service management look like [Hint: more AI]
These days generative AI is making a huge impact on service operations – and field service asset management is no exception. Assets produce a huge amount of data. AI can track and analyze this data, use it to make predictions, and offer suggestions — helping companies to scale and prioritize.
Here are a few exciting practical applications to look forward to:
Vision prediction: Computer vision – the ability of computers to ‘see’ and understand images – has the potential to improve customer self-service for field assets. For example, a customer could upload an image of a thermostat that they are connecting to wiring. AI could then analyze the image, and let the customer know if they have america phone number list correctly connected the wires – or, if not, what to do instead.
Service forecasting: Given enough data, AI can recognize patterns and forecast the future. This means that AI can not only detect when an asset needs servicing, but even forecast when it is likely to fail – and recommend preventive maintenance, or replacement so that failure never occurs.
Assistance to the tech: Let’s say a mobile worker arrives on a site visit and encounters a problem they haven’t experienced before. The tech may message a colleague for help, or embark on a lengthy search through knowledge base articles – or, if a knowledge base article on this issue does not exist, past service records. This all takes time.