How agentic AI can reinvent telecom field service ops

Agentic AI systems promise to solve some of telecoms’ toughest field ops challenges in key areas of workflow management, capacity planning and on-site troubleshooting.

Telecommunications businesses have long sought to modernize their famously cost-heavy, labor-intensive field service operations. Field ops can be sprawling organizations, encompassing network maintenance and repair, infrastructure, customer-premise installations and emergency response teams.

Yet despite efforts to streamline the function, telecom field ops can still account for 60% to 70% of the operating budget, according to McKinsey. At large telecoms, this can add up to billions in annual spending on labor, fleets, fuel, and dispatch and tracking systems.

This is why interest is rising among leading telecoms in the emergence of agentic AI. With the ability to autonomously plan, make decisions and take actions, multi-agent systems are increasingly seen as a way to solve some of the toughest challenges of field services operations, including workflow management, capacity planning and on-site troubleshooting. With agentic AI, telecoms have the opportunity to not just modernize field ops but to reinvent it, creating a smarter, faster and more resilient operation.

Agentic AI in a telecom world

From our work with telecom clients, we see the following areas as key places to apply agentic AI:

Workflow management and capacity planning: Optimizing workflow management is a critical factor in streamlining field service operations. With continuously shifting variables—from weather to traffic congestion to unexpected outages—dispatchers and staff schedulers often struggle to create schedules that effectively align with their available resources and real-world conditions.

Agentic AI can play a key role here, particularly when it comes to guiding incident tickets on their winding path from issue detection to resolution and closure. An AI agent can be created to generate and classify a ticket and then route it to the appropriate team's inbox. From there, another AI agent would assign the ticket to a particular team after assessing factors like the availability and skill set of the field tech staff. After the assignment is made, a human scheduler or dispatcher will step in to verify the decision before the technician is dispatched. The result: validated truck rolls and greater efficiency.

AI agents can also play a big role in optimizing dispatch operations, as they can make decisions based on staff availability, traffic, routing and fleet capacity. Agentic systems can organize and prioritize how work gets done to make the best use of field ops resources.

Field tech assistance: Agentic AI’s natural language processing (NLP) capabilities make it a welcome assistant to field techs as they plan their work for the day. Techs can access their work roster directly from their mobile devices rather than logging into a portal. And instead of querying systems using rigid or structured phrasing, they can engage with agentic AI applications in a conversational way: What jobs do I have today? What equipment do I need to carry? What locations am I assigned to? If I have four jobs in five different Zip codes, which route makes the most sense?

Equally important, agentic AI applications can be equipped with a voice interface. Techs can interact with the application as they’re loading their trucks or driving to customer appointments—and avoid the frustration and time sink of typing errors.

On-site decision making: An agentic AI system can also act as an on-site assistant. Traditionally, the field tech would need to connect to the VPN and search the company’s internal wiki pages for help with troubleshooting. But an agentic AI system could provide fast, on-demand access to troubleshooting guidance. While experienced field techs might be able to quickly resolve most equipment and infrastructure problems they encounter, newer hires often need support as they develop their troubleshooting skills.

Access to relevant information and tutorials could be built into the automated incident ticket, making it easy for field techs to quickly access guidance in internal repositories or external sources, whether they’re text, videos or images. Techs can even begin to triage a problem by opening a chat, detailing the situation they’re observing and then asking about the next steps.

How telecom can prepare for agentic AI

When it comes to successfully implementing agentic AI in their field operations, telecom companies need to consider the following:

1. Develop a data strategy. Successfully incorporating AI into field service operations is completely dependent on data. Agentic AI’s ability to make decisions and recommendations—whether to resolve problems in the field or route issues properly within the back office—requires both data and defined data policies. Data needs to be clean, centralized and easily accessible to AI agents. The adage “no data is better than bad data” is key here.
   
   
2. Define your AI strategy. Important considerations include the use of on-premises technology and AI models vs. tapping into AI models from hyperscalers such as Google, Amazon, Microsoft and OpenAI, to name a few.
   
   Any major AI investment in field ops must also be tied back to measurable business impact. Identify the metrics your company wants to improve through agentic AI, such as mean time to repair, first-time fix rate and truck roll reduction. If possible, include KPIs where agentic AI has had proven or expected impact, supported by internal pilots or industry benchmarks.
   
   
3. Prioritize responsible AI. Realizing the goals of any AI application requires the embrace of responsible AI. Successful AI means establishing guardrails that ensure the safety of the system’s users and the continuation of a trusted corporate brand. Any AI agent that handles customer data and device telemetry will need to comply with privacy laws and corporate security standards.

The future of telecom field ops with agentic AI

Agentic AI is poised to transform field operations from a cost center into a competitive advantage. Telecom companies can prepare now to move beyond incremental improvements toward an adaptive, intelligent service model.