Agentic AI forever changes the telco operating model

HFS Research, in partnership with Amdocs, surveyed 90 Tier-1 telecom executives across Europe, Australia, and North America and conducted multiple in-depth interviews across customer experience (CX), BSS, OSS, IT, and financial operations. Our research identifies where telcos are investing in agentic AI, how it is (or isn’t yet) delivering value, what outcomes they are seeking to measure, and where operating-model shifts are required to move from incremental pilots to scalable impacts.

This paper is for senior leaders in telecommunications service provider firms under pressure to identify, invest in, and improve operations and business impact by becoming an agentic AI telco. This study examines the current state and the impact of future investments and provides a playbook based on insights from leaders in OSS, BSS, CX, and more who are seeing entirely new outcomes emerge from the use of agentic AI.

Our research revealed five areas where executive leadership can drive fundamental business change with agentic AI.

- Shift AI from proof-of-concept activity to enterprise execution priorities
  Establish a set of cross-domain outcomes aligned to business metrics, such as churn reduction, exception-free order fulfillment, revenue assurance, service resilience, mean-time-to-repair, or reduced cost-to-serve. Anchor programs to end-to-end outcomes, not isolated use cases.
- Bring governance front and center
  Agentic AI introduces a different risk profile than traditional automation. Scaling requires clear boundaries, auditability, escalation models, and controls allowing autonomy to increase without compromising compliance, reliability, resiliency, or customer trust.
- Reset KPIs from functional to “orchestration-led”
  Agentic AI delivers the most value when it can coordinate across data, systems, and workflows. As systems become more autonomous, KPIs shift from labor- and cost-based metrics to governance-grade measures such as predictive accuracy, revenue integrity, resolution effectiveness, and experience consistency.
- Invest in orchestration readiness, not just tools
  Agentic AI creates value when it acts as an execution layer, not a productivity tool. Early gains come from task automation and efficiency, but meaningful ROI emerges only when agentic AI coordinates actions across systems and functions, reducing execution friction in areas like churn prevention and revenue assurance.
- Scale through sequencing, not enterprise-wide rollout
  Leading telcos surveyed are demonstrating that the most effective adoption approach is through staged models. Start where feedback loops are the strongest and risk is bounded, such as across data pipelines and systems in the CX and FinOps value chains. Once multiple teams are seeing success, expand into BSS and OSS, where risk and rewards are highest.

While our research finds telcos are making meaningful progress with agentic AI within individual domains, the most complex challenge is moving from experimentation to enterprise coordination. Agentic AI is not a technology bet; it’s an operating-model decision. The opportunity is not to deploy more AI, but to better execute across domains through governance, KPI resets, and sequenced autonomy. Telcos that treat agentic AI as an enterprise execution model and not a set of tools are positioned to protect revenue, improve customer health, and increase resilience in structurally flat growth markets.

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Authors

Joel Martin Chief Research Officer

Suhas A R Associate Practice Leader
Don Ryan Research Fellow & Senior Advisor

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