Key Takeaways

  • Advent International runs over 80 active AI initiatives across 30 North American portfolio companies, shifting away from open-ended pilot tests toward concrete operational mandates.
  • Dedicated technical specialists inside Advent's portfolio support group build working minimum viable products before handing them off to management teams for internal rollout.
  • Process automation, consumer intelligence, and customer churn reduction represent the highest-conviction applications producing measurable margin gains.
  • Longer holding periods across private equity are forcing sponsors to drive returns through direct operational productivity rather than relying on multiple expansion.

Moving Past the AI Experimentation Phase

Private equity sponsors spent eighteen months running exploratory pilots. Advent International decided that phase is over. Managing Partner John Maldonado explains that the firm has built high conviction around specific operational workflows rather than chasing open-ended tech trials.

“Right now, just to give you a flavor and to mention that, we've got 30 North American portfolio companies. We have over 80 different AI initiatives up and running across the 30,” Maldonado told Bain's Hugh MacArthur.

The firm is not looking for general excitement. It tracks direct productivity gains and EBITDA growth. “For us, we are experimenting in some respects, but we also don't wanna experiment forever. So some of the things I just described, we're out of experimentation phase. We have high conviction,” Maldonado noted. “It is about productivity. It's about margin improvement. It's about better decision-making. So where we've built some conviction are across all those levers.”

Where Portfolio Deployments Actually Produce Margin

Instead of spreading resources thin across every potential tool, Maldonado focuses on three specific operational zones: customer churn reduction, consumer intelligence, and process automation.

“Where we're seeing the most traction today is process automation, consumer intelligence, and some core operating workflows. Churn identification and churn management has been one of the areas where we've had a lot of success,” Maldonado explained.

In subscription and high-volume consumer businesses, identifying customer churn risks weeks earlier changes unit economics immediately. Small gains in customer retention flow straight to gross margins without requiring additional sales headcount.

The execution model relies on centralized technical talent paired directly with portfolio operators. Advent places dedicated data science and engineering staff within its portfolio support group to de-risk development.

“And we also have some AI experts within the portfolio support group that are working directly with management teams to usually evaluate the opportunities where we can deploy AI. Sometimes they're gonna roll up sleeves and execute some of those initiatives. Usually it's to a minimum viable product stage, and then we've gotta figure out a better way to commercialize those within the companies,” Maldonado said.

This structure solves a classic execution bottleneck. Mid-market and large-cap portfolio companies often lack the internal machine learning talent required to build custom workflows. By deploying firm-level technical experts to build working prototypes, Advent absorbs the initial development friction before handing proven tools to company management.

Why It Matters

As exit windows remain tight and sponsor holding periods stretch, buyout funds cannot rely on market tailwinds or multiple expansion to hit target returns. Value creation now depends directly on quantifiable operational efficiency. Advent's model shows that large sponsors are treating AI capability as an active operational discipline run by specialized in-house talent rather than leaving tech adoption to individual portfolio companies.