Everyone wants AI magic.
Few want to invest in the plumbing.
We live in a moment where the promise of AI
is louder than the reality of data.
I see it every week with teams.
The excitement is real. The budgets are flowing. The pilots look impressive.
But when you lift the lid a different story shows up.
Data that lives in twelve places.
Metrics that mean one thing in finance and another in operations.
Ownership that sounds like “we think it sits with them”.
Governance that feels optional.
And still we keep sprinting toward AI, hoping it will smooth over the cracks.
It never does. It makes them more expensive.
Here is the part no one markets:
AI built on weak data creates more rework, more delays and more organisational friction than leaders expect.
If you want real value, start with the boring foundation:
→ Clear definitions
→ Reliable data
→ Agreed owners
→ Confidence in the outputs
When these are in place AI finally becomes what everyone wants it to be:
simple, scalable & repeatable.
So here is my question for every exec team:
What's the data truth you have been quietly avoiding?
♻️ Repost to help someone get their data AI-ready.
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The data truth most PE-backed LMMs are quietly avoiding: they don’t actually know what their data looks like. Not at the level of specificity that AI deployment requires.
They know roughly where the data lives. They have a general sense that the CRM and ERP don’t talk to each other cleanly. They suspect the revenue recognition logic in the billing system doesn’t match what gets reported. But they haven’t done the forensic work to quantify the gap — because that work is expensive, uncomfortable, and doesn’t show up in a board update as progress.
So the AI pilot launches on top of the mess. It looks impressive for 90 days. Then someone asks a question the model wasn’t designed to handle and the underlying fragmentation surfaces — faster and more expensively than before.
Clare’s boring foundation — clear definitions, reliable data, agreed owners, confidence in outputs — isn’t actually boring. It’s the $2M-$5M remediation investment that determines whether the AI spend that follows it is transformational or just expensive.
Brad Wolfe | wolfepacks.com Apr 9 2 likes
You could reply with something like: “Exactly this. AI doesn’t fix broken foundations it amplifies them.The teams getting real ROI aren’t the ones with the best models, they’re the ones who treated data like infrastructure first: defined, owned, and trusted. Everything else is just expensive iteration.” Apr 9 1 like