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Telecom doesn’t need another AI science project; it needs outcomes. That’s the thread running through our conversation with kishore bobbarjung from Tupl, where explainable, agentic automation is delivering real wins: faster resolution of customer issues at a US tier one and double-digit energy savings across more than ten European networks—without sacrificing KPIs. We walk through how connecting customer signals to network data shrinks time-to-fix, how policy-aware power optimization lets radios sleep smart, and why trust and transparency with engineering teams are the real force multipliers for scale.
We dig into the design choices that matter: building AI around workflows engineers already use, surfacing every step so fixes are understandable and safe, and promoting repeatable investigations into closed-loop automations. That’s how costs stay predictable and knowledge compounds. We also unpack the tough parts—workforce concerns, governance, security, and the surprise bill that can come from token-heavy agents—and show how Tupl contains exposure by localizing processing and turning one-off agent work into durable scripts.
The roadmap gets bold: a “no tools” North Star where an agentic co‑pilot interrogates data, guides deep investigations, and then codifies proven remedies back into Network Advisor for ongoing, low-cost execution. With AI moving deeper into the RAN, we explore early gains in radio design, optimization, and critical-site resilience, as major vendors push intelligence closer to the edge. If you care about cutting OPEX, boosting customer experience, and replacing tool sprawl with a coherent AI layer, this conversation lays out a practical playbook.
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