Strategic AI Sourcing in Telecommunications Operations: A Governance-Oriented Framework for External, Vendor-Managed, Private, and Hybrid AI Models

Abstract

Artificial intelligence is becoming increasingly relevant to telecommunications operations, including service assurance, customer operations, network-related workflows, operational knowledge support, anomaly detection, automation, and decision support. However, responsible AI adoption depends not only on technical capability, but also on how AI capabilities are sourced, governed, controlled, monitored, and sustained.This paper develops a governance-oriented conceptual framework for AI sourcing in telecommunications operations. It addresses the decision gap that appears when organizations have access to public AI tools, vendor-managed enterprise AI platforms, private AI deployment options, and hybrid architectures, but lack a structured method for matching sourcing models to use-case risk, data sensitivity, operational criticality, capability maturity, auditability, vendor dependency, and strategic control.The paper proposes the Telecommunications AI Sourcing Decision Framework (TASDF). The framework consists of nine decision dimensions: use-case criticality, data sensitivity and boundary, control and customization need, auditability and explainability, integration and observability, cost and scalability, capability maturity, vendor dependency with portability and reversibility, and regulatory and contractual accountability. The framework is designed to help organizations evaluate external/public AI, vendor-managed enterprise AI, internal/private AI, and hybrid AI models in a structured and proportional way.The paper argues that no AI sourcing model is universally superior. External AI may support speed and low-cost experimentation for low-risk tasks. Vendor-managed enterprise AI may provide domain capability and faster implementation with contractual and technical controls. Internal/private AI may offer stronger control, customization, and data protection, but requires higher organizational maturity. Hybrid AI may balance speed and control, but only when data boundaries, architecture, monitoring, and accountability are clearly governed.The paper's contribution is a practical decision framework that supports responsible AI sourcing in telecommunications and similar technology-driven environments. The paper does not claim empirical validation. Instead, it offers a structured conceptual model for sector discussion, internal assessment, future research, and more disciplined AI sourcing decisions.