Picture this: A customer calls his telecom provider after losing mobile service abroad. The artificial intelligence assistant verifies the account, asks the right questions, and appears to handle the issue well. Then the journey breaks down. It gives outdated advice, transfers the call without context, and sends the customer to the wrong team. Every system might look healthy, but the customer has not reached the right outcome.
I see this as a major gap in contact center AI. Companies monitor individual systems well, but they do not always prove that those systems work together across the journey. That is the difference between visibility and assurance. Visibility shows what each system reports about itself. Assurance shows whether the customer can complete the task.
That matters as AI spreads across networks, operations, and customer support. The GSMA has identified fragmented data and legacy infrastructure as barriers to scaling AI, while Fierce Network reports that operators still struggle to connect data and operations. AI can help, but it also creates more places for journeys to fail.
AI Adds Intelligence and Dependencies
Traditional interactive voice response (IVR) systems follow defined paths. Voice AI is more variable because one request might pass through a carrier, cloud contact center, speech recognition service, language model, identity platform, billing system, and agent desktop.
A recent ITU report on generative AI points to the need for telecom-specific assessment methods and risk controls. A stale knowledge entry, routing change, or delayed API response can send an interaction in the wrong direction, even when components remain online. From the dashboard, everything might look healthy. From the customer perspective, the experience failed.
Start Where the Customer Starts
Most monitoring works from the inside out. Customers experience calls from the outside in, entering through a phone number, carrier, language, device, and local network condition. Journey testing should reflect that starting point. It should place calls through in-country routes, present realistic requests, and confirm whether the right outcome is achieved.
The question is not simply whether the AI answered. Did it understand the request, retrieve the right information, follow the correct policy, and complete the transaction? If it could not continue, did it escalate properly?
An ITU standard describes AI-based customer experience management as a closed loop involving evaluation, analysis, assurance, and optimization. Testing the whole journey also gives operations teams better evidence, allowing them to identify the market, route, or handoff where the failure appeared.
Launch Is Not the Finish Line
AI systems can change, even when no application code is released. Models are updated, prompts are refined, knowledge bases are refreshed, and business policies are revised. Carrier routes and downstream APIs could change independently.
A prelaunch test only confirms that the journey worked under one set of conditions at one time. The National Institute of Standards and Technology (NIST) framework emphasizes testing, evaluation, verification, and validation throughout the AI lifecycle.
One approach is to define a journey contract for each critical task. It should set out the intended outcome, information the AI can use, authentication requirements, acceptable response times, escalation conditions, and fallback behavior.>
Those contracts can guide pre-release regression testing, post-change checks, and scheduled live testing. They should also cover unavailable systems, after-hours calls, and requests outside the AI's authority.
Escalation Is Part of the Product
An AI-supported journey should not be judged only by how many interactions it automates. It should also be measured by what happens when the AI cannot complete the task.
A reliable service needs to recognize low confidence, repeated misunderstanding, technical failure, and policy limits. It must then transfer the customer to the right destination, preserve the context already collected, and offer another route when the preferred option is unavailable. These paths are often treated as exceptions, but customers need them to work. If AI traps customers with complex problems in repeated prompts, it has moved the failure rather than removed it.
TM Forum,& a global industry association for service providers and their suppliers in the telecommunications industry, argues that operationalizing AI requires clear decision boundaries, confidence thresholds, rollback procedures, and continuous monitoring. Those controls matter only when operators can prove they work across real customer journeys.
Measure the Outcome
Telecom leaders need a reliability view above individual system health. They should know whether customers complete journeys across markets and routes, whether escalations reach the right destination, whether context is preserved, and whether fallback processes work.
Observability explains what individual systems are doing. Continuous journey testing shows whether those systems are collectively delivering the service promised to the customer.
The industry needs more than intelligent systems. It needs proof that those systems work together under real operating conditions. Operators that close this assurance gap will deploy AI with greater confidence, identify failures earlier, and maintain customer trust.
Satish Barot is co-founder and chief technology officer of Klearcom.