In 2024, the combined global revenue of the top 100 telecommunications operators worldwide reached $1.75 trillion. However, analysis by Appledore Research shows that the operational expenses for these operators amounted to a staggering $1.38 trillion. This massive expenditure underscores a significant economic opportunity to enhance efficiency and automate numerous tasks that currently hinder Communication Service Providers (CSPs) from improving customer service, increasing revenue, and optimising network performance.
For an investment of less than 0.5% of current operational expenses, telcos could dramatically transform digital enablement, operations, and management functions. By strategically applying investments to the right areas of the business, there is immense potential to generate additional revenue, elevate customer satisfaction, and further optimise network performance.
The market for Agentic AI is in early stages of development but moving rapidly in both realising the potential of AI and its use in a wide range of applications to optimise tasks.
Appledore Research forecasts that the global market for Agentic AI in the telecommunications market will grow from $92 Million in 2025 to $6.2 Billion in 2030.
Figure 1: Global 5 Year Forecast for Agentic AI in Telecommunication Market, 2025- 2030
Source: Appledore Research
The promise of Agentic AI
Agentic AI is the next frontier of innovation in the AI race which allows agents to dynamically adapt to new situations. The agent evolves via continuous learning, and in some cases, taking action in sequential order, much like a human being performing complex tasks. The power of Agentic AI is its ability to manage complex, multi-step tasks by interacting with structured and unstructured data sets. The promise of agentic AI is that many specialty agents can coordinate activities to complete highly complex tasks more efficiently than human experts.
In our research, we have identified six major areas in which Agentic AI is being exploited in early pilot deployments.
Figure 1: Major segments of Telecom Agentic AI Investments
Source: Appledore Research
Grow revenue
The concept-to-cash process in most CSPs remains largely manual, leading to inefficiencies, delays, and revenue leakage. The primary challenge extends beyond solution deployment to ensuring seamless data access across multiple silos, establishing efficient workflows, retraining employees, and redesigning customer engagement channels, all of which require significant time and resources. Consequently, many CSPs remain cautious about the short- and medium-term feasibility of new concept-to-cash solutions.
However, CSPs recognise the need to modernise both consumer and enterprise digital engagement stacks. They are increasingly transitioning from fragmented legacy architectures with multiple manual touchpoints to automated, cloud-based, and unified frameworks. Productised, configurable solutions are gaining traction over highly customised offerings that require extensive integration and ongoing maintenance. A clear use case in this domain is order fallout detection analysis.
In sales, Agentic AI is emerging as a powerful tool for generating personalised outreach emails, sales proposals, and customer-specific content. By enhancing the relevance and effectiveness of sales interactions, AI streamlines the sales cycle and improves engagement.
Customer care
Consumer billing inquiries still account for 30% of all calls into the contact center every month. This is due to variability in usage and service plans including roaming charges.
Customer service remains a priority for CSPs, but there is a strong focus on leveraging technology to minimise support costs. AI-driven enhancements to self-service capabilities will be a key area of interest and investment in the coming years.
Agentic AI can be used as a virtual assistant for customer service agents, providing real-time recommendations, summarising case details, and offering guidance on best responses. This improves agent productivity and ensures consistent quality across interactions. Agentic AI can also be used for agent training and quality assurance by scoring interactions and providing feedback.
Agentic AI can identify gaps in existing knowledge bases and automatically generate new recommendations based on successful previous resolutions. This keeps knowledge bases up-to-date and comprehensive, benefiting both self-service and agent-assisted support.
Network optimisation
Effective network planning and optimisation are critical in delivering efficient, reliable, and scalable connectivity services while adhering to regulatory mandates such as coverage obligations and service quality standards. This strategic process encompasses decisions on infrastructure deployment, resource allocation, and capacity management to meet current and future demands effectively.
The integration of Agentic AI into network planning introduces a transformative approach, enabling real-time adaptation to fluctuating traffic patterns and preventing potential capacity issues. By autonomously analysing vast datasets, Agentic AI can optimise network configurations, forecast demand surges, and ensure compliance with regulatory requirements, thereby enhancing overall network performance and customer satisfaction.
Network energy efficiency is a critical priority for telecom operators, particularly in wireless infrastructure, which accounts for approximately 45% of total power consumption. A key challenge lies in the inability of networks to dynamically adjust power usage in response to fluctuating traffic volumes, leading to unnecessary energy expenditure during off-peak periods.
Agentic AI presents a transformative solution by leveraging advanced pattern recognition and predictive analytics to dynamically optimise energy consumption, ensuring both sustainability and network performance without compromising service quality.
Service assurance
As 5G networks transition to Standalone (SA) to enable network slicing and ultra-low latency, real-time network status updates become critical for maintaining high-quality service. Instead of relying on periodic reports, CSPs can leverage Agentic AI to detect subscriber impacts and identify anomalies in real time. One particularly promising area of innovation is analysing how different devices perform under varying traffic conditions, allowing for more precise network optimisations.
Security
The cybersecurity landscape is facing unprecedented challenges, with over 100 million malware variants and thousands more emerging daily. The expansion of mobile devices, cloud storage, and IoT technologies has significantly widened attack surfaces, making traditional security measures inadequate. Conventional signature-based threat detection is struggling to keep pace with the sheer volume and complexity of emerging threats, highlighting the urgent need for autonomous, AI-driven cybersecurity solutions.
Agentic AI is redefining cybersecurity by enabling real-time, adaptive threat detection and response. Unlike traditional approaches that rely on predefined signatures, Agentic AI autonomously detects, predicts, and mitigates zero-day vulnerabilities and advanced persistent threats (APTs) by continuously analysing patterns, anomalies, and evolving attack behaviors.
Conclusion
Agentic AI is poised to revolutionise the telecommunications industry, driving efficiencies, automation, and intelligence across multiple operational areas. With a projected market growth from $92 million in 2025 to $6.2 billion in 2030, the technology’s impact is expected to be transformative, particularly in digital enablement, service management, IT operations, and network security.
For more on Appledore’s coverage of Agentic AI in telecom, visit Appledore Research
Patrick Kelly