Let’s be blunt: most private equity firms are behind on AI.
Not in their fund decks. Not in their investment memos. Not in the slides they show LPs.
But in how they assess, implement, and scale AI inside their portfolios.
And it’s costing them growth, margin, and—most critically—time.
Because while they’re still focused on functional benchmarking and classic EBITDA margin expansion, a new class of operators is building AI-native companies that:
If you're a PE firm and you're not driving this inside your portfolio, you’re already losing ground. Not to theory. To competitors.
Let’s get this straight: AI isn't “emerging tech.” It's an immediate operational advantage.
This isn’t 2014 cloud strategy or 2018 digital transformation.
AI today is:
If AI isn’t showing up in your margin profile within 12 months, you’re not using it right.
This isn’t a bet. It’s a missed compounding lever.
Across dozens of portfolio companies, here’s what we’re seeing most often:
That’s the gap. And it’s growing.
Here’s what AI makes possible across a PE-backed company:
Function -> Pre-AI Benchmark -> Post-AI Target
Sales -> 1 AE → $1.2M quota -> 1 AE → $1.6–$2M (AI-augmented)
Marketing -> 1 marketer → 2 campaigns/month -> 1 marketer → 6–8 campaigns/month
Support -> 1 rep → 50 tickets/day -> 1 rep → 100 tickets/day (triaged)
RevOps -> 1 analyst per 2 teams -> 1 analyst per 4+ teams (auto reports)
Engineering -> 1 feature/month -> 1–2 features/week (AI + smaller team)
Multiply that across a portfolio and the IRR uplift is material.
The best AI-adopting portcos have these characteristics:
If you're a board member or operating partner, here’s the truth:
You don’t need to be an AI expert. But you do need to know what great looks like.
Your job is to ask the right questions and hold leadership accountable. Here’s where to start:
If the answer is “our innovation team” or “we’re experimenting across functions,” they’re not serious.
There should be a:
This person owns the roadmap, metrics, and implementation cadence.
Ask:
If they can’t point to clear, quantified improvements, they’re not moving fast enough.
You want to hear:
Adoption without usage is just optics.
If the org chart hasn’t changed since AI entered the building, they’re probably not leveraging it.
Ask:
Org design is the lagging indicator of real AI transformation.
AI is not just a cost story. It’s also:
Make sure it shows up in revenue, not just expense lines.
AI-readiness should now be part of every diligence process.
You’re looking for signs like:
If you find these early, you know you’re buying a company that can scale without scaling cost.
That’s the next-generation compounder.
If you're building a cross-portco AI strategy, start here:
Area -> Standardization Recommendation
Tooling -> Choose baseline AI tools for each function (e.g., Gong, Copilot, Jasper, ChurnZero)
Role Definitions -> Redefine GTM, CS, and ops roles with AI expectations built in
KPIs -> Track AI-related productivity metrics (e.g., tickets/rep, campaigns/marketer)
Education -> Run internal “AI enablement bootcamps” with examples per team
Operating Reviews -> Make AI roadmap updates part of every board meeting
Incentives -> Tie OKRs to AI usage and measured leverage
This isn't theory. This is operating system design.
Here’s where PE firms should be driving the edge:
The best firms aren’t waiting for the tech to mature. They’re designing for it now.
Be careful of:
Your job is to separate motion from performance.
Because adoption without implementation is just burning cycles.
The firms that win in this next chapter will be the ones who understand AI not as a tech trend — but as a structural advantage.
They’ll back CEOs who:
This isn’t a memo for the future.
This is the playbook right now.
And if you're not holding your teams to it, someone else is — and they'll move faster, burn less, and return more.
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