Only a quarter of operators are ready to scale telecom AI
In sum – what we know:
- Ambition vs. capability – Roughly 60% of telecom leaders see AI as a future revenue driver, but only about 25% believe they can operationalize it at scale.
- Legacy and data silos – Aging networks, fragmented BSS/OSS platforms, and disconnected data slow cloud-native modernization and undermine AI models.
- Hype vs. proven value – Across industries, 43% of major AI initiatives are expected to fail, echoing the telecom execution gap.
HCLTech and Mobile World Live have released their “Telecom Pulse Survey Report” for 2026, and the big takeaway is not a flattering one for the telecom industry, when it comes to AI adoption. The report examines the global “TechCo” transformation — the long-promised shift of network operators from connectivity providers into technology-driven platform businesses — and finds a wide gulf between what telecom executives say they want from artificial intelligence and what their organizations can actually deliver.
The report calls this an “execution gap,” and the report notes that telecom leaders overwhelmingly see AI as central to their future revenue, yet only a fraction believe they’re operationally ready to deploy it at scale across networks, operations, and enterprise services.
Ambition versus readiness
According to the report, roughly 60% of surveyed telecom leaders view AI as a key driver of future revenue, but only about 25% believe their organizations can operationalize AI at scale or deliver AI-powered, cloud-native services with high confidence. That’s a 35-point gap between ambition and capability.
The stakes, at least as HCLTech models them, are substantial. The report cites the GSMA Intelligence estimate that roughly $400 billion in enterprise value potentially available to operators that manage the transition beyond basic connectivity into AI-enabled services. That figure is aspirational rather than guaranteed — more on that later — but even a fraction of it would matter to an industry watching its core product commoditize.
Around 60% still judge their AI efforts primarily on cost savings and efficiency metrics rather than new revenue or customer experience outcomes. In other words, most telcos are using AI to run the old business slightly cheaper, not to build a new one. And about one in five operators has yet to make significant investments in digital platforms and next-generation networks at all, despite the stated AI ambitions. It’s hard to become an AI-native company on infrastructure that was never built for it.
Barriers to scaling AI
So what’s actually holding operators back? The most-cited culprit is slow product and service innovation — 49% name it as the primary barrier to capturing higher-value AI revenue. Approximately 80% of companies in the sample launched fewer than five new digital products in the previous year. For an industry that talks constantly about platform transformation, that’s a thin pipeline.
Legacy technology sits underneath much of the problem. Heavy reliance on aging network and IT systems limits cloud-native modernization, which in turn rules out the things AI is supposed to enable — dynamic network optimization, real-time analytics, on-demand services. Fragmented BSS and OSS platforms make it worse, leaving data siloed and inconsistent across functions. AI models are only as good as the data feeding them, and in most operators that data lives in a dozen disconnected places.
Then there’s talent. Operators face severe shortages of AI/ML specialists, data engineers, and cloud architects, and they’re competing for those people against hyperscalers and big tech firms that can generally outbid them. That’s not a fight most telcos are positioned to win on compensation alone.
The softest barrier may be the hardest to fix. 49% of respondents describe their internal transformation culture as only “moderate” — a polite way of saying organizational inertia, change-averse mindsets, and a workforce understandably wary of what automation means for their jobs. You can buy technology and, with enough patience, hire talent. Culture is slower.
The shift from telco to TechCo
The report’s prescription is essentially that it’s time for carriers to move away from commoditized connectivity and towards a more scalable AI structure. That’s a harder shift than it sounds, because it touches org charts and budgets rather than just technology stacks.
Partnerships get an interesting emphasis here. Nearly half of surveyed operators view partnerships as critical to innovation, and the framing goes beyond standard vendor relationships toward orchestrating a genuine ecosystem of hyperscalers, software vendors, and vertical partners. The implicit admission is that operators can’t own the whole stack, and probably shouldn’t try.
Threaded through all of this is the report’s central argument — that AI readiness, spanning infrastructure, service innovation, ecosystem strategy, and value realization, will be the differentiator between future market winners and laggards. Adoption alone won’t cut it. Plenty of operators have deployed AI somewhere. Far fewer can scale it.
Hype versus realism
Some skepticism is warranted here, and to its credit, the broader research context supplies plenty of it. The execution gaps in telecom mirror what’s happening across enterprises generally. HCLTech’s own cross-industry report from May 2026 found that roughly 43% of major AI initiatives are expected to fail or fall short of expectations. An Economist TMT report from July 2026 identified what it called a “value paradox” — 91% of organizations believe their AI investments deliver results, but only about 33% can consistently measure the business value generated. Believing in AI and proving it works are, apparently, very different things.
The report also notes weak AI governance as a risk, yet telecoms are among the most heavily regulated businesses around. The push for rapid AI deployment will inevitably run into strict regulatory, privacy, and ethical constraints — and some of what looks like organizational sluggishness may actually be justified caution.
Still, the underlying warning holds regardless of who commissioned the study. The report frames this as a “once in a generation” opportunity for telecoms, and closing the execution gap over the next two to three years will require concrete shifts in governance, talent strategy, and cloud-native modernization — not just more pilots. If operators can’t operationalize AI quickly and securely, hyperscalers and cloud providers are already positioned to take the high-margin, network-adjacent services market for themselves, leaving telcos as the low-margin pipes underneath someone else’s platform.
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