The AI Readiness Trap
Why Most AI Investments Fail Before They Start
You have been pitched AI six times this quarter. Your operating partner is asking about it. Your board wants an AI roadmap. Three vendors have shown you demos that look incredible.
Here is what none of them told you. Their product needs your data to work. And your data is not ready.
This is the AI readiness trap. Companies invest in AI tools, platforms, and consultants before fixing the data those tools depend on. The result is expensive pilots that stall, dashboards that nobody trusts, and a growing sense that "AI doesn't work for us."
AI works fine. The data underneath it doesn't.
What the Data Says
How the Trap Works
The Vendor Demo
An AI vendor shows you what their tool can do with clean, structured, well-labeled data. The demo is impressive. You sign a contract.
The Integration
The vendor's team tries to connect to your systems. They discover your CRM has 40,000 records with no consistent naming convention. Your ERP exports don't match your financial model. Your customer segmentation lives in a spreadsheet maintained by one person.
The Workaround
Instead of fixing the data, the team builds workarounds. Manual data cleaning. Custom scripts that break when someone changes a field name. A "data prep" phase that was supposed to take two weeks and is now on month three.
The Pilot That Never Scales
The tool works on a curated sample. Leadership gets excited. But the curated sample was hand-cleaned by a consultant. Scaling to production data means confronting the same mess they bypassed in step 2.
The Shelf
The tool sits unused. The vendor blames adoption. The team blames the tool. Nobody talks about the data.
What AI Actually Needs From Your Data
AI is not magic. It is pattern recognition at scale. The patterns are only as good as the data they are built on.
Clean Inputs
Every AI model inherits the quality of its training data. Duplicates, inconsistent formats, and missing fields mean garbage in, confidently stated garbage out.
Consistent Definitions
If "revenue" means something different in your CRM, ERP, and board deck, any AI tool trained on those sources will produce contradictory results.
Accessible Infrastructure
Most AI tools need data in a structured, queryable format. If critical data lives in spreadsheets and email attachments, the AI has nothing to work with.
Traceable Lineage
When AI produces an output, someone needs to verify it. Without data lineage, you cannot tell whether the recommendation is brilliant or hallucinated.
Repeatable Processes
AI works best when it can process data the same way every time. Manual steps that vary by person or month mean different results each time.
The Uncomfortable Truth for PE
AI has become a valuation narrative. Companies with credible AI implementations may command a premium on exit. Companies without an AI roadmap face buyer skepticism.
But here is the tension. AI readiness is becoming table stakes for exit, while premature AI investment can destroy the growth metrics that drive valuation. Companies are diverting cash from sales to re-engineering products with AI features. Growth rates fall. The exit window gets smaller.
The MMG Outlook Report for 2026 found that AI disruption now creates a new valuation gap in deals. A seller might discount AI disruption risk at 15%. A buyer puts it at 30% or 40%. That gap did not exist two years ago.
The firms getting this right are the ones that separate "AI strategy" from "AI tools." Strategy starts with the data. Tools come after.
The AI Partners Know This Too
If you have been approached by AI consultants, implementation firms, or technology vendors, ask them this question: "What does our data need to look like for your solution to work?"
The honest ones will tell you. Some of them are already sending prospects to firms like ours because they have learned the hard way that their value proposition falls apart without data foundations in place.
This is not a competitive dynamic. It is a sequencing problem. The data work comes first. The AI work comes second. When the sequence is right, both sides deliver.
How AI-Ready Is Your Data?
Take a 2-minute self-assessment to find out where you stand. Seven questions. Instant results.
What to Do Next
If you scored below 12, the sequence is clear.
The companies that win in the next two years will not be the ones that adopted AI first. They will be the ones that got the sequence right.
About Crawford McMillan
We help PE-backed and growth-stage companies fix data foundations so the numbers hold up under scrutiny, whether that scrutiny comes from a diligence team, an AI model, or an operating partner measuring value creation.
20 years of Fortune 100 data leadership. Cloud migrations saving hundreds of millions. Real-time systems powering billion-dollar decisions. Now focused on mid-market PE.
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