Across all sectors, a stark divergence has emerged between AI ambition and corporate reality. Boardrooms are eager to capitalise on the rapid pace of AI adoption, yet underlying technical foundations are frequently unequipped to support it. In the private equity sector, this gap has direct, measurable consequences for how risk is priced, how value is created, and how assets are monetised.
When investors ask, “What exactly am I buying?”, they are increasingly asking a technical question about how AI has shaped the software they are acquiring. First: How much of this product’s core functionality was autonomously generated or accelerated by AI? Second: What systemic liabilities does that acceleration introduce in terms of long-term security, maintainability, and regulatory compliance?
Despite their critical importance, these inquiries are not yet standard components of most IT due diligence (ITDD) frameworks. To protect capital in an AI-saturated market, it is vital that they become so.
Private Equity Firms must re-evaluate their criteria for AI Quality
If competitors utilising equivalent foundational models and nimble teams can replicate a target software’s core functionality in weeks, the historical valuation benchmark of “100 person-years” of R&D effort becomes irrelevant. Code volume has been eclipsed as a competitive hurdle; the true challenge lies in duplicating the underlying operational logic. As a result, competitive advantage is shifting away from the code itself and toward the proprietary expertise embedded within it. This sentiment was echoed in a Financial Times interview with Thoma Bravo, who recently noted that enterprise software value is no longer about the code itself, but about deep, defensible domain expertise.
The capacity to construct complex software systems is now secondary to an enterprise’s structural grasp of its vertical market. Long-term asset success depends entirely on a firm’s deep familiarity with automated processes and the deliberate translation of that expertise into product choices. While AI dramatically accelerates raw code production, it remains a poor substitute for domain-specific insights or sound system design.
Furthermore, it is necessary to evaluate the exact engineering methodologies behind AI-assisted development. Accelerated implementation without maintainable code leaves a highly fragile infrastructure underneath. When portfolio companies prioritise sheer speed, they routinely bypass standard quality gates, code reviews, and static analysis checks. This creates an aggressive accumulation of technical debt. Over time, poor structure and limited modularity make systems increasingly expensive to maintain.
Investors must therefore look past superficial metrics. Extensive codebases and large engineering departments are no longer sufficient indicators of a defensive competitive moat. Due diligence must target the elements that are genuinely difficult to mimic: the embedded domain knowledge and the effectiveness with which it is integrated into the software architecture. Blindly acquiring AI-accelerated systems without validating their underlying engineering principles introduces severe downstream operational risks.
AI is still software, despite appearing otherwise
Despite its apparent novelty, AI remains software and should be governed accordingly. While AI introduces additional considerations around data provenance, model behaviour and regulatory compliance, it does not replace the need for disciplined software engineering. If anything, it raises the bar for governance and engineering discipline.
When assessing AI-driven software assets, investors must introduce deeper engineering criteria. With AI capabilities evolving at breakneck speed, many applications are being optimised strictly for short-term commercial gains and rushed to market. Autonomous AI agents and complex systems are frequently deployed before they are thoroughly understood, leaving investors with limited visibility into how those systems operate and what risks they introduce.
For AI deployment to be structurally safe, investment firms must verify the integrity of the data environments feeding these models. This requires a strict evaluation of the software architecture to secure the inputs that train the model, the interfaces that interact with it, and the operational permissions it holds. Failing to do so directly exposes the asset to data compromise, architectural drift, and intellectual property vulnerabilities.
Bridging the value gap
It’s vital to stress that this isn’t just a technical risk, but a fundamental business one. Institutional buyers and enterprise customers increasingly demand strict lineage, transparency, and traceability for the data and decisions produced by automated systems. If a portfolio company’s AI engine is built on an unverified foundation that has not been rigorously tested for algorithmic resilience or output hallucinations, the underlying business model itself becomes fragile.
Weak traceability or unpredictable model behavior raises immediate red flags during exit diligence, severely impacting valuations and compressing exit multiples.
Traditional IT due diligence frequently fails to pierce the veil of modern AI hype. When private equity firms omit a rigorous focus on the core engineering of a target’s AI stack, they risk investing in companies that cannot scale, adapt, or pass strict regulatory scrutiny under emerging frameworks like the EU AI Act.
The private equity industry stands at a critical crossroads where the market pressure for rapid AI integration is colliding directly with the fundamental requirements of disciplined software engineering. While the appetite for technology-driven transformation is clear, the current gap between investment enthusiasm and operational reality exposes firms to severe maintenance traps.
To bridge this divide, private equity firms must shift their focus from the superficial veneer of AI capabilities to the structural resilience of their software assets. By updating standard IT due diligence scopes to explicitly audit AI governance, verify guardrails around generated code, and assess architectural differentiation, investors can ensure their portfolios are not just temporarily AI-enabled, but are fundamentally engineered for sustainable growth and long-term exit readiness.
