- The Measureverse
- Posts
- The dirty secret of AI that nobody talks about at conferences
The dirty secret of AI that nobody talks about at conferences
Jeff Sauer
January 29, 2026
Why do 95% of AI projects fail?
MIT just studied 300+ AI deployments across major enterprises. Only 5% reached production or delivered measurable ROI.
The culprit behind this lack of success?
Not the large language model. Not the prompts. Not which AI tool you're using.
Data quality.
And here's what's wild: this isn't just a problem. It's one of the biggest service opportunities that nobody in our industry is talking about yet.
The Part Nobody Wants to Admit
Companies are spending half a trillion dollars on AI infrastructure in 2026 alone.
But here's what they're actually feeding their AI:
Duplicate customer records. Naming conventions change depending on who set up the campaign. Data scattered across a dozen platforms that don't talk to each other.
Only 16% of executives trust their own data.
And now they're plugging AI into this mess.
In this week’s video, I break down a framework for cleaning up the same data disasters we see time and time again with clients.
What This Actually Costs
Poor data quality costs organizations an average of $12.9 million per year.
The data quality market is projected to grow from $7 billion today to $31 billion by 2034.
Only 8% of organizations say they're AI-ready when it comes to their data.
That means 92% of companies know they need AI but have no idea how to fix their data mess.
And AI literally cannot clean this data on its own.
This is one of the few jobs that exists specifically because AI can't do it.
Right now, almost nobody is positioning themselves as "the data cleaning for AI experts."
That gap is wide open for you to take…
To Your Measured Success!
--Jeff Sauer
CEO of MeasureU