For field-service organizations, growth can be a double-edged sword, creating as many operational challenges as business opportunities. When service networks expand, information gets distributed across teams, workflows become increasingly interconnected and coordination can often grow more difficult. Small inefficiencies when multiplied across thousands of service requests, increase costs and reduce responsiveness.

One of the largest commercial security and fire-safety providers in the United States had reached that inflection point. The organization served millions of customers through a nationwide workforce of 2,500+ field technicians and managed more than $150 million in annual field-service costs.

The pressures were visible across the service operation:

• Remote issue resolution remained at approximately 5%, making technician dispatches the default response to many service requests.
• Technicians spent significant time navigating systems and locating the information needed to resolve issues.
• Troubleshooting workflows depended on knowledge spread across multiple repositories and tools.
• Limited coordination between customer support, technical support and field teams slowed service delivery.
• Average call-handling and on-site service times were creating pressure on both customer experience and operating costs.

The cumulative impact was that revenue growth became increasingly tied to higher operating costs, making it essential for the organization to find a more efficient way to scale.

Towards Creating an Intelligent Service Operating Model

To better understand where operational friction was creating the greatest impact, Persistent conducted design-thinking workshops with business and technology stakeholders and mapped installation, inspection and repair journeys end to end.

The exercise exposed how disconnected systems, fragmented knowledge and manual handoffs contributed to unnecessary dispatches, longer resolution cycles and higher service costs. It also highlighted a recurring challenge across the service lifecycle: customer support teams, technical specialists, schedulers and field technicians often lacked access to the same information at the same time, making it difficult to resolve issues efficiently and maximize opportunities for remote support.

Rather than addressing each challenge in isolation, Persistent worked alongside business and technology leaders to identify how information, expertise and work could flow more effectively across the service lifecycle. Potential improvements were evaluated not as standalone initiatives, but as part of a broader operating model designed to improve coordination across customer support, technical support, scheduling and field operations.

The resulting business case did more than justify investment. It established a shared framework for prioritizing initiatives, aligning stakeholders around common operational goals and defining the capabilities needed to create a more connected and intelligent service operation.

Turning Strategy into Operational Capabilities

The next challenge was turning these insights into an operating model capable of improving service delivery at scale. Rather than automating individual tasks, Persistent focused on creating a coordinated service operating model. Information, decisions and work could move seamlessly across customer support, technical support, scheduling and field operations, enabling teams to operate from a common understanding of customer needs and service priorities.

The goal was to ensure that every team involved in service delivery could access the same operational context and coordinate decisions from a common understanding of customer needs, service priorities and field conditions.

That vision came together through four connected capabilities:

• A Connected Service Network: Customer support, technical support, scheduling teams and field technicians were connected through a shared multi-agent orchestration layer that improved visibility across the service lifecycle. Agentic AI-powered workflows transformed job creation, scheduling, troubleshooting, knowledge retrieval and service execution, reducing manual effort and improving operational flow across the organization.
• Increased Opportunities for Remote Resolution: A Remote Resolution Agent analyzes service requests, device signals and troubleshooting guidance to identify opportunities for remote resolution before dispatching a technician.
• The Right Expertise at the Right Moment: The Job Execution Assistant surfaces historical jobs, troubleshooting guides and contextual recommendations while technicians are on-site, helping reduce troubleshooting effort and improve first-time resolution.
• Smarter Dispatch Decisions: Proactive monitoring capabilities identify large-scale outages and signal disruptions early, helping prevent avoidable dispatches while keeping customers informed.

Together, these capabilities created a shared operational foundation across the service lifecycle. Information became easier to access, expertise became easier to apply and work became easier to coordinate across customer support, technical support, scheduling and field operations. Teams continued to make the decisions, but they could do so with greater context, visibility and alignment.

Customer support agents, technical specialists, schedulers and field technicians remained central to decision-making, while intelligent automation helped surface context, streamline routine activities and improve coordination across the service lifecycle.

From Workforce Expansion to Operational Leverage

The initiative is expected to deliver measurable business outcomes across service operations:

• Up to 25% reduction in operational costs.
• Increase in remote resolution from 5% to more than 20%.
• Reduction in average call-handling time by nearly 50%.
• Reduction in average on-site service time from 100+ minutes to approximately 70 minutes.
• Increase in technician productivity from roughly two jobs per day to more than four jobs per day.
• Improved customer and technician experience through faster issue resolution and better access to operational knowledge.

Lower service costs are the visible outcome. The more significant shift is that growing demand no longer results in increased operational complexity.

Building an Intelligent Service Network

The transformation succeeded because it addressed the underlying causes of operational complexity rather than individual symptoms. Persistent helped create a shared operational core harnessing agentic AI that connected information, knowledge and workflows across customer support, technical support, scheduling and field operations. This gave teams a common context for understanding customer issues and a more coordinated way of resolving them.

Rather than applying AI to isolated tasks, we helped the organization leverage Agent Entra ID and Microsoft Foundry to create an operating model where a shared operational core, common context and intelligent coordination worked together to transform customer service and field operations. The transformation connected teams, systems and workflows around a common understanding of operational priorities, enabling faster decisions, more efficient service execution and better customer outcomes.