Objection 1: “AI can’t understand complex codebases—only engineers can.”
That’s the traditional mindset. It assumes that human engineers manually sifting through repositories is always superior to algorithmic scanning. But here’s the reality: humans overlook patterns, miss hidden dependencies, and fatigue over time.
An AI-powered technical due diligence platform doesn’t replace engineers—it augments them. It continuously analyses version histories, architecture changes, and dependency health at scale. While a human reviewer might miss a deprecated library buried in a third-party dependency, the platform flags it instantly. It’s not that machines “understand” code like humans. It’s that they never blink while scanning every inch of it.
Objection 2: “You can’t trust AI to make judgment calls about risk.”
This assumes risk is only about judgment. But many risk indicators—like contributor churn, lack of test coverage, or security misconfigurations—are quantifiable. They’re not opinions. They’re signals.
A technical due diligence platform that’s AI-powered doesn’t decide whether to invest. It tells you, with precision, how many commits haven’t passed code review. How many production incidents trace back to one fragile module. How long it takes to deploy changes in real time. These are not guesses. They’re metrics. And when paired with human judgment, they raise red flags weeks before manual reviews would.
Objection 3: “The platform will surface too much noise—we’ll end up overwhelmed.”
This criticism assumes that signal prioritisation can’t be built into intelligent systems. In reality, the best tools already triage issues based on relevance, not just volume.
A well-built AI-powered technical due diligence platform scores risks by severity, recurrence, and business impact. It doesn’t just report a thousand code smells—it tells you which five are attached to customer-facing APIs that have historically triggered downtime. It learns from previous outcomes across deals. Over time, its “noise” becomes pattern recognition. And its precision improves with every evaluation.
Objection 4: “AI can’t replace deep domain knowledge during due diligence.”
This is actually true—and also irrelevant. No serious platform aims to replace domain experts. It aims to make their expertise more actionable by giving them context at machine speed.
An AI-powered technical due diligence platform gives a manufacturing-sector CTO an architectural comparison of three MES systems within 30 minutes. It shows how long security patches were delayed in similar SaaS environments. It surfaces anomalies, but lets the domain expert apply nuance. In fact, the best use case for such a system is accelerating domain insight—not faking it.
Objection 5: “We already have spreadsheets, checklists, and technical advisors—why complicate it?”
Because those tools are static, siloed, and often disconnected from the tech being evaluated. Spreadsheets don’t update themselves when a new CVE hits a key open-source component. Checklists can’t auto-flag inconsistent documentation across codebases. Advisors are valuable—but they’re limited by time, memory, and what they’re told to look for.
A smart AI-based due diligence tool doesn’t eliminate your team’s judgment—it elevates it. It gives you a living, collaborative view of a target company’s tech stack: how it behaves, evolves, and compares to benchmarks. In fast-moving M&A deals, that context isn’t just helpful—it’s decisive.
Objection 6: “AI can’t explain how it reached its conclusions—so how can we trust it in diligence?”
Transparency is a valid concern. But it’s outdated to assume all AI is a black box. The best AI-enabled due diligence systems now include explainability layers built in.
You don’t just get a score—you get the audit trail. Line-item logs, evidence of test failures, version histories, and change impact models are included. An AI-powered technical due diligence platform actually increases traceability—because it never forgets a flag, a fix, or a fault. If trust is built on transparency, this tech delivers exactly that.