The Artificial Intelligence Field Service Revolution

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Artificial Intelligence in Field Service
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What is the role of artificial intelligence in field service? In today’s customer-centric and highly competitive marketplace, organizations providing field service must meet the growing expectations for shorter waiting times, quicker resolutions, and better overall customer experience. Failure to achieve a first time fix means more downtime for the customer, another tech dispatch and lower satisfaction ratings. Technicians are therefore expected to arrive fully prepared, armed with knowledge about the history of the problem and any previous work on done on site, as well as the proper parts and tools necessary to get the job done – on the first visit.

In order for this to work, technicians must be aware of any remote diagnosis previously attempted and have access to knowledge base articles and videos, as well as the ability to collaborate with senior colleagues. Thanks to Artificial Intelligence (AI), field service management can now make all this a reality. In this article we’ll explore how three practical and innovative applications – AI-driven scheduling, AI-powered knowledge bases and AI-based visual analysis – are driving an Artificial Intelligence field service revolution.

AI-Driven Scheduling for Field Service Management

Human schedulers at field service organizations must keep on top of multiple technicians, assessing their availability and skill sets for each job. Human error, such as double booking, and mis-assignment are often inevitable, as are job overruns and cancellations. AI technology overcomes the hurdles facing manual dispatchers by automatically assigning jobs to the right technicians based on their history, skills, location, tools and availability, as well as the job priority. Aside from resulting in higher success rates, it also frees up the company’s human resources for strategic activities.

AI-powered knowledge base for Field Service Technicians

Many enterprises have invested heavily in their knowledge bases in order to provide opportunities for technicians to “self-help,” especially when remote supervisors are overloaded with requests for advice. Sometimes, however, technicians or experts simply cannot find the information they seek in a timely manner. Next-generation AI assistants are now emerging to help field service technicians and experts find the solutions they seek from the company knowledge base using natural language processing (NLP). These assistants can process voice requests from the technician such as ordering parts, rescheduling a service call or confirming that the next customer is at home.

AI-based visual analysis by Remote Experts

Technicians are often dependent on remote experts for specific aspects of the job, such as confirmation, remote guidance, quality control and safety. The expert has only one pair of eyes – a fact that often causes workflow bottlenecks, delaying job completion. Automating these processes using visual analysis tools can be a game-changer, thanks to Computer Vision AI, which helps the remote expert see the technician’s issue or the customer’s problem, driving efficiencies in the resolution process. Computer Vision AI also enables the field service technician to perform a variety of tasks in self-service/self-help mode.

Going deeper

For a deeper dive into these transformative applications, download the new eBook, Artificial Intelligence: A New Frontier for Field Service which is crammed with use cases and practical field service examples. The resource explores AI’s importance to the field service industry, including how it drives efficiencies and powers productivity with knowledge management tools and data analytics. It explains how enterprises can lower operational costs by ensuring that the right person is dispatched with the right skills and the right parts every time.

With 70% of organizations concerned about knowledge loss from a retiring workforce, it also details how organizations can close the generational gap by ensuring successful knowledge sharing between veteran technicians and novice employees.

This article was first published on the TechSee blog.

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