Right now, nearly every organisation is under pressure to: “Do something with AI.”
Some are automating customer support. Others want to predict churn, personalise journeys, or replace surveys with AI agents.
But here’s what we keep seeing: Companies are pushing ahead with AI, without the data to build it or measure if it’s working.
AI Without Data Is Just Expensive Guesswork
Executives greenlight pilots, teams scope builds, vendors pitch solutions. But under the hood? It’s thin.
They’ve got:
- CRM profile data (name, role, industry)
- A few support ticket logs
- A handful of anecdotal insights
What’s missing?
- Behavioural patterns
- Transaction history
- Customer feedback signals
- Clearly defined outcomes
- Data structure and volume to train from
AI doesn’t run on ambition. It runs on patterns. And if the patterns don’t exist, there’s nothing to learn from.
Data quality isn’t just about cleanliness—it’s about relevance, richness, and structure. Without those, AI has nothing meaningful to work with.
Example: The Churn Prediction Model That Misfired
What they built: A churn prediction model using CRM data and support ticket volumes.
What they lacked:
- Product usage tracking
- Customer sentiment (unstructured signals like complaint data, open-text responses, and support tone)
- Historical outcomes (renewed vs churned)
- Clear definition of “at-risk” behavior
The result: The model flagged high-support users as churn risks— But these were actually the most engaged, loyal customers.
Meanwhile, silent, disengaged accounts weren’t flagged—and quietly churned. The AI didn’t fail. It just learned from the wrong signals—because the right ones weren’t available.
What Good AI Needs
If you’re trying to predict, simulate, or automate anything, you need:
- A dataset that reflects reality—depth, breadth, and variation
- Multiple interactions per user or entity
- Behavioural context (not just profile info)
- Structured data that can be learned from
What Data Experts Will Tell You
It’s not just about scale.
Yes, consumer-facing models (e.g., product recommendations or dynamic pricing) often require large datasets—sometimes 10,000+ users—to uncover subtle patterns. But in B2B or lower-volume environments, you don’t need massive scale. You need clarity, structure, and signal.
A few hundred well-labeled records can outperform 10,000 rows of noise—if you know what you’re training for.
And here’s the catch: AI won’t fix data that’s weak, biased, or poorly defined. It will just amplify it.
A Company That Got It Right
A telecom provider also wanted to reduce churn. But instead of rushing into tooling, they focused on data readiness:
- Integrated billing, usage, support, and loyalty data
- Tagged past outcomes (renewed, downgraded, churned)
- Created interpretable signals for AI to learn from
The result:
- 30% churn reduction
- 20% increase in offer acceptance
- Faster, more cost-effective service delivery
Why did it work? Because they started with the data—not the deadline.
Don’t Just Build AI. Build It Right.
At Twenty44, we help businesses stop guessing and start delivering. If you’re being told to “just build something,” hit pause and ask: Do we actually have the data to do this well?
If the answer is anything but a confident “yes,” let’s talk.
Build from insight. Not impulse.
