Are telcos truly slow in adopting AI? Are they really sitting on a gold mine and using it as a chair? While even the banking industry has moved fast to adopt AI, why does the telco industry lag behind? The basic adoption we see in telcos is still around network fault detection, customer service chatbots, and fraud detection. Are there any telcos doing anything different and what opportunities do they have?
Are telcos truly slow in adopting AI? Are they really sitting on a gold mine and using it as a chair?
Telcos are not bad at technology
Let us address each of these questions one by one. First, the notion that telcos are bad at technology is baseless. These companies run some of the most complex, real time distributed systems on the planet. A major carrier is managing millions of concurrent connections, routing packets across continents in milliseconds, orchestrating spectrum allocation in real time. Telco people are not dumb.
The issue is something more subtle. Telcos are bad at a very specific kind of technology: the kind that threatens to change who they are, not how they operate. And AI does exactly that.
The mental model baked into telcos for over a century is this: we own the pipe, you pay to use the pipe. Everything else is noise. The business model is subscription based access. The competitive advantage is geographic coverage and spectrum licences, neither of which you can code your way out of or into. It is how this industry was built.
Banks had the same problem for years. They thought they were in the vault business. But they eventually realised they were actually in the trust and financial flow business. The ones that figured it out early became industry leaders. The others were cautionary tales in economics and MBA programmes.
The real blocker: legacy systems and a lack of urgency
Every conversation about telco and AI eventually hits the same wall. Our systems are old. And that is true.
Many major carriers run BSS and OSS stacks that were architected before the iPhone existed. There are transformation programmes underway and yours truly has consulted on some, but there is a long way to go. The customer record for your phone plan might live in a system that communicates via SOAP over a VPN that has not been meaningfully updated in the last decade.
You cannot just swap in a transformer model when your data is scattered across seventeen siloed systems with inconsistent schemas. Any serious AI programme at a telco starts with a data infrastructure problem. That is not an easy or cheap problem to solve.
But every industry has legacy debt. Healthcare has HIPAA constrained HL7 messages flying between systems that look like they were built in 1996. Financial services run COBOL that no one alive fully understands. I have provided consulting services to an e-commerce company whose billing was built on a system that only one person, close to retirement, knew how to configure. And these industries still found ways to move on AI.
In telcos, the existing business still works. ARPU is declining slowly, not catastrophically. There is no burning platform. So the hard infrastructure work keeps getting deprioritised to the next quarter, if not the next financial year.
What AI looks like in most telcos today
It is not that no AI implementation exists. Network fault detection and predictive maintenance were among the first use cases. Churn prediction is another, with propensity models flagging customers likely to leave. Still valuable, but not exactly frontier stuff.
Then there are chatbots. We know the customer service chatbots. We are also aware how mediocre they have been. Addressing actual customer issues has always seemed less of a driving factor than reducing headcount.
Fraud detection, covering SIM swap, international revenue share fraud, and account takeover, is probably the best executed AI vertical in telco. Real time scoring on call patterns and authentication signals. Here, the financial incentives were direct enough to drive genuine rigour.
- Network operations: fault detection and predictive maintenance
- Customer care: chatbots, often mediocre in practice
- Retention: churn and propensity models
- Security: fraud detection covering SIM swap, revenue share fraud, and account takeover
Do you see anything missing? There is almost nothing about building new revenue streams. Nothing about turning the network into a platform. Almost nothing about leveraging data assets in ways competitors cannot replicate. The data telcos hold is enormous: call records, location traces, network telemetry, device metadata, payment histories, and customer support logs. It genuinely frustrates me that this is not leveraged sufficiently.
There is almost nothing about building new revenue streams.
The opportunity surface: five plays hiding in plain sight
The opportunity is enormous and mostly untouched. These are not research problems. They are integration and will problems.
What leading operators are doing differently
Credit should be given where it is due. Some operators are already proving this can be done at scale, not as pilots.
If you were designing the perfect company to be infrastructure for the AI era, you would describe a telco.
The bottom line
The telco industry has what it needs to be the centre of AI infrastructure: nation scale sensor networks, optical fibre in the ground, data centres in the right places, regulatory trust that hyperscalers will never have, billing relationships with businesses that exist, and edge compute locations that agentic AI will increasingly depend on.
And still, most of the industry is spending time optimising EBITDA and running pilots that never become products.
Telcos have survived every platform shift since the telegraph. The internet was supposed to make them irrelevant. The smartphone was supposed to commoditise them completely. They are still here, still essential, still embedded into the fabric of how the world communicates. That is not luck. That is deep infrastructure that compounds over time.
The AI era does not diminish that. It amplifies it. Once telcos stop thinking of themselves as pipe companies and start becoming the physical layer of the AI economy, they will be extraordinarily well positioned. Not despite the infrastructure, but because of it.