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.
“It can take two weeks, or it can take several months,” Boussac says, “depending on how much time you’re willing to invest.”
Why Purpose-Built AI Changes the Starting Point
One factor that consistently shortens the runway from deployment to results is working with AI that was built for the environment it’s operating in.
Diabolocom’s models are developed in-house and trained specifically for contact center use cases – not adapted from general-purpose LLMs designed to handle the full breadth of human queries.
For Boussac, the difference is most apparent in how quickly customers begin trusting what the system tells them.
“We’re investing not in general AI, but in AI for contact centers, which is very specific,” she says.
“That means you get to work with a model specifically trained to respond to humans when they’re reaching out for customer service, which is better suited to winning customer trust.”
Many customers arrive expecting the kind of generic model they’ve encountered with other vendors. Finding that the technology was built for their vocabulary, call conditions, and actual workflows, tends to shift the implementation dynamic early – helping to move it from working around limitations to working with something designed for those conditions from the outset.
What Good AI Actually Delivers – and How You Know
When it comes to building the internal ROI case after go-live, Boussac points to a consistent set of indicators, adjusted for what the deployment was designed to achieve.
For agent-facing tools, the clearest signals are efficiency-related: shorter handle times, higher call qualification rates, and fewer transfers. On the customer side, the markers are NPS, first-call resolution, and the overall quality of the experience.
“You get to have a shorter conversation, you get to have more people available because they’re having shorter conversations,” she explains.
A customer who reaches an agent already holding the full context of their inquiry is a fundamentally different experience from one who has to repeat themselves three times before getting to the right person.
Quality monitoring adds another dimension. When supervisors can review a thousand calls instead of ten, their role changes, as Boussac explains:
“The person who used to take hours to listen to conversations – and in general only analyze less than ten – is now able to analyze a thousand. And they’re able to spend time training people, because they know exactly where the issue is.”
Better coaching, grounded in a full operational picture rather than a small sample, flows directly into agent performance and customer satisfaction scores.
The contact centers seeing the strongest results from AI right now are not always the ones that moved fastest or spent the most; they’re the ones that went in with a clear goal, chose tools built for their environment, and gave the process the room it needed to deliver.
When those conditions are in place, the ROI follows.
For more on the research and quality foundations behind Diabolocom’s AI approach, read the previous piece in this series: ‘The Real Reason Your Contact Center AI Isn’t Delivering ROI’, featuring Machine Learning Research Engineer, Théo Deschamps-Berger.
You can also watch our video interview with Head of AI Product Rémi Guinier on how tailored, shapable AI delivers operational control.
Discover more about Diabolocom’s AI capabilities and contact center solutions at diabolocom.com.
