Telecommunication companies (telcos) are accelerating their use of generative artificial intelligence (AI). Some estimates suggest that telcos will incorporate generative AI into their business processes over the next three years. The technology has the potential to transform many facets of telecom operations, ranging from enhancing the customer experience to optimizing network quality, driving not only value for customers but significant bottom-line impact.
But that just scratches the surface. Companies must align their adoption of artificial intelligence with broader strategic, operational, and financial goals. Those that do will gain a competitive edge.
The disruptive impact of generative AI for telcos
Roughly 94% of telecom operations believe that generative AI will have a significant impact on their businesses over the next five years. AI-powered digital agents, personalized interactions, and automated processes could enable telco operators to improve customer support, reduce downtime, and enhance operational efficiency. By leveraging AI for product support and introducing new AI-based offerings, operators are set to add value and drive growth. AI also will play an increasing role in managing risks and ensuring resiliency.
Telcos can embrace generative AI through targeted testing and learning
Successful telcos understand that achieving the true potential of generative AI will not happen by merely deploying the technology. Instead, they are adapting their technological capabilities and process architecture to fully harness the value of generative AI for their business and applying it to high value use cases. By taking a comprehensive approach — infusing AI throughout their organization and business model — they can go beyond mere process automation. They understand the importance of considering the company's vision, identifying relevant use cases, and leveraging the right enablers.
The opportunity for generative AI in telco is just beginning; adoption at scale is the long-term goal. Right now, telcos appear to be focused on testing and learning in specific functional areas, concentrating use cases to gain insights and refine implementation strategies (Exhibit 1).
Enhancing efficiency with generative AI in telco operations
Additional opportunities to leverage generative AI include knowledge management to enhance information retrieval and decision-making and co-piloting using large data sets to boost productivity and creativity and assist in decision-making and performing actions at scale.
Autonomous digital agents powered by generative AI are the next frontier for telcos. Such digital agents could act as true co-workers, managing tasks and processes within functional domains. These bots or systems can be embedded in the core of back-end operations and interact independently. The most efficient approach are agentic models, which consist of a group of specialized agents collaborating to address specific use cases. They can handle large workloads while driving better business outcomes. Team members can use everyday language to ask about project status, build software features, or seek in-field troubleshooting assistance from these agents. For instance, they can conduct root cause analysis on performance degradation and resolution, implement AI-driven Maintenance Operating Procedures that reduce maintenance costs by 25%-30%, augment project management capacity by 30%-40%, and provide predictive demand and capacity recommendations that lead to 5%-8% optimization in Capex.
Overall, the implementation of new AI tools could significantly increase earnings before interest, taxes, depreciation, and amortization (EBITDA) for telcos in the near to mid-term, with benefits extending beyond cost optimization (Exhibits 2 and 3).
Generative AI, in particular, has the power to unlock value for telcos that surpasses that of advanced analytics and traditional AI, through its ability to generate new and innovative solutions. As the technology continues to advance, generative AI is on track to become a new standard in the telecom industry. As shown in Exhibit 4, we have already observed concrete examples of telcos beginning to unlock value across diverse functional areas, including marketing, customer care, information technology, and operations. For example, they are utilizing AI to enhance technician training and development, implementing a smart AI-powered Next Best Action (NBA) and Next Best Offer (NBO) engine to boost up-selling and cross-selling efforts, and accelerating technology debt modernization by transforming legacy code into newly refactored code.
Navigating the AI transformation for telcos
Succeeding with generative AI will require building the right foundations to run models, scale use cases, ensure adoption, and optimize costs. These foundations go beyond the technical to encompass operating models, systems and data, talent and culture, and change management. Especially when it comes to preparing for the advent of next-gen operations, such as digital agent systems, telcos will need to answer a number of critical questions, as shown in Exhibit 5.
While answering these questions is essential, this alone won’t be enough to ensure successful scaling. Accelerating an AI-driven transformation will require additional key practices, such as clearly defining the business problem from the outset and regularly reviewing the sequencing of use cases. Other essential strategies will include embracing an iterative process that prioritizes progress over perfection, allowing for continuous improvement, and focusing on seamless integration rather than optimizing each individual component.
Unlocking the true value of AI is fundamentally about enhancing business outcomes. For telecommunications companies, this means reimagining business processes and operating models from end to end. This involves redefining network maintenance procedures, pinpointing pain points, designing streamlined processes, simplifying business rules, and automating activities wherever possible. By leveraging AI and generative AI, organizations can accelerate this transformation journey. However, the primary challenge lies not in the technology itself, but in establishing sustainable practices. Effectively managing change at scale will be crucial to overcoming this challenge.