Podcast overviewThis podcast explores where AI is already delivering measurable ROI for telecom operators, with a focus on customer care, churn reduction, network operations, field service, and fraud detection. Moderated by Will Gibson, Vice‑President of Maplewave, the conversation features Jean‑Pierre Lacroix, President of SLD, and Damien Clothier, a leading AI consultant, who will walk through practical use cases, implementation roadmaps, and governance considerations tailored to telecom leaders. Link to blog: https://phoenixai.solutions/insights/guides/ai-for-telecom-operators What the audience will learn Why telecom is one of the highest-ROI sectors for AI given its data volumes, repetitive workflows, high cost of delay, and clearly measurable outcomes. How to pinpoint the biggest “leaks” in time, margin, and customer trust across customer care, network operations, commercial teams, field service, and fraud/revenue assurance. Six proven, high-ROI AI use cases: AI-powered customer care and service resolution that reduces routine contacts, improves first-contact resolution, and cuts after-call work. Churn prediction and next-best-action retention that links risk scoring to targeted, economically sound interventions. Network operations, incident triage, and root-cause analysis that turn overwhelming telemetry into faster, smarter decisions. Field service optimization and predictive maintenance that reduce truck rolls, increase first-time-fix rates, and improve technician utilization. Fraud detection, scam mitigation, and revenue assurance that lower losses while strengthening customer trust and regulatory standing. AI-powered retail staff knowledge hubs that give frontline teams quick, reliable access to approved information in natural language. A pragmatic implementation roadmap: how to run an operational audit, select one high-ROI use case, design and execute a focused pilot, and then scale across the organization without getting stuck in “AI transformation” rhetoric. How to build AI into the operating model rather than treating it as an innovation side project, ensuring that deployments directly support service, cost, trust, and retention objectives. Key compliance, security, and governance requirements for AI in telecom, including data protection, human-in-the-loop oversight, reliability, and explainability. The ROI and customer metrics that matter most when evaluating AI success in telecom, from handling time and incident resolution to churn, fraud losses, and trust indicators. Concrete next steps operators can take in the next 90 days to move from experimentation to measurable AI impact in their own environments.
