|Companies can utilize big data and AI since many field workers are retiring / Photo Credit: guruXOX (via Shutterstock)|
According to a 2018 report by ManpowerGroup, 70% of firms interviewed in a survey stated that they expected a skills shortage in field service personnel over the next decade, as cited by Mary Shacklett of online trade publication TechRepublic. Field service workers refer to individuals who travel to other locations “to repair goods and services onsite.” Some factors include an aging and retiring workforce and a lack of enthusiasm for millennials to be employed in the field service industry.
Arka Prava Dhar, CEO and founder of field service AI provider Zinier, explained that field service begins with work orders, in which the job order is done by the field service worker. Then, the work is either forwarded or verified by an expert. Large corporations that employ 100,000 field service workers are often seen working with paper. But AI can help them with their jobs.
For instance, big data like photos in everyday workflows to bolster the operational effectivity of teams deployed in the team. If a technician is stumped with a problem, they can just take a photo using a mobile device and forward it to headquarters. An AI or an expert will make a diagnosis and help the technician in real-time.
Since many experienced field workers are retiring from their careers, companies can use AI and big data to address problems quickly, thereby improving customer satisfaction. Dhar noted that employees are apprehensive about the prospect of AI taking over their jobs. However, field service workers become supportive of AI after seeing its capabilities of augmenting their jobs, allowing them to focus on more challenging tasks.
The AI operates on big data grounded on a “set of rules and intelligence that have been gleaned” from experts. Developing a detailed set of rules and troubleshooting techniques can take three to six months to complete, as the field workers need to train the AI with the information they obtained from experts, Dhar said.
Moreover, Dhar continued that Zinier will continue to refine the algorithm and how they operate on big data until a 95% level of accuracy has been attained. If it does, then AI is ready to deployed for field service support.