The traditional PE strategy involves buying legacy companies and cutting costs by ~10%. AI enables startups to rebuild entire industries from scratch, slashing costs by 90-99%. This allows VCs to fund disruptors that can out-compete and dismantle sectors previously dominated by PE roll-ups.

Marketers win with AI not by making existing tasks faster, but by using it to unlock new growth opportunities. The focus should be on game-changing programs that drive revenue, rather than on simply achieving incremental efficiency gains.

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.

Focusing AI efforts on efficiency and cost reduction offers limited, short-term benefits. The truly transformative approach is to invest in AI to create new revenue streams, enhance product offerings, and grow the business exponentially.

Mature enterprises are moving beyond using AI for operational efficiency. They are now leveraging their unique, proprietary data to train custom models. This allows them to build differentiated services and products that competitors cannot replicate, creating new top-line revenue opportunities rather than just improving bottom-line savings.

Initially dismissing AI for creative tasks, media companies now recognize its inevitability. The key to adoption is framing AI's value around revenue generation (making more money), which is a far more compelling business case than simply cost-saving (e.g., reducing producer headcount).

AI companies are pivoting from simply building more powerful models to creating downstream applications. This shift is driven by the fact that enterprises, despite investing heavily in AI promises, have largely failed to see financial returns. The focus is now on customized, problem-first solutions to deliver tangible value.

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.