AI Strategy Can Destroy Exit Value, Not Boost It

Itay Sagie, a strategic adviser specializing in M&A and growth for tech companies, published an opinion piece challenging the widespread belief that AI automatically enhances company valuation and improves exit outcomes. While acknowledging that AI may create value for some companies, he argues that in many cases it can actually erode exit value—a counterintuitive claim given how aggressively boards and founders have embraced "AI-native" positioning.

Sagie identifies three concrete ways AI strategy can harm exit value. First, the rush to build AI-heavy product architectures—adding copilots, integrating multiple AI models, layering orchestration systems, deploying vector databases, and grafting on third-party AI tools—accelerates product development and helps teams ship faster. However, from an acquirer's perspective during due diligence, this approach creates a tangled technical landscape. Buyers want to understand which AI models are embedded, which vendors are essential to delivery, how customer data flows through the system, how outputs are monitored, and what happens if pricing changes, APIs break, or regulation shifts. A startup may celebrate this as innovation; a buyer sees it as vendor dependency, compliance exposure, security risk, and integration complexity—all of which reduce confidence and lower the acquisition price.

Second, Sagie notes that the novelty premium on AI features has largely disappeared. Even one year ago, adding AI functionality could excite investors and buyers. Today, many AI capabilities—summarization, search, chat interfaces, recommendations, content generation, workflow assistance—are readily available through shared underlying models and infrastructure, making them easy for competitors to replicate within weeks or months. Strategic acquirers rarely pay a premium simply for integrating the latest model; they pay for what they cannot easily build themselves: proprietary datasets, unique customer workflows, strong distribution, deep vertical adoption, or network effects. Founders should ask whether their AI strategy creates a defensible asset or merely adds copycat features.

Third, AI is redrawing the strategic buyer map. Historically, exit strategies followed predictable lines: cybersecurity startups sold to larger cybersecurity vendors; vertical SaaS to competitors in the same industry; workflow automation to productivity platforms. As AI expands platform capabilities, strategic buyers are moving into adjacent markets they previously ignored. An infrastructure company may acquire an identity platform because AI agents need secure access controls; an ERP vendor may buy workflow automation because AI is moving closer to business process execution; a data platform may acquire a vertical application because domain-specific data is becoming more valuable. Sagie advises CEOs to revisit their buyer map every six to 12 months, as the most logical acquirer today may not be the same one that would have been logical even one year ago.