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.
AI is commoditizing knowledge by making vast amounts of data accessible. Therefore, the leaders who thrive will not be those with the most data, but those with the most judgment. The key differentiator will be the uniquely human ability to apply wisdom, context, and insight to AI-generated outputs to make effective decisions.
Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.
The Jevons Paradox observes that technologies increasing efficiency often boost consumption rather than reduce it. Applied to AI, this means while some jobs will be automated, the increased productivity will likely expand the scope and volume of work, creating new roles, much like typewriters ultimately increased secretarial work.
To innovate quickly without being bogged down by technical debt, portfolio companies should ring-fence new AI development. By outsourcing it and treating it as a separate "skunk works" project, the core tech team can focus on existing systems while the new initiative succeeds or fails on its own merits.
Passively reading consultant decks is insufficient for grasping AI's potential. True understanding comes from active experimentation. Firms and their portfolio companies should "get their hands dirty" by building their own AI agents and co-pilots to discover the art of the possible and apply it directly to their own operations.