Telecom operators are moving from GenAI experimentation to real-world deployments, with early adopters gaining a competitive edge.
To scale GenAI in network operations, operators must secure strong C-level backing, focus on high-ROI use cases like RAN optimisation, and invest in robust data infrastructure. Building in-house AI talent, establishing Centers of Excellence, and partnering with experienced AI vendors are key enablers.
Operators should integrate GenAI across Day 0–2 operations, track measurable outcomes, and foster a culture of agile experimentation. These steps will help overcome challenges like skill gaps, data readiness, and unclear ROI — unlocking GenAI’s full value in telecom networks, Analysys Mason’s Gorkem Yigit said in a recent report.
AI deployments
Here are the some of the examples of AI deployments by telecom operators such as Telefonica, AT&T, Verizon, Reliance Jio, China Mobile, among others.
Vodafone uses AI for network planning and to reduce energy consumption across its infrastructure.
Telefonica has implemented its in-house AI platform, Aura, to personalize customer interactions and streamline service delivery.
AT&T leverages AI for predictive maintenance and to optimize network performance.
Verizon uses AI to enhance its customer support and automate backend processes.
China Mobile and China Telecom are deploying AI for intelligent network management and to support 5G rollout.
Reliance Jio in India utilizes AI for data analytics, customer service automation, and improving network efficiency.
Deutsche Telekom employs AI to automate customer care and to manage its network infrastructure more efficiently.
These operators are integrating AI with their digital transformation strategies to improve service quality, reduce operational costs, and support the growing complexity of next-generation networks.
Tips for Deploying GenAI
#1 Secure Strong C-Level Sponsorship
Ensure GenAI initiatives are championed by senior leadership. Executive buy-in accelerates decision-making, investment, and cross-functional alignment.
#2 Start with Scalable, ROI-Driven Use Cases
Focus on high-impact, near-term use cases like RAN optimisation, energy management, and self-healing networks. Demonstrating early value builds internal momentum and supports budget justification.
#3 Build Robust Data Foundations
Invest in unified, secure, and high-quality data architectures. GenAI’s success depends on access to reliable, well-governed, and integrated data across network domains.
#4 Establish a GenAI Center of Excellence (CoE)
Form dedicated teams to define best practices, lead training, oversee compliance, and coordinate implementation efforts across departments.
#5 Upskill and Expand AI Talent
Address the skills gap by investing in employee training, hiring data scientists and AI engineers, and collaborating with academic or tech institutions to build in-house capabilities.
#6 Collaborate with Strategic AI Partners
Work with technology vendors that offer domain-specific AI expertise, hybrid cloud capabilities, and customizable toolsets to accelerate development and deployment.
#7 Create a Long-Term GenAI Roadmap
Move beyond short-term pilots by developing a clear GenAI roadmap that aligns with business transformation goals and includes metrics for performance and ROI.
#8 Design for Operational Integration
Embed GenAI solutions into existing network management and cloud platforms. Integration across Day 0, Day 1, and Day 2 operations ensures continuity and automation.
#9 Measure and Demonstrate Value Continuously
Quantify the impact of GenAI projects in terms of cost savings, network efficiency, or customer satisfaction to secure future investment and stakeholder support.
#10 Foster an Agile, Experimentation-Driven Culture
Encourage proof-of-concept trials, fast iterations, and learning from failures. A flexible, fail-fast approach helps refine models and adapt to evolving business needs.
Telecom operators worldwide are adopting AI to enhance network operations and customer engagement. But there are serious concerns about AI talent shortage. Can telecom operators invest in AI for enhancing customer experience?
Baburajan Kizhakedath
