DEEP DIVE
Your AI strategy is already failing, and it’s costing you multiples
Most PE firms I speak with currently have at least one portfolio company racing to implement AI.
The board loves it. The CEO is all in. The vendors promise transformation.
Here’s the uncomfortable truth. More than 70% of AI projects fail. Not because the models are weak, but because the data underneath them isn’t ready.
When your portfolio’s data is fragmented, inconsistent, or poorly governed, AI doesn’t fix it. It multiplies it. Instead of insight, you get hallucinations. Instead of efficiency, you get expensive noise.
And when diligence season comes, you discover the real cost: missed synergies, valuation discounts, and post-close cleanup that eats internal rate of return.
The $15 Trillion Divide in Enterprise Value
Look at what the smartest suppliers in the world are doing. Palantir and NVIDIA are not just selling AI models. They are selling data infrastructure.
Everyone now has access to the same AI tools. What differentiates performance is data maturity.
The firms that treat data as an investable asset will own the next decade. The rest will spend it fixing the mess their pilots created.
The Three Foundations of AI That Don’t Appear on a Balance Sheet
1. The Expensive Wake-Up Call
Most portfolio companies realize their data is broken after they have already paid for the licenses and vendors. They spend heavily on pilots and hype, only to discover their “AI” can’t reconcile basic metrics across finance and operations.
The cleanup costs more than the project.
2. Data Quality as a Valuation Lever
This is not about dashboards; it is about confidence. When “CAC” means one thing to finance and another to marketing, diligence stalls. When customer records are incomplete or duplicated, buyers discount forecasts.
Data errors leak value. Clean data protects multiples.
3. Technology as Strategy
Portfolio companies that treat technology as overhead cannot scale insight. The ones that invested in data infrastructure early, with clear governance and quality controls, are now deploying AI in 90 days instead of nine months.
They can answer buyer questions instantly. They look enterprise-ready and command a premium.
The Palantir Test for PE Firms
Ask yourself this: if you deployed Palantir Foundry or any enterprise AI platform onto your portfolio today, how many of the companies could actually use it?
If the answer is not many, the problem is not AI strategy. It is data maturity.
AI multiplies what already exists. And in most mid-market portfolios, it is multiplying chaos.
The Investment Thesis You Can’t Ignore
Here’s what this really means:
- AI without solid data foundations is a valuation risk, not a growth play
- Poor data quality kills more AI projects than poor algorithms
- Companies that treat data as strategic infrastructure will exit faster and cleaner
The firms that fix this now will acquire those that don’t, at discounts.
In 2025, data-blind portfolio companies are the new distressed assets.
Next Step for Operators
Before you fund another AI initiative, pick one critical metric: revenue, retention, or margin.
Trace it back to its source.
Can you reconcile it across every system, with confidence, right now?
If not, you’ve already found the constraint that will cap your multiple.