AI is everywhere but value creation is harder to find. 

In this piece, John Bova, seasoned value-creation leader and Advisory Board member for the Value Creation Summit, breaks down how AI fits (and doesn’t fit) into real-world value creation strategies for private equity and portfolio leaders.

At the Value Creation Summit, March 12–13 in Miami, practitioners aren’t just talking theory. They’re focused on execution: what works, what doesn’t, and how to turn technology into a real lever for value across the investment lifecycle.

Below, John breaks down how to move beyond experimentation and align AI with lasting value creation.

👉 These themes will be explored further during the Summit’s Technology Transformation sessions, where operating partners, deal teams, and portfolio leaders will share how they’re applying AI and digital tools to drive measurable performance.

Does AI Fit Into Technology Value Creation?

2025 has been the year AI sparked both anxiety and opportunity. Much of this stems from companies wrestling with how  and where to adopt AI across their organizations. At the heart of this question is not just “Can we use AI?” but “How does AI fit into a broader technology-enabled value creation strategy?”- especially in private equity.

In 2024, many firms began experimenting with AI tools through familiar platforms. But as we pivot into 2026, the era of playing with AI is ending. What’s required now is a cohesive strategy that aligns AI capabilities with clear business outcomes. Too often, teams across portfolio companies treat AI as a tactical add-on rather than a strategic enabler, which can lead to fragmented efforts and missed value.

A key challenge lies in data readiness. AI’s promise depends entirely on the quality of the data that feeds it. Without foundational investments in data governance, warehousing, and business intelligence tools, firms will struggle to move beyond basic automation and into predictive modeling and scenario planning.

John emphasizes the importance of creating an internal center of excellence for AI within the operating partner model, not just a technology function, but one tied to strategic priorities and measurable KPIs. In private equity, this means aligning AI initiatives with value creation plans across portfolio companies and establishing ownership for execution and accountability.

For example, early wins with AI might come through business process improvement and documentation, where automation uncovers inefficiencies and accelerates execution. From there, firms can layer in predictive analytics and more advanced use cases once the foundational tech and data infrastructure is in place.

Ultimately, John’s perspective is clear: AI should enable strategy, not replace it. Integrated thoughtfully, AI can help firms speed insight, amplify human expertise, and unlock new levers of value but only if it’s grounded in a comprehensive approach to technology and data.