Unlocking Real AI Value: A Due Diligence Guide for PE Investors

AI has become a defining feature in technology-driven private equity deals. Just ten years ago, few companies used AI in core products. Today, over a third of pitch decks reference machine learning or predictive intelligence. For investors, this presents a powerful opportunity — but only if they can distinguish between genuine innovation and empty claims.

Recent events have highlighted the need for greater scrutiny. In 2024, the U.S. SEC charged two firms with misleading investors about their use of AI. And in 2025, Builder.ai was reported to have faked business deals to inflate its value (Bloomberg, 2025).

These incidents aren’t reasons to shy away from AI — they’re reminders that smart capital requires smart validation. With the right technical due diligence, PE firms can go beyond the pitch and evaluate what truly works, scales, and creates value.

Why Traditional IT Due Diligence Isn’t Enough

Traditional IT diligence methods — interviews, demos, and documentation — rarely provide a full picture of how software actually performs under the hood.

Software Improvement Group (SIG) data reveals a key blind spot: 73% of AI and big data systems fail to meet basic build quality benchmarks.

These systems are often:

• Poorly documented
• Built on legacy infrastructure
• Hard to scale or transfer between teams

These risks don’t just impact integration — they can suppress long-term growth potential.

How AI Is Reshaping the PE Lifecycle

AI is no longer just a buzzword. It’s becoming a core factor in deal sourcing, value creation, and exit strategy:

• Buy: In the acquisition phase, it’s not enough to just assess features — investors must evaluate how well the system is built. AI systems that lack documentation or structural stability can signal technical debt — making early diligence a strategic advantage.

• Grow: During ownership, AI can unlock efficiency and innovation — if the system is scalable and maintainable. PE firms should treat AI readiness as a marker of maturity. When poorly implemented, AI can become a bottleneck rather than a lever for innovation — making scalability and maintainability essential metrics.

• Sell: At exit, buyers are paying close attention to AI capabilities and technical robustness. A solid, well-architected AI system adds to the company’s story and can increase valuation — while unstable or hardcoded systems raise red flags.

Who to Trust With AI Due Diligence

That’s where Software Improvement Group (SIG) comes in. With 25+ years of software analysis, they enhance traditional diligence by validating what’s real — and revealing untapped opportunities for value creation.

Here’s how SIG protects PE firms from costly AI surprises:

• Code-Level Verification: SIG analyzes source code for quality, security, and scalability.
  
  
• AI Capability Checks: Their experts assess whether the models, pipelines, and data structures actually work — and whether they deliver value.
  
  
• Security & Compliance: Built-in checks ensure systems meet relevant standards and avoid hidden vulnerabilities.

• Maintainability: SIG evaluates whether the tech can evolve and scale with the business - or if it's a ticking time bomb.
  

Why It Matters

Visionary roadmaps and polished demos might open doors — but lasting value comes from robust, scalable, and maintainable technology foundations.

Download the full Private Equity Signals 2025 report by SIG for actionable insights on AI risk, tech due diligence, and operational resilience.

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