Many firms are stuck in "pilot purgatory," launching numerous small, siloed AI tests. While individually successful, these experiments fail to integrate into the broader business system, creating an illusion of progress without delivering strategic, enterprise-level value.

Many firms mistakenly focus on AI outcomes first. True success, as shown by THL Partners, begins with the unglamorous foundational work of establishing a solid data structure, aggregation, and strategy before building tools or chasing insights.

Private Equity-backed companies are significantly behind their venture-backed counterparts in AI spending. This is largely because their CFOs and sponsors demand a clear, quantifiable return on investment and P&L impact, a difficult hurdle for emerging and experimental AI technologies.

Recognizing that enterprises struggle to deploy AI effectively, some PE firms are acquiring traditional businesses. Their strategy is to directly own the change management process, forcing AI implementation to unlock latent value that the original management couldn't capture on their own.

Private Equity value creation has evolved. In the 2000s, it was driven by leverage; in the 2010s, by digital transformation. Today, AI serves as the new foundational "operating system" for growth, embedding intelligence into every process, contract, and customer touchpoint to drive returns.

The rapid evolution of AI means traditional private equity M&A timelines are too slow. PE firms and their portfolio companies must now behave more like venture capitalists, acquiring earlier-stage, riskier AI companies to secure necessary technology before it becomes unaffordable or obsolete.

Private equity firms are aggressively implementing AI across thousands of their portfolio companies. This isn't just for efficiency; it's a strategy to boost profitability and make these companies, particularly struggling SaaS businesses, more attractive for exit in a tough market. This creates a massive, real-world testbed for enterprise AI.

There is a significant gap between how companies talk about using AI and their actual implementation. While many leaders claim to be "AI-driven," real-world application is often limited to superficial tasks like social media content, not deep, transformative integration into core business processes.

A satirical take highlights a real trend: large enterprises are rolling out AI tools not for tangible ROI but for "digital transformation" optics. Success is measured with fabricated metrics like "AI enablement" to impress boards and investors, while actual usage remains negligible and productivity gains are unverified.