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AI fails in telecom not because of weak models, but because of operational gaps. CTOs who succeed treat AI as production infrastructure — owned, governed, integrated, and operated with the same discipline as the network itself.
Telecom AI fails at the pilot-to-production gap. Success depends less on model accuracy and more on robustness, integration, and operator trust in live networks.
Most telecom AI programmes fail before modelling begins because data isn’t ready. Volume hides fragmentation, quality issues, and weak ownership — problems AI exposes at production scale.
Deploying AI in telecom is not success. Without MLOps, models degrade, trust erodes, and systems are abandoned. Continuous monitoring, retraining, and governance keep AI reliable.
AI delivers little value in telecom without deep OSS/BSS integration. Insights matter only when they trigger actions, respect workflows, and operate as trusted operational systems.
Telecom CTOs succeed with AI when they treat it as infrastructure, not innovation — owned, governed, integrated, and operated with the same discipline as the network itself.
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