Artificial intelligence (AI) is having a huge impact on businesses in a variety of industries, including high tech. ServiceMax, a company that provides cloud-based field service software to help organizations manage contracts, scheduling, and parts, is a case in point.
"We're constantly exploring different options and paths to incorporate artificial intelligence and machine learning into our product offering," said Indresh Satyanarayana, chief architect at ServiceMax, which was acquired by GE Digital in November 2016 to enhance is Industrial Internet of Things (IIoT) capabilities in field service.
"For the field service industry in particular, AI holds interesting implications for changing how we operate our businesses," Satyanarayana said. "We already see the practical applicability in many different areas of AI -- from enabling natural language processing in our mobile applications to realizing autonomous field work by enabling AI-based decision making."
In servicing equipment and keeping it running right, AI has the ability to directly impact how field technicians complete jobs and managers optimize work processes, Satyanarayana said.
The creation of a product that leverages AI and machine learning faces a host of challenges, however. Understanding how humans and machines can work together is never going to be easy, Satyanarayana said.
"AI and machine learning concepts operate at a fundamental level in product and feature design, and can sometimes lead to false positives if the data being fed into it isn't robust," Satyanarayana said. "We can't treat those concepts as peripheral utilities or one-trick ponies. Instead, we must take a holistic view of how we can enable them in our products."
In addition, AI and machine learning take a somewhat tangential approach compared with conventional software design and development. "The mindset shift has to happen sooner," he said.
AI has been gaining buzz for many months, but before jumping in headfirst into it, companies need to consider a few different areas of their business and whether AI will work for them, Satyanarayana said.
"The success of AI counts on the quality of the data that's fed through its system, but the magnitude, depth, and heterogeneity of it gets multiplied in the context of field service," Satyanarayana said. "No two businesses execute their processes in the same way -- especially in field service -- so it's important to make sure that your data is understood in a particular context and the insights derived from it can cater to both the process and the business."
Furthermore, individuals with different roles come and go through the flow of AI-supported programs, whether that's technicians in the field with mobile devices, back office users, dispatchers, or others. "Even machines are connected to enterprise systems" via the Internet of Things (IoT), Satyanarayana said. "It's difficult to make the engagement across all of these moving parts optimized based on continuous feedback mechanisms."
Finally, because decisions are made on account of data, making sure the decisions made by AI are accurate is crucial. "It impacts the evolution of field service organizations, whether that's how a field tech is dispatched or whether a larger business process is deployed," Satyanarayana said.
"These decisions must be made by an intelligent system, even if it's artificial. So, while enterprise data is valuable, it's tough to make computers process data the same way that people do. It takes a major mindset change that isn't always straightforward for organizations."
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