Telcos today operate in a world of relentless digital complexity: customers switch between apps, chat, voice, and even video within seconds. Legacy IVR menus and rigid systems can’t keep up. Telecom routing needs to evolve, and fast.
AI, despite its challenges, could hold the answer. Intelligent systems can now transform raw signals about a customer’s recent app action, account status, device type, and more, into an optimal connection decision. They can choose whether an interaction goes to an AI agent, a human specialist, or a self-service flow.
It’s the start of a more effective approach to journey orchestration, with AI forming the brain behind connected workflows that support both customers and agents.
Further reading:
- Why Real-Time AI is Becoming Critical for CX
- How Can Contact Centers Reduce AHT with AI?
- Proactive CX Use Cases to Explore in 2026
Why Are Old IVR And Routing Systems Causing Problems For Telcos?
Telcos can’t digitize chaos. Many feel pressure to adopt AI and advanced automation, but rushing to layer new tech over legacy workflows just accelerates inefficiency. Simplification needs to come before automation, and smart routing can help.
Intelligent routing, combined with composable CX architecture, smart journey orchestration, and even CPaaS addresses the challenges that stunt telecom journeys, such as:
- High Churn from Low FCR: Every extra handoff or failed first contact damages. Research shows customers whose issue is resolved on the first call are twice as likely to trust and recommend the brand. AI telecom routing reduces unnecessary transfers by predicting intent and matching to the right resource at hello.
- Complex Handoffs Across Departments: Telecoms often operate with fractured silos: network operations, billing, device support, and customer care each use different systems. When routing fails to carry context, everyone feels the impact of repetition. That’s why companies like Genesys are already embedding journey management straight into CCaaS.
- Overwhelmed Agents & Cognitive Load: Agents bear the brunt of routing errors. A poorly routed contact forces context lookups, redundant questions, and tool juggling. Over time, this degrades performance and morale. When customers arrive with context, automated summaries, or suggested next steps, agents operate fluidly.
- Handling Fraud and Security: Telecom networks face unique fraud vectors: abused CPaaS APIs, spoofed calls, and SIM swap attempts. What’s needed is routing logic that can spot suspicious traffic in real time and divert it to validation or risk queues.
What is AI Routing in Telecom?
Every telecom routing decision is a complex blend of signals, predictions, and governance. It’s where raw data meets business logic. The heart of AI routing is the decision engine: inputs, logic, outputs, and guardrails.
Real-world success stories show that AI routing creates a measurable change in how telcos resolve issues, serve customers, and optimize costs. Across different markets, routing intelligence has become the engine behind better FCR, higher agent productivity, and stronger CX consistency.
Looking for a deeper insight into what AI and automation can do for contact centers? Start here.
Verizon: Predictive & Assisted Routing
Instead of routing purely by call type or menu selection, Verizon now uses predictive context, analyzing device telemetry, contract data, and family-plan eligibility before a call reaches an agent.
These signals help virtual agents to handle straightforward queries autonomously while routing more nuanced interactions to the right specialist. During live calls, AI copilots support frontline staff by surfacing real-time insights, such as whether a customer is due an upgrade or missing loyalty perks.
The outcome is a dual-benefit system: fewer misroutes for customers and less manual data-scraping for agents. Verizon’s digital-care leadership has publicly stated that this combination of routing intelligence and GenAI assistance enables “meaningful human interactions” rather than scripted exchanges.
Telecom Italia: Back-Office Task Routing
Telecom Italia realized that broken handoffs between its contact center and back-office teams were eroding first-contact resolution and slowing response times.
By extending AI telecom routing beyond voice queues into back-office workflow management, Telecom Italia re-architected how every customer task is allocated. Using Enterprise Workload Management within its Genesys platform, each task is dynamically assigned to the best-suited agent or specialist based on skill, load, and priority.
The results were a 6 percent productivity gain and a 75 percent reduction in unresolved customer issues. Agents can now handle multiple task types efficiently, while customers see faster closure on complex cases.
T-Mobile & Ericsson: Provisioning Flow Routing
T-Mobile US faced a problem familiar to large telecoms: process sprawl. Across more than 140 applications, order fulfillment suffered an alarming 17 percent fallout rate. Traditional analytics couldn’t trace the cause.
In partnership with Ericsson, T-Mobile deployed AI-powered process routing and monitoring, integrating data from CRM, order management, and network systems. Machine-learning models mapped each order’s journey and automatically re-routed tasks when bottlenecks appeared.
The impact was a 95 percent reduction in order fallouts, 90 percent faster issue identification, and activation times cut to under five minutes for nearly every order.
EE: Analytics-Driven Sensitive Routing
EE, one of the UK’s largest telecom brands, took a different path toward intelligent routing: it began with deep interaction analytics rather than new infrastructure. Using NICE Interaction Analytics, the company wanted to see intent clusters, customer sentiment, and even emotional tone.
From this data, EE created dynamic routing policies to support vulnerable customers and high-risk situations. When AI detects signals of distress, calls are routed to agents trained in empathy and sensitivity. Fraud patterns are similarly flagged and redirected to specialist teams.
Beyond compliance, this approach improved coaching and sentiment as performance indicators. EE also reported sharper quality assurance and better detection of regulatory risks.