There is no shortage of AI vendors promising accuracy, efficiency, and transformative results.
Walk any contact center conference floor, and you’ll hear the same claims repeated with only minor variation.
Chief amongst these often-parroted promises are the Three Musketeers of AI benefits:
- High accuracy rates
- Seamless integration
- Measurable ROI
And yet, a significant number of enterprise CX teams are still struggling to see those promises translate into anything meaningful on the ground.
The problem, increasingly, is not about which AI you buy; it’s about how that AI was built.
Most CX AI tools in circulation today are assembled from generic large language models (LLMs) and off-the-shelf automatic speech recognition (ASR) components.
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.
“A demo typically runs in a controlled environment, while in production you’re dealing 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 conditions’” says Théo Deschamps-Berger, Machine Learning Research Engineer at Diabolocom.
“Generic models are trained on clean, controlled datasets. Real contact centers are none of those things.”
That disparity between the training environment and the contact center is clearly an issue.
How do you evaluate AI quality in a way that actually reflects operational performance, rather than headline accuracy figures that look great in a vendor pitch but don’t survive contact with reality?
The Metrics You’re Using Might Be Lying to You
Word Error Rate, or WER, has long been the go-to benchmark for measuring transcription quality.
And, like any limbo dancer worth their salt will tell you, lower is better.
This sounds Simple enough, but as a sole measure of AI quality in CX environments, it tells an incomplete story.
A model can score well on WER and still fail to accurately capture a customer’s name, a policy number, or a product identifier – the precise data points that need to flow cleanly into a CRM for any downstream process to work correctly.
For Deschamps-Berger, the best way to think about metrics like WER is as “a starting point, not the finish line.
“What 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’s what determines whether the data coming out of a call is usable.”
This is the distinction between accuracy and usability. An AI system can be technically accurate and still produce outputs that create more manual work than they save, which is arguably worse than having no AI at all.
Five Things That Actually Determine AI Quality in a Contact Center
Diabolocom’s research team has developed a framework for evaluating AI quality that moves beyond standard benchmarks, built around five pillars that reflect what contact center operations actually demand:
- Entity Recognition Accuracy
Names, phone numbers, company names, and customer identifiers – these are the building blocks of a complete CRM record.
If ASR models miss or distort them, the downstream impact on data quality is significant.
- Robustness
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.
- Latency and Processing Speed
Diabolocom measures both of these as RTFX, determining whether AI can keep pace with live conversations.
A model that cannot operate in real time is largely irrelevant for in-call assistance or live transcription use cases.
- Domain Adaptation
This addresses one of the most persistent failure points in off-the-shelf AI. An insurance company’s calls are full of terminology that a generic model simply hasn’t encountered.
“We essentially tune the model to recognize the specific language of a given industry,” explains Deschamps-Berger. “Without that, transcription quality drops off sharply the moment specialist vocabulary enters the conversation.”
- Evaluation on Real-World Scenarios
Diabolocom builds and tests its benchmarks using actual contact center audio rather than idealized lab conditions.
Performance measured against real CX workflows gives a far more reliable picture of how a model will behave once it’s live.
Why In-House Research Changes the ROI Equation
The question of whether AI delivers return on investment often comes down to a more fundamental question: who controls it?
When models are built and maintained by an external provider with no CX-specific focus, refinement is slow, feedback loops are weak, and customization is limited.
For enterprise teams with specific workflows and quality standards, that dependency creates a ceiling.
Diabolocom’s in-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.
For Deschamps-Berger, the testing on unseen entities is particularly important:
“We test our models on entities they’ve 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.”
The operational payoff is cleaner CRM data, less time spent on manual correction, and more consistent performance across agent interactions.
For enterprise CX leaders trying to prove AI ROI to their boards, those are the numbers that move the conversation forward.
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.
The model is only as good as the research that built it.
You can find out more about how to effectively measure your AI solutions by checking out this interview with Diabolocom’s Head of AI Product, Rémi Guinier.
You can also discover more about Diabolocom’s AI capabilities and contact center solutions at diabolocom.com.
