There is no shortage of AI vendors promising accuracy, efficiency, and transformative results.\u00a0\u00a0<\/p>\n
Walk any contact center conference floor, and\u00a0you\u2019ll\u00a0hear the same claims repeated with only minor variation.\u00a0\u00a0<\/p>\n
Chief amongst these often-parroted promises\u00a0are\u00a0the Three Musketeers of AI benefits:\u00a0\u00a0\u00a0<\/p>\n
And yet, a\u00a0significant number\u00a0of\u00a0enterprise\u00a0CX teams are still struggling to see those promises\u00a0translate\u00a0into anything meaningful on the ground.\u00a0\u00a0<\/p>\n
The problem, increasingly, is not about which AI you buy;\u00a0it\u2019s\u00a0about how that AI was built.\u00a0\u00a0<\/p>\n
Most CX AI tools in circulation today are assembled from generic large language models (LLMs) and off-the-shelf automatic speech recognition (ASR) components.\u00a0\u00a0<\/p>\n
They were not built with contact centers in mind; they were not trained on noisy call audio, overlapping speech, or the kind of industry-specific vocabulary that comes up dozens of times per shift in insurance, finance, or retail environments; and when they hit production, the cracks start to show.\u00a0\u00a0<\/p>\n
\u201cA\u00a0demo typically runs in a controlled environment, while in production\u00a0you\u2019re\u00a0dealing with background noise from a call center floor, poor mobile connections, and crosstalk where both parties speak at once. We stress-test against these real-world\u00a0conditions\u2019\u201d\u00a0says\u00a0Th\u00e9o Deschamps-Berger, Machine Learning Research Engineer at\u00a0Diabolocom.\u00a0\u00a0<\/p>\n
\u201cGeneric models are trained on clean, controlled datasets. Real contact centers are none of those things.\u201d\u00a0<\/p>\n
That disparity between the training environment and the contact center is clearly an issue.\u00a0\u00a0<\/p>\n
How do you evaluate AI quality in a way that\u00a0actually reflects\u00a0operational performance, rather than headline accuracy figures that look great in a\u00a0vendor\u00a0pitch but\u00a0don\u2019t\u00a0survive contact with reality?\u00a0\u00a0<\/p>\n
The Metrics You\u2019re Using Might Be Lying to You\u00a0\u00a0<\/p>\n
Word Error Rate, or WER, has long been the go-to benchmark for measuring transcription quality.\u00a0\u00a0<\/p>\n
And, like any limbo dancer worth their salt will tell you, lower is better.\u00a0\u00a0<\/p>\n
This sounds Simple enough, but as a sole measure of AI quality in CX environments, it tells an incomplete story.\u00a0\u00a0<\/p>\n
A model can score well on WER and still\u00a0fail to\u00a0accurately capture a customer\u2019s name, a policy number, or a product identifier \u2013 the precise data points that need to flow cleanly into a CRM for any downstream process to work correctly.\u00a0\u00a0<\/p>\n
For Deschamps-Berger, the best way to think about metrics like WER is as \u201ca starting point, not the finish line.\u00a0\u00a0<\/p>\n
\u201cWhat we care about is whether the model can reliably recognize the entities that actually matter to the business: names, phone numbers, and account IDs. That\u2019s what determines whether the data coming out of a call is usable.\u201d<\/p>\n
This is the distinction between accuracy and usability. An AI system can be technically\u00a0accurate\u00a0and still produce outputs that create more manual work than they save, which is\u00a0arguably worse\u00a0than having no AI at all.\u00a0\u00a0<\/p>\n
Five Things That Actually Determine AI Quality in a Contact Center\u00a0\u00a0<\/p>\n
Diabolocom\u2019s\u00a0research team has developed a framework for evaluating AI quality that moves beyond standard benchmarks, built around five pillars that reflect what contact center operations\u00a0actually demand:\u00a0\u00a0<\/p>\n
Entity Recognition Accuracy<\/p>\n
Names, phone numbers, company names, and customer identifiers \u2013 these are the building blocks of a complete CRM record.\u00a0\u00a0<\/p>\n
If ASR models miss or distort them, the downstream impact on data quality is significant.\u00a0\u00a0<\/p>\n
Robustness<\/p>\n
Real-world call environments are rarely clean. Background noise, crosstalk, and poor audio connections are everyday realities, and models need to perform consistently across all of them.\u00a0\u00a0<\/p>\n
Latency and Processing Speed<\/p>\n
Diabolocom\u00a0measures both of these as RTFX, determining whether AI can keep pace with live conversations.\u00a0\u00a0<\/p>\n
A model that cannot\u00a0operate\u00a0in real time is\u00a0largely irrelevant\u00a0for in-call\u00a0assistance\u00a0or live transcription use cases.\u00a0\u00a0<\/p>\n
Domain Adaptation\u00a0<\/p>\n
This addresses one of the most persistent failure points in off-the-shelf AI. An insurance company\u2019s calls are full of terminology that a generic model simply\u00a0hasn\u2019t\u00a0encountered.\u00a0\u00a0<\/p>\n
\u201cWe essentially tune\u00a0the model\u00a0to recognize the specific language of a given industry,\u201d explains Deschamps-Berger. \u201cWithout that, transcription quality drops off sharply the moment specialist vocabulary enters the conversation.\u201d\u00a0\u00a0<\/p>\n
Evaluation on Real-World Scenarios<\/p>\n
Diabolocom\u00a0builds and tests its benchmarks using actual contact center audio rather than idealized lab conditions.\u00a0\u00a0<\/p>\n
Performance measured against real CX workflows gives a far more reliable picture of how a model will behave once\u00a0it\u2019s\u00a0live.\u00a0\u00a0<\/p>\n
Why In-House Research Changes the ROI Equation\u00a0\u00a0<\/p>\n
The question of\u00a0whether AI delivers return on investment<\/a>\u00a0often comes down to a more fundamental question: who controls it?\u00a0\u00a0<\/p>\n When models are built and\u00a0maintained\u00a0by an external provider with no CX-specific focus, refinement is slow, feedback loops are weak, and customization is limited.\u00a0\u00a0<\/p>\n For enterprise teams with specific workflows and quality standards, that dependency creates a ceiling.\u00a0\u00a0<\/p>\n Diabolocom\u2019s\u00a0in-house AI research team takes a different approach. Models are continuously refined based on production feedback, tested against unseen entities to confirm genuine understanding rather than pattern recitation, and benchmarked in conditions that mirror actual deployment.\u00a0\u00a0<\/p>\n For Deschamps-Berger, the testing on unseen entities is particularly important:\u00a0\u00a0<\/p>\n \u201cWe test our models on entities they\u2019ve never seen before, such as new addresses or unfamiliar product names. If accuracy drops, it means the model was memorizing rather than learning, and it will likely fail as soon as a client introduces something new.\u201d\u00a0<\/p>\n The operational payoff is\u00a0cleaner\u00a0CRM data, less time spent on manual correction, and more consistent performance across agent interactions.\u00a0\u00a0<\/p>\n For enterprise CX leaders trying to prove AI ROI to their boards, those are the numbers that move the conversation forward.\u00a0\u00a0<\/p>\n At some point, the industry needs to move beyond headline claims and start evaluating AI based on how it performs in real contact center conditions, against real workflows, real data, and real constraints.\u00a0<\/p>\n The model is only as good as the research that built it.\u00a0\u00a0<\/p>\n You can find out more about how to effectively measure your AI solutions by\u00a0checking out this interview<\/a>\u00a0with\u00a0Diabolocom\u2019s\u00a0Head of AI Product,\u00a0R\u00e9mi Guinier.\u00a0\u00a0<\/p>\n