Rhys Fisher, Associate Editor at CX Today, sits down with Rémi Guinier, Head of AI Product at Diabolocom, to tackle one of the most pressing challenges in customer experience right now: the gap between AI investment and measurable business value.
If your contact center has been pouring budget into AI tools but struggling to prove the impact, this conversation is essential viewing. Rémi pulls back the curtain on why generic models fall short, what “Shapeable AI” actually means in practice, and how Diabolocom's quality monitoring tools are helping supervisors work smarter, not just harder.
AI is everywhere in the contact center space, but can you actually measure what it's doing for your business? Rémi Guinier breaks down the hard truths and practical fixes CX leaders need to hear.
Generic AI models are failing contact centers. Trained on clean, idealistic audio datasets, most off-the-shelf models buckle under real-world conditions – background noise, interruptions, and latency that makes real-time use impractical.
Shapeable AI puts control back in your hands. Rather than forcing teams to fit a generic tool, Diabolocom's approach lets supervisors configure evaluation criteria, summary formats, and quality grids to match their specific business standards.
Auto-calibration closes the accuracy gap fast. By grading a "golden dataset" of calls, Diabolocom's quality monitoring tool self-adjusts its prompts – moving similarity scores from ~70% up to the low-to-mid 90s against supervisor evaluations.
The three signs of a truly successful AI project. Rémi's framework is refreshingly clear: improvement on core business metrics (CSAT, FCR, AHT), strong adoption rates across the team, and measurable time saved. Hit all three and you've got a winner.
For more Customer Experience tech news visit CX Today.
