From Data Quality To Autonomous Networks In Telecom

Interested in being a guest? Email us at [email protected] is finally forcing a scoreboard moment in telecom: some organizations are seeing massive productivity gains, while others are stuck between fear of missing out and fear of getting it wrong. From the floor at IBM Think in Boston, we sit down with IBM’s global CTO for telecom, media, and entertainment Eoin Coughlan to get practical about what separates “AI pilots” from AI that actually lands in production and earns trust.We start with the hard truth that hasn’t changed for decades: data is the bottleneck. Clean, timely, governed data determines whether AI helps you run a network or quietly amplifies bad decisions. From there we move into telecom operations where fragmented observability makes it hard to see what’s really happening. We talk about pulling signals into a unified view, using AI to correlate root causes, and keeping control as you introduce agentic AI. Autonomous networks come up as a real journey, not a magic switch: time series models for network telemetry, multiple agents that can read tickets and vendor manuals, and then automation that begins with humans in the loop and expands as trust grows.Then we zoom out to the ecosystem: hyperscaler dependence, rising sovereignty requirements, and what it means to run compliant, air-gapped platforms that enterprises can rely on. One of the biggest opportunities may be hiding in plain sight: SMEs often trust their telecom provider more than software vendors or hyperscalers, opening the door for CSPs to deliver packaged AI assistants and managed platforms. We also hit legacy modernization and 5G monetization realities, and finish with what might surprise us next, including early quantum computing use cases. If you found this valuable, subscribe, share it with a telecom leader on your team, and leave a review with the AI or automation challenge you’re tackling right now.Support the showMore at https://linktr.ee/EvanKirstel