A question I hear consistently from telecom leaders is: “We are piloting agentic AI, but how do we know if it’s actually working or just expensive automation with a new label?”
It is the right question. And the fact that senior leaders are asking it tells me two things – the technology is real enough to invest in, and the industry is still figuring out how to separate signal from noise.
After leading AI-driven transformation across large-scale digital platforms and building AI-powered operations engines that now run across Tier-1 operators globally, I have seen both sides of this question up close. Here is my honest practitioner’s view on what is real, what is hype, and where this is genuinely going.
What Is Actually Working Today
Intelligent incident triage and self-healing operations is where agentic AI is delivering measurable, provable ROI right now. Not in pilots — in production.
At the core, what works is an agentic system that monitors real-time telemetry across a multi-vendor OSS environment, correlates anomalies across layers, predicts failure patterns before they impact SLA, and in many cases triggers automated remediation without human intervention. The result at scale: approximately 20% OPEX reduction and 30% fewer customer-impacting incidents.
The key word is orchestration. The agent doesn’t just detect, however it decides, acts, and learns from the outcome. That feedback loop is what separates agentic AI from the rule-based automation telecom has been doing for 20 years.
AI-driven service desk automation is the second area with proven results. Multi-agent workflows handling first-line customer operations — routing, knowledge retrieval, resolution suggestion, and escalation — with human-in-loop checkpoints at the right decision gates have delivered operating cost reductions of ~40% while maintaining ~98% SLA adherence. The agents don’t replace the human team. They make the human team dramatically more effective.
What Is Hype Right Now
“Full autonomy” claims — any vendor telling you their agentic AI system can run your network operations end-to-end without human oversight is either misleading you or hasn’t deployed it at real scale. Autonomous decision-making in complex multi-vendor telecom environments without robust governance is not production-ready. It is a demo.
Generic LLM deployments labelled as agentic AI — dropping an LLM on top of your ticketing system and calling it an agentic operations platform is not agentic AI. True agentic systems have memory, feedback loops, dynamic planning, and the ability to decompose complex goals into multi-step actions. Most of what is being sold today is sophisticated autocomplete.
ROI projections without baseline measurement — I have seen decks promising 60–70% cost reduction from agentic AI without a single baseline metric defined. If you cannot measure what you have today, you cannot prove what AI changed tomorrow. Demand baseline-first conversations with every vendor.
The Governance Problem Nobody Talks About
This is the most underestimated challenge in enterprise agentic AI deployments , and it is where most programs quietly fail.
When an autonomous agent makes a decision that triggers a network change, causes a billing anomaly, or denies a customer request then who is accountable? How do you audit it? How do you prove to a regulator that the decision was explainable?
Governance frameworks need to be designed before the first agent goes into production and not retrofitted after. This means defining human-in-loop thresholds, implementing audit logging at every decision node, running bias reviews on the models, and ensuring your agentic platform is aligned with emerging AI regulation frameworks like the EU AI Act.
Telecom operates in one of the most regulated industries in the world. Agentic AI without governance is not innovation – it is liability.
What Is Coming in the Next 18 Months
Multi-agent coordination across the full OSS/BSS stack — today most deployments are single-agent or loosely coupled. The next wave will see agents across network operations, billing, customer management, and field services operating as a coordinated system — sharing context, passing tasks, and making collective decisions in real time.
RAG-powered knowledge management — telecom organisations carry enormous institutional knowledge locked in runbooks, incident histories, and engineering documentation. Retrieval-Augmented Generation (RAG) architectures will make this knowledge queryable and actionable in real time, dramatically reducing mean time to resolution and accelerating engineer onboarding.
Predictive SLA governance — instead of reacting to SLA breaches, agentic systems will predict breach probability hours in advance and autonomously trigger preventive actions. This is the shift from reactive managed services to genuinely proactive operations.
Edge AI for RAN and network operations — as 5G and Open RAN deployments scale, agentic AI will move closer to the network edge, enabling real-time autonomous optimisation of radio resources, interference management, and capacity allocation without round-tripping to centralised systems.
What I Learned Deploying This at Scale
Start with a well-defined, high-frequency, high-volume operational problem, not a flagship transformation. Prove the loop: detect -> decide -> act -> learn. Get your governance framework right before you scale. Measure everything against a defined baseline.
Agentic AI in telecom is real. The results are real. But the gap between what is being promised in boardrooms and what is actually deployable in production is still significant.
The operators who will win in the next three years are not the ones who invest the most in agentic AI. They are the ones who govern it the best.
