Why GenAI Programs Fail: It’s Not the Model—It’s the Data
Generative AI is everywhere right now. The possibilities are exciting: tailored customer experiences, faster workflows, new revenue streams. But here’s the problem no one wants to say out loud: most GenAI programs are failing.
A recent McKinsey article, “Overcoming two issues that are sinking GenAI programs,” highlights the common stumbling blocks—companies don’t know how to scale GenAI beyond pilots, and they lack clarity on where the value really lies. But there’s another unspoken truth: you can’t scale GenAI if your data isn’t ready for it.
Let’s dig in.
Messy Data, Messier AI
Generative AI models don’t magically generate brilliance. They rely on what you feed them: terabytes of company data spread across SharePoint folders, Slack channels, cloud drives, PDFs, databases, and more.
But how much of that data is actually usable?
If your unstructured data is fragmented, mislabeled, outdated, or riddled with duplicates, your GenAI tool will mirror those problems. Instead of generating useful insights or content, it could produce hallucinations, reinforce biases, or expose sensitive information.
GenAI doesn’t fail because the algorithms are bad. It fails because the data foundation is cracked.
Scaling Starts With the Basics
The McKinsey article explains that scaling GenAI requires “value clarity” and operational discipline. That’s true—but there’s an even earlier step: data discipline.
If your organization doesn’t have visibility into its unstructured data—what you have, where it lives, who owns it—you can’t responsibly or effectively deploy AI at scale.
Think about it:
How can you automate workflows if your data isn’t classified?
How can you create content if half your files are outdated?
How can you trust GenAI outputs if you don’t trust your inputs?
Scaling GenAI isn’t about buying more GPU power. It’s about preparing your data to support intelligent outcomes.
The Aparavi Approach: Clean Data. Smarter AI.
At Aparavi, we help organizations lay the groundwork for AI success. Our products connect to all your unstructured data—wherever it lives—and gives you full visibility into what’s there. From cleaning and classifying to preparing and piping it into AI workflows, we make sure your data is accurate, relevant, and secure before it ever touches a model.
The result? GenAI programs that deliver real value, not more risk.
Treat Data Like the Fuel It Is
Here’s a metaphor: building a GenAI program on messy data is like fueling a luxury car with dirty gasoline. Sure, it might start—but you’ll stall halfway down the road.
Cleaning: Removing duplicates, ROT (redundant, outdated, trivial) data, and noise
Classifying: Labeling and tagging files so AI knows what’s relevant
Controlling: Ensuring sensitive data isn’t accidentally exposed to AI tools
Curating: Selecting only the data that’s fit for purpose
These aren’t just IT tasks anymore. They’re prerequisites for responsible AI adoption.
Don’t Let Data Be the Bottleneck
The companies winning with GenAI aren’t the ones racing to build models. They’re the ones slowing down just enough to prepare their data first.
So before you ask: How do we scale GenAI?
Ask:
Do we know where our unstructured data lives?
Is it clean, current, and compliant?
Can we confidently feed it into a model without risk?
If the answer is “no,” your GenAI isn’t ready to scale. Yet.
Final Thought: Smarter AI Starts With Smarter Data
The McKinsey article highlights a hard truth: most GenAI programs don’t fail because of a lack of ambition—they fail because the groundwork isn’t there.
That groundwork is your data. Clean. Organized. Context-rich.
You don’t need all your data to be perfect. But you do need the right data to be prepared. Otherwise, your GenAI journey could sink before it even leaves the harbor.
Want to make sure your data is ready to scale GenAI?
Aparavi helps teams prepare, pipe, and activate unstructured data for AI. Because responsible AI starts with responsible data.
