AI Readiness in Telecommunications Operations: A Practice-Informed Framework for Process, Data, Governance, and Human Oversight

This paper proposes the Telecommunications AI Operations Readiness Framework (TAORF), a practice-informed conceptual framework for assessing readiness before deploying AI-enabled capabilities in telecommunications service operations. TAORF is organized around six interdependent readiness layers: use-case and risk framing; process ownership and decision rights; data readiness and traceability; systems integration and operational observability; human oversight and decision governance; and governance, dual-metric feedback, and continuous learning. The paper argues that AI readiness is an operational and managerial discipline before it is a technical deployment activity. It positions business analysis as a translation capability that connects AI ambition with operational problems, data requirements, decision controls, stakeholder expectations, and measurable outcomes. TAORF is not presented as a technical architecture, regulatory compliance instrument, or validated maturity model. Its contribution is to provide a structured readiness lens for responsible AI planning, pre-pilot review, and cross-functional alignment in telecommunications operations.