspecting current state; it’s about building an agile foundation for the anticipated future.
Bain & Company has noted that modern technology due diligence must “translate technical issues into financial insights,” helping deal teams understand how infrastructure, platforms, and data strategy will directly influence value realization (Bain).
That requires a shift in approach—from audit-style checklists to a more nuanced, hypothesis-driven evaluation model.
Why AI Raises the Bar
AI is changing the game, especially in the middle market. The promise is real: predictive analytics, intelligent automation, better decision-making at scale.
But with promise comes pressure. Companies rushing to adopt AI without the technical and ethical foundations in place face real risk—reputational, regulatory, and operational.
In our article, Before the Bots: Laying the Groundwork for Responsible AI Adoption, we outline what responsible adoption really requires:
- Clean, well-governed data
- Secure, scalable cloud infrastructure
- Model oversight and explainability
- Cross-functional coordination between tech, legal, ops, and leadership
Technology due diligence must now evaluate whether a target company has (or can reasonably build) those capabilities. It’s more than spotting gaps, it’s estimating the cost, time, and complexity of closing them.
A Better Framework for Technology Due Diligence
Modern diligence—especially in AI-conscious M&A—requires a more integrated, thesis-aligned approach. Sponsors need to move beyond IT hygiene and develop a structured way to assess technology as a growth enabler.
Here’s what that looks like in practice:
1. Link Tech Capabilities to the Deal Thesis
Every deal has a story. The role of diligence is to assess whether the tech stack supports that story. Is this a roll-up play? Then platform extensibility matters. A data monetization thesis? Then architecture and analytics maturity are key.
This framing keeps diligence focused and financially relevant.
2. Assess Technical Debt—and Its Implications
Legacy systems, outdated ERPs, or ad hoc integrations can slow growth and balloon post-close costs. But not all tech debt is deal-breaking. What matters is understanding where it is, how much it costs to remediate, and what it limits going forward.
A clear inventory of technical liabilities lets deal teams adjust valuations and timelines accordingly.
3. Evaluate Data and Infrastructure for AI-Readiness
Even if AI isn’t part of Day One, it’s likely in the roadmap. Sponsors need to understand whether the company has the ingredients in place:
- Structured, accessible data
- Cloud-native architecture
- Secure and compliant environments
- Organizational readiness for change
This helps avoid costly rebuilds and misaligned expectations later.
4. Consider Integration Complexity Early
Tech is often the bottleneck in post-close integration. Diligence should proactively assess how systems will or won’t fit together. That includes APIs, data models, cloud compatibility, and vendor lock-ins.
Sponsors who surface integration risks early can build more realistic synergy models and transition plans.
5. Don’t Just Flag Risks. Quantify Them
Good diligence doesn’t just say “there’s a problem.” It says:
- This issue will delay rollout by six months
- It will cost $1.2M to modernize
- The lack of data governance reduces AI feasibility by 60%
Anchoring findings in business impact helps deal teams make smarter decisions—and gives operating partners a running start.
Diligence That Looks Forward
Technology has become a core pillar of deal value. But unlocking that value requires more than gut instinct or surface checks. It calls for a clear, strategic view of how a company’s digital capabilities will enable—or constrain—the investment thesis.
Done well, technology due diligence can help sponsors spot hidden value, avoid costly missteps, and prepare for a future where AI, automation, and agility are table stakes.
Done poorly, it leaves blind spots that can surface months too late.
To future-proof your next investment, start by asking better questions. Tech diligence isn’t just about minimizing risk. It’s about maximizing confidence.
