AI in PE isn’t just about sourcing. The bigger question: how do you make it stick across dozens of portfolio companies?

The firms pulling ahead aren’t just testing AI in one or two portfolio companies—they’re institutionalizing it across the board.

The difference between “we tried AI” and “we scaled AI” comes down to structure, culture, and execution. The leaders are making AI a portfolio-wide mandate, creating internal hubs of expertise, and setting up systems that turn one portco’s success into a repeatable playbook for the next.

Vista’s AI Mandate: Targets for Every Portco

Vista Equity Partners has made it clear—AI adoption isn’t optional. Every portfolio company is required to:

• Set annual AI performance targets
• Report progress against clear KPIs
• Integrate AI into at least one revenue- or efficiency-driving workflow within the year

This top-down approach ensures that AI isn’t siloed in innovation labs—it’s embedded in sales, customer experience, operations, and product development. And because the targets are tracked, AI becomes part of the value creation conversation at every board meeting.

Apollo’s AI Center of Excellence: Scaling Best Practices

Apollo Global Management took another approach—building an AI Center of Excellence (CoE) that acts as the connective tissue between portfolio companies.

The CoE’s role:

• Evaluate AI tools and vendors before they’re rolled out
• Share implementation blueprints from early adopters to late movers
• Provide compliance, data security, and change management frameworks
• Maintain an internal “AI wins” database for operators to reference

This not only speeds up adoption but also de-risks experimentation by avoiding redundant vendor trials and costly false starts.

Cross-Portfolio Knowledge Sharing: Turning Wins into Playbooks

One of the fastest ways to multiply AI impact is to share results quickly and transparently.

Leading firms are:

• Hosting quarterly AI roundtables for portfolio CTOs, COOs, and CDOs
• Running internal Slack/Teams channels where operators can post questions and lessons learned
• Creating searchable libraries of case studies, ROI metrics, and implementation timelines

When one SaaS portfolio company cracks an AI-powered lead scoring model, the framework gets rolled out to five more in months—not years.

Building a Culture of Experimentation and Scale

Institutionalizing AI means embedding it into the way the firm thinks and operates:

• Quick wins first → Prove value in weeks, not quarters
• Guardrails in place → Data governance, bias audits, and security baked in from day one
• Iterate, don’t stall → Pilot, measure, refine, expand

And critically, leadership sets the tone. When deal teams and operating partners consistently ask “Where’s the AI opportunity here?”—it signals that this isn’t optional innovation, it’s strategic execution.

Why It Matters

For LPs, AI isn’t a buzzword—it’s a performance lever. Firms that can show portfolio-wide AI impact deliver:

• Faster EBITDA growth
• Stronger exit multiples
• Reduced operational costs
• Resilient, future-ready companies

The takeaway? The winners in the next decade of private equity won’t be the firms that experimented with AI—they’ll be the ones that built systems to scale it.

Final Thought:
AI success in private equity isn’t luck—it’s design. Mandates set the expectation. Centers of excellence provide the toolkit. Knowledge sharing multiplies results. Together, they transform AI from a point solution into a portfolio-wide advantage.

At Kayana, we help PE firms embed AI-savvy remote professionals into both portfolio companies and operating teams—bridging strategy and execution so you don’t just try AI, you scale it.

👉 Ready to build your firm-wide AI playbook?
Book a strategy call and learn how Kayana can help you turn AI mandates into measurable portfolio-wide wins.