en.Wedoany.com Reported - Telecom operators possess a key asset for the AI economy—proximity—but a new report from Fierce Network Research identifies four specific barriers that may prevent them from leveraging this asset.
At the core of this asset is the fact that AI training takes place in centralized hyperscale data centers, while inference—where AI performs real-world tasks—must run close to users and data. Telecom operators are already positioned at the nodes required for inference, with towers, fiber, and edge facilities spread across every market. Salim Kouidri, Senior Vice President of Field Engineering at T-Mobile, said in an interview for the report "AI and Automated Networks: Designing Telecom Infrastructure for the Inference Era" that telecom infrastructure is closer to end users than that of hyperscale cloud providers, giving operators a license to participate and win.
Barrier one is dirty data. Gabriele Di Piazza, Vice President of Product Management at Blue Planet, noted that most operators' inventory systems have accuracy rates below 50% to 60%. Operators cannot trust data they cannot verify, meaning the foundational layer for AI-driven operations is simply missing in most operators. Automation based on poor data generates wrong answers at machine speed. Research from Gartner confirms this, with 38% of organizations experiencing AI setbacks citing poor data quality as a direct cause.
Barrier two is the autonomy gap. Di Piazza stated that most operators self-rate their network autonomy levels between 2 and 3, a finding corroborated by a March report from TM Forum. The AI opportunity requires networks capable of understanding intent and acting autonomously, with humans overseeing rather than executing—corresponding to level 4 and above. The industry as a whole is still several levels behind.
Barrier three is organizational inertia. Telecom operators have traditionally been slow to act, with good reason: the requirement for five-nines reliability demands caution. But legacy OSS and BSS systems written in outdated languages limit agility, and the organizational inertia that caused operators to miss the cloud transition has not disappeared. Sid Nag, President and Chief Research Officer at Tekonyx, said operators completely mishandled the entire cloud opportunity, and if telecom operators want it, this is their second chance.
Barrier four is the consumption gap. Hyperscale cloud providers won the cloud era in part by making service purchasing and deployment effortless. Operators still cannot match this. Di Piazza said operators have long aspired to have this capability, but they are still unable to deliver like hyperscale cloud providers.
None of these barriers are permanent. Operators like T-Mobile and MetTel have already demonstrated what clearing these obstacles looks like, with MetTel's AI engine improving analyst efficiency by 83% in some years. The report's implicit warning is clear: assets alone are not enough. Operators that fix their data, climb the autonomy ladder, and move faster than their cultural biases will capture value; the rest can only watch as value flows past them.
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