Use the Data You Have. Not the Data You Wish For.
Imperfect data is fine. Misplaced expectations are not.
One of the biggest blockers to AI momentum isn’t a lack of data. It’s a belief that the data isn’t “ready.”
We hear it all the time:
“Our data’s too messy.” “It’s scattered across systems.” “We’ll explore AI once we clean it all up.”
The impulse makes sense. No one wants to build on shaky ground. But here’s the problem: if you’re waiting for perfect data, you’re not just delaying AI. You’re delaying learning. And more often than not, you're chasing a moving target.
Today, most modern organizations have enough data to start asking better questions. The key is to focus less on data perfection, and more on data connected to real decisions.
What makes data “AI-ready”?
It’s not about structure or cleanliness. It’s about context and actionability.
Ask:
- Is this data tied to a decision someone is already making?
- Are there patterns that repeat: win/loss, delay/on-time, escalate/resolve?
- Can we identify what “better” looks like?
You don’t need pristine datasets. You need just enough signal to explore relationships, test hypotheses, and learn in cycles.
In fact, insisting on complete cleanliness can actually backfire. It slows progress, burns time, and distances your team from the real work.
Start with directional value – not diagnostic certainty
AI doesn’t have to be right to be useful. A pilot that’s 70% accurate can still surface trends, prioritize better, or flag risk in a way your teams can act on. That’s forward motion. That’s operational learning.
Think of it like this:
- Perfect data is a nice-to-have.
- Relevant data, applied to a real problem, is how you build confidence.
And confidence is what makes the next dataset easier to improve because you’re no longer cleaning in the abstract. You’re improving data in service of a real outcome.
Reframe readiness
Readiness isn’t a checklist. It’s a mindset. Your teams don’t need to “get ready” before they start. They need to start, then adapt. The organizations that move fastest are the ones that learn in motion.
TRY THIS NEXT 👇
Identify one workflow where your team handles monotonous tasks on an ongoing basis – especially those that rely on vague inputs, scattered knowledge, or repeated judgment calls.
It could be things like answering new employee benefits questions, searching past tickets to respond to similar customer issues or tagging feedback that comes in through freeform channels.
Then ask:- What informal rules or patterns are we already following to handle this work—even if no one’s written them down?
- Could we start labeling or grouping this data to surface those patterns more clearly?
- If AI could suggest a response, tag, or routing path—even 70% of the time—how much time would that save?
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