For multi-market telecom groups, CX transformation rarely fails because of a lack of ideas. It fails because every market builds differently.
Each OpCo may run different contact center processes, backend systems, service journeys, escalation rules, and customer support models. As a result, even when one market proves a successful automation use case, scaling it across the group can become slow, expensive, and operationally fragmented.
This telecom group needed a more repeatable approach.
The objective was to build a live AI-powered customer service journey in one anchor market, prove the operational model, and create a blueprint that could be replicated across the group. The first focus area was TV technical support — a high-volume journey where subscribers often contact support for troubleshooting, service restoration, device setup, or connectivity issues.
By applying Sense AI, the group could move from one-off automation to a scalable AI-first CX capability.
High-volume technical support journeys were creating avoidable contact center pressure.
Subscribers were reaching support teams for routine TV service issues that could often be diagnosed, triaged, or resolved earlier in the journey. Human agents and technical teams were spending time on repetitive discovery, while escalation paths often required additional context gathering before the issue could be handled effectively.
At group level, the larger challenge was scalability.
A successful automation use case in one market could not remain a local project. The group needed a model that could be extended across multiple OpCos without rebuilding every journey from scratch.
Key challenges included:
The opportunity was clear: build once, prove the value, and create a repeatable model for group-wide AI service expansion.
Sense AI was used to create an AI-first service layer for technical support automation.
The first live build focused on TV technical support, giving subscribers an AI-led pathway to describe the issue, receive guided troubleshooting, capture technical context, and reach the right next step without immediately entering an agent queue.
The solution was designed as a repeatable group blueprint, not a single isolated automation project.
The initial live build
Expansion pipeline
The anchor use case established a measurable baseline for group replication.
The live build supports a TV technical support journey with approximately 36,000 calls per month, a 50% containment target for the v1 build-ready baseline, and an estimated $324K in annual savings from the single OpCo target.
More importantly, the project created a reusable model for scaling Sense AI across the group.
Instead of treating each market as a separate transformation effort, the group can use the first deployment as a blueprint. The same approach can be adapted across additional OpCos, service journeys, channels, and backend environments.
This changes the transformation model from local automation to group-level replication.
Group Replication Model
By standardizing the AI service model, the group can accelerate rollout, reduce duplication, and create more consistent customer service journeys across markets.
For large telecom groups, AI customer service cannot remain a collection of local experiments.
The value comes when one successful use case becomes a repeatable capability.
Sense AI helps telecom operators build that capability by combining AI-led resolution, reusable journey design, backend-connected automation, and group-level scalability.
The result is a CX model that can start with one high-volume journey, prove measurable value, and expand across multiple markets.
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