Insights / AI

Your data decides whether AI works

Why AI demos work and production stalls

An AI demo usually runs on a clean sample someone prepared by hand. Production runs on the real CRM: the same customer entered three ways, empty fields, notes that live in someone's inbox. The model hasn't changed. The data has.

In a Gartner survey of 1,203 data management leaders, 63 percent said their organization either lacks the right data management practices for AI or isn't sure. Gartner's conclusion was blunt: if the data has issues, it isn't ready for AI.^[1] Gartner also estimates that poor data quality costs organizations an average of $12.9 million a year.^[2] A smaller company won't lose millions, but it feels the same problem as wasted hours and wrong answers.

What AI-ready means in a CRM

• Complete. The fields an agent would need to answer a question are actually filled in.
• Consistent. One format for names, addresses, statuses and dates.
• Connected. One record per customer, linked to orders, cases and documents in other systems.
• Current. Updated as work happens, not reconciled once a quarter.
• Governed. Clear owners and permissions, so AI only sees what the person asking is allowed to see.

A readiness check you can run in an afternoon

Pull 50 recent customer or member records and answer honestly:

1. How many are duplicates of each other?
2. How many are missing an email, phone or status?
3. Can you see the last interaction with each one?
4. Would you let a new hire answer a customer's question using only this record?

If the last answer is no, an AI agent shouldn't answer from it either. That's not a reason to wait on AI. It's a reason to start with the cleanup that makes AI worth doing.

Where AI pays off first

Summarizing calls and cases, triaging inbound email into the right queue, reading forms and documents, and drafting replies for a person to approve. Each works on a narrow slice of data that can be made ready quickly, which makes it a good proof of concept.