The race to deploy AI in customer service has reached a fever pitch.
CCaaS providers have embraced the technology wholeheartedly, integrating AI offerings as part of their service.
One of the most coveted uses of it is AI chatbots. These AI chatbots are now capable of handling the lowest level of customer queries, taking that task away from human agents.
Yet, while enterprises race to deploy AI agents and chatbots, most neglect the infrastructure needed to monitor these systems effectively. Not doing so leaves them at risk of churn, compliance breaches, and a weaker understanding of their customers.
But how does this lack of observability manifest? To find out, we spoke with Michael Hutchison, Principal Customer Operations Division at eClerx, about the hidden dangers of unmonitored AI and why comprehensive monitoring has become essential for any organization deploying customer-facing AI.
The Governance Gap in AI Deployment
Companies are drawn to AI chatbots because the return on investment appears logical.
"AI investments often begin with customer-facing applications like chatbots, self-service, agent assist, etc., because the ROI is straightforward,"
Hutchison explains.
However, while customer service applications of AI chatbots are seen as front-end applications, their management is not.
"On the flip side, monitoring is seen as back-office governance. While it is critical, it's far less flashy and exciting," Hutchison says.
This mindset creates a dangerous misalignment. While AI systems scale rapidly across customer touchpoints, oversight mechanisms lag months or even years behind.
The result is what Hutchison describes as organizations scaling AI without control, creating vulnerabilities that can quickly erode any initial efficiency gains through compliance risks, customer dissatisfaction, and revenue leakage.
Why Traditional Quality Assurance No Longer Works
Although many companies have quality assurance in place, they are often not equipped to handle this new reality.
Today's customers seamlessly move between phone, chat, email, social media, and messaging platforms, expecting consistent and emotionally intelligent service across every channel.
"Gone are the days of siloed interactions," Hutchison notes.
This makes manual spot-checking, where a tiny sample of interactions are taken to assess the system as a whole, insufficient.
For instance, a chatbot suggesting outdated product bundles might appear as a minor issue in sampled reviews, but across tens of thousands of conversations in different channels, it represents millions in lost revenue.
Hutchison calls this "quality blindness," a gap between what leadership believes is happening in customer interactions and reality.
When reviewing only 1-2% of conversations, critical issues remain hidden.
Similarly, early warning signs of customer dissatisfaction, such as repeated requests or subtle frustration cues, rarely surface in small samples, meaning organizations only discover problems after customers have already churned.
Beyond customer dissatisfaction, these issues could pose risks for AI chatbots being used in regulated industries.
Financial services and healthcare, for instance, face both legacy compliance obligations and an entirely new category of risks stemming from AI systems' behavior.
