How ADT Uses UiPath to Cut Technician Confirmation Calls from 15 Minutes to Under 2

ADT is the largest residential and commercial security company in the United States, serving millions of customers with intrusion detection, fire monitoring, smart home technology, and field service operations. The scale of ADT’s operation is significant: finance processes, field technician scheduling, call centers, and customer support each run at high volume, and many are time-sensitive and SOX-regulated. As transaction volumes grew, the company needed to increase throughput and accuracy without replacing its existing technology stack.

Before automation, routine but high-stakes processes consumed substantial manual effort. After installing security equipment, field technicians spent 15–20 minutes on confirmation calls to verify job completion and reconcile records across multiple backend systems. This created delays for technicians moving between jobs and added load to call center staff who had to field and log those calls.

ADT deployed the UiPath Platform to address these friction points. UiPath Robots integrated across legacy systems, modern applications, and APIs without requiring system replacement. For the technician confirmation workflow, a simple tablet interaction now triggers a Robot to complete verification automatically in under two minutes. ADT Express, built using UiPath Apps, consolidates multiple system tasks into a single interface, reducing the number of application switches needed to resolve customer issues.

As the automation program matured, ADT began layering AI on top of RPA. AI models interpret variability in inputs and produce structured, governed outputs that feed into UiPath Robots, allowing expansion into more complex workflows while preserving the reliability and SOX compliance the business requires. The organization is now scaling toward 13 planned AI agent use cases for 2026.

With more than five years of enterprise automation running in production, ADT has embedded UiPath into its operational backbone. The next phase—combining agentic AI with the existing automation layer—is positioned to extend the same efficiency gains to more complex, judgment-intensive processes across the enterprise.