Stop Buying AI Tools
Part 1 of 2: Why AI Tool Sprawl Is Killing Your Value Creation Plan
There’s a pattern I’ve watched repeat itself across PE-backed companies over the past two years.
A new sponsor takes control. The 100-day plan goes up. Someone on the operating team — or a well-meaning CIO — flags that the commercial stack needs modernization. A budget gets approved. Gong goes in. HubSpot gets upgraded. Clari gets layered on top. Maybe a revenue intelligence platform also gets tagged on.
Twelve months later, the board deck has a slide on AI adoption. Usage metrics look fine, but revenue performance looks almost exactly like it did before.
The tools worked. The outcome didn’t.
This isn’t a technology failure. It’s a sequencing failure. It’s costing PE companies more than they realize — not just in wasted spend, but in a more dangerous currency: lost time inside the hold period.
The Real Problem Isn’t the Tech Stack
Some operating teams diagnose commercial underperformance as a tooling problem. Pipeline is weak? Get a better prospecting tool. Forecasting is unreliable? Add a forecasting layer. Churn is creeping up? Find a customer health scoring platform.
The tools get purchased. The implementation gets scoped. The reps get trained. Then three quarters later, the CEO or operating partner is back in the same conversation — just with a more expensive stack underneath it.
The issue was never the tools, it was the absence of commercial signal architecture — a coherent system that takes all that data generated by those tools and converts it into decisions a commercial leader can actually act on.
CRM data without signal is just records. Conversation intelligence without signal is just transcripts. A forecasting platform without signal is just a more sophisticated and dressed-up guess.
What “Buying AI” Actually Gets You
I’ve spent the last decade running commercial organizations inside sponsor-backed companies — BrightRoll, Yahoo, Motorsport, Recurrent Ventures, Vivvix. Before that and after, I’ve been inside enough PE board rooms to see the pattern clearly.
The AI tool purchase checks what is considered an important box, but it usually does not solve the revenue problem.
Here’s why: most AI tools are built to surface data, and they’re extraordinarily good at it. The better ones will identify a churning account, flag a deal that’s gone quiet, or score an inbound lead with impressive accuracy.
But surfacing data is not the same as creating signal.
Signal requires a layer of interpretation, prioritization, and decision architecture that sits above the tools. It’s the difference between an alert that says “this deal is at risk” and a system that tells you which deals to reprioritize this week, why, and what the rep should do differently in the next conversation.
That layer doesn’t come in the box. And most PE-backed companies have never built it.
The Question Few Ask Before They Buy
I’ve sat in tons of tool evaluation conversations to know the questions that always get asked: What’s the implementation timeline? How does it integrate with Salesforce? What do the contract terms look like?
The question that almost never gets asked: What decision will this tool make faster or more accurate?
Not “what data will it surface.” Not “what workflows will it automate.” What decision — by which human, on which timeline, with what consequence to revenue — will this tool improve?
If you can’t answer that question clearly before the purchase order goes through, you’re not buying signal. You’re buying infrastructure with a revenue story attached.
That’s not inherently wrong, because infrastructure has value. But it will not move your commercial outcomes on its own, and if your hold period is four years, you don’t have three cycles to figure that out.
The Arb That Still Exists
The spread between PE firms that understand this and those that don’t is still enormous.
The firms that get it are running something that looks more like a signal architecture — connecting commercial data sources to a prioritization logic to a set of operator-level decisions — and they’re extracting measurable revenue outcomes from AI investment that their peers aren’t seeing.
The firms that don’t are accumulating tools. Their portfolio companies are better equipped, but they’re not growing faster. The gap is not about which tools you buy. (My personal view is that most tools are starting to become commoditized.) It’s about whether you’ve built the layer that makes any tool produce a decision worth acting on.
I’ll dive into that layer next week — and walk through what it actually takes to build it inside a PE-backed company, starting from the first 60 days of ownership.
Matt Young is the founder of AI Value Edge and a senior commercial operator with two decades of experience scaling revenue inside PE-backed and sponsor-supported companies. He publishes AI Value Edge — The Growth Multiple on Substack.