AI was supposed to eliminate the grunt work. So why are CX teams becoming the journey police?

Everyone has an agent now, and suddenly everyone's an insight author. Product, research, design, and support can all add to your journeys faster than anyone could before. On paper, that should free up the practitioners who used to do this work by hand.

But scaling creation isn't the same as scaling the practice. And if the core CX team still has to check everything before anyone can use it, journey management hasn't really scaled beyond CX at all.

Picture a Tuesday morning in a mid-sized CX team. Overnight, agents across three departments generated forty new insights from support tickets, sales calls, and a product survey. Some are duplicates of things the team already knew, worded just differently enough that search doesn't catch them. Some are tagged to the wrong journey. A couple contradict each other because they were pulled from different time periods without anyone noting that.

None of that is a bug in the AI. It's what happens when speed meets a lack of shared standards. By lunchtime, the journey manager has waded through all forty, merged a dozen, corrected the tagging on a dozen more, and flagged three for a second opinion. That's a full morning gone, and not one new decision was made because of it.

This isn't a story about AI being bad at CX work. AI is good at CX work, maybe too good: it can generate insights all day without getting tired. The problem shows up after generation. Speed without a shared standard doesn't scale a practice. It just produces more of the same mess, faster. Letting 10 people create 10X as much doesn't help if two people still have to check every line by hand.

It's also not a new problem, just a bigger one. Journey managers have policed quality for years: checking naming conventions, merging near-duplicates, deciding what counts as real evidence. When a handful of people submitted the occasional insight, a journey manager could hold that line by hand.

Now, an entire organization's worth of agents can submit insights continuously, and hand-checking is no longer part of the job. It becomes the job, leaving no time for the part that actually matters: deciding what all that evidence means and what to do about it.

Every hour spent policing content is an hour not spent turning it into a decision the business acts on. The team that was supposed to get more strategic as AI took over the grunt work has instead become the bottleneck between AI's output and anything useful happening with it. 

The fix isn't slower AI. It's a standard AI can actually hold itself to. That's the shift we're bringing to TheyDo this September, and it's why we're not calling this release a feature update. We're talking about what it actually changes: whether your CX team spends its time policing AI's output, or acting on it.

We'll show exactly how on Sept. 15. Part of it is keeping your library in check by automatically detecting duplicate insights and merging them before they pile up. The bigger part is what happens before that mess gets created in the first place, giving your team's agents a standard to follow so the output already meets the bar instead of needing someone to clean it up after.