Private equity firms are shifting from financial engineering to AI-enabled operational improvements to boost portfolio company performance, speakers said during a July 3 webinar hosted by CDO Magazine and Burtch Works. The move comes as traditional value levers like debt financing and multiple arbitrage deliver diminishing returns in high-multiple markets, pushing firms toward data-driven efficiency gains.
Panelists Michael Butts, CEO of Burtch Works; Anurag Sachdev, Head of Data and AI at May River Capital; and Robert McElherne, Technology Operating Partner at Varsity Healthcare Partners, outlined how AI is reshaping the PE operating model. They discussed practical methods for scaling AI across portfolio companies to improve efficiency and produce measurable outcomes.
AI as a driver of operational value
The standard private equity playbook is evolving. Instead of relying on financial engineering, firms are using AI to automate repetitive work and scale operations. The goal is to increase productivity and support EBITDA growth directly through technology, not just through cost cutting.
Shifting manual processes toward data-driven automation is a core focus of AI for Operations. The panel emphasized that AI's value lies in making existing workflows faster and more reliable, freeing up teams to work on higher-impact tasks.
A modular path to data foundations
One persistent question is whether to build a perfect data infrastructure before deploying AI. The panel recommended a modular, business-first method: identify high-impact, repeatable workflows, then construct data foundations just good enough to support them. The environment matures as value is demonstrated, avoiding the trap of endless data preparation with no business return.
This approach lets firms prove AI's worth quickly and funds further data investments from demonstrated wins. It also aligns data work tightly with operational priorities, preventing data teams from building solutions without a clear business need.
The rise of hybrid talent
As AI adoption accelerates, firms need professionals who blend technical skill with operational understanding. The panel highlighted roles like forward-deployed engineers and T-shaped professionals who bridge data science, engineering, and business strategy. These hybrid workers make sure AI solutions solve real problems, not just demonstrate technical capability.
The need for such talent is a central topic in the AI Learning Path for Operations Managers. Operations leaders who can translate between technical teams and business stakeholders are becoming critical to successful AI initiatives.
Automation alone isn't transformation
The panel drew a clear line between automation and transformation. Automation reduces manual effort and increases efficiency - but true business transformation changes products, customer experiences, and business models. Both have a place, but firms chasing long-term enterprise value must think beyond incremental efficiency gains.
This distinction matters for operations executives, who often oversee automation projects. The panel warned against confusing a series of small automations with a strategic shift in how the business operates.
Strategy and governance for sustainable AI
A business-first AI strategy starts with the problem, not the technology. The panel said organizations should define measurable outcomes first, then design the supporting data and AI capabilities. This approach demonstrates value faster and builds a scalable foundation for later projects.
As AI use expands, governance and cost management become more important. Techniques like Retrieval-Augmented Generation (RAG) and fit-for-purpose small language models (SLMs) can improve efficiency, reduce token usage, and protect proprietary data while maintaining strong performance.
Why this matters for operations professionals
The shift to AI-driven value creation puts operations leaders at the center of private equity strategy. You are the people tasked with identifying repetitive workflows that automation can improve and with building the data infrastructure that makes AI reliable. The hybrid talent model described by the panel means your ability to connect business needs with technical solutions is now a measurable driver of portfolio company returns. Build those skills, and you become indispensable in the new PE operating model.
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