Every AI vendor in the contact center space has a highlights reel.
And most feature some sort of combination of better handle times, improved customer satisfaction, and agents freed from the grind of manual tasks to focus on conversations that actually need them.
What gets talked about far less is the stretch between go-live and those headline results.
When a team is working with new tools in real conditions for the first time – with real customers, real call volumes, and real edge cases – that's where the quality of a deployment actually gets decided. And it rarely looks as clean as the pitch.
Audrey Boussac, Head of Project Management at Diabolocom, has experience working with contact centers through the entire AI deployment cycle, spanning everything from configuration to friction points, to the moment a team stops second-guessing the AI and starts building on it.
“A lot of people are getting excited to work with AI because it looks cool, because everybody's talking about it,” Boussac says.
“But that excitement lessens when they realize it can take time to make sure that you have something that is relevant and effective.
“AI is not just a magic button; you just don't click on something and, boom, you've got the result.”
Not Every AI Product Reveals Its Value on the Same Timeline
One of the more common mistakes contact center leaders make when evaluating AI performance is applying a single measurement window to tools that work on completely different timescales.
Real-time tools – the kind that support agents live on calls with live transcription – tend to show their impact quickly, as Boussac explains:
“From the first day you're going to start working with it, you're going to see the impact for the agent.”
Things like shorter calls and improved accuracy show up fast enough that you know relatively quickly whether the foundation is solid.
Quality monitoring, on the other hand, is a different story. The configuration is more involved, calibration takes longer, and early results are harder to read.
“When it's going well, it's easy to spot – because you get something that is just as accurate as a human person,” Boussac explains.
Organizations that previously had supervisors manually reviewing a handful of calls per week now have visibility across their entire operation.
It's a step-change in coverage, but it doesn't announce itself in week two. When leaders evaluate that kind of deployment on a short window and conclude it isn't working, they're often measuring the wrong thing at the wrong time.
Clarity Before Go-Live Changes Everything
Beyond product type, the single biggest variable in how quickly an implementation delivers is how clearly the organization knows what it wants before the work starts.
“Implementing AI just to implement AI is not what you want. Implementing AI because you have a commitment, because it's clear to you where you want to go, this is where you get the real wins.”
In a nutshell, Boussac is arguing that organizations with a defined goal move faster. Those starting from a blank page have to figure everything out in real time, which eats into the timeline and tests the patience of teams expecting quick wins.
This shows up most clearly in quality monitoring, where customers have to translate what they want to evaluate into terms that an AI can work with.
A criterion like “smile in the voice” is intuitive for a human supervisor and genuinely difficult to configure effectively.