Can We Trust Our Data Enough to Let AI Act On It?

This is Part 1 in a short series answering the questions CROs and RevOps leaders raised at the CRO networking event in London.

In This Series

Part 1: Can We Trust Our Data Enough to Let AI Act On It?

Part 2: How Do You Bring Your Team With You When AI Arrives and the Stack Is Shrinking?

Part 3: How Do You Explain Your Value When You Sell More Than One Thing, and Through Partners?

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At a recent gathering of CROs in London, almost everyone had the same story. Someone switched on an AI assistant, asked it a sensible question about the pipeline and got a confident answer that was wrong. Not wildly wrong. Wrong in the way that takes a sales manager twenty minutes to spot.

The AI wasn't the problem. It read the record exactly as it was.

That's the uncomfortable part. Messy CRM data has always been survivable because people quietly correct for it. A rep knows the contact on the deal left in March. A manager knows one region only updates stage dates on Fridays. Experienced people carry a layer of corrections in their heads that never makes it into the system.

AI doesn't have that layer. It takes your data at its word, and it acts on it at speed.

So the real question isn't whether your data is perfect. It never will be. The question is whether it's good enough for the specific job you're asking AI to do.

That shift turns an impossible project into a set of manageable ones. Summarising an account before a call needs complete activity history. Prioritising leads needs reliable engagement signals and clear ownership. Forecasting needs honest stage data and close dates people actually update. Each job depends on a short list of fields and sources, and that list is where you start. A company-wide data cleanse, by contrast, rarely finishes.

Three questions will tell you where you stand for any one job. Start with where each piece of data comes from, and whether it lands in one place. If the same contact lives in the CRM, the marketing platform and a spreadsheet, the AI will find whichever version it was pointed at. Then ask how old it is, because data that was true when it was entered decays as people change jobs and deals stall. Finally, ask who owns fixing it. A field with nobody's name on it won't stay clean after the project team moves on.

The teams getting real value from AI tend to do something unglamorous first. They pick one use case, trace the data it needs back to its source, and fix what's broken along that path. Only then do they switch it on.

They also let people see what the AI saw. When a rep can click from a recommendation to the activity behind it, they catch a bad record in seconds. Just as importantly, they learn which outputs hold up, and that's how trust builds.

Visibility changes behaviour too. Once people see their data being used to help them rather than to report on them, they become far more willing to keep it accurate. The data improves because it's finally useful to the person entering it.

Trust in AI is borrowed from trust in the record. Earn the second one use case at a time, and the first follows.

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