The Big Discussion: Artificial Intelligence and Field Service. Part 4.
In the Big Discussion we bring together a panel of industry experts and focus on one key topic within the field service sector. In the final part of this series on AI our panellists, FieldAware’s Mark Tatarsky and ServiceMax’s Amit Jain, discuss if the technology compliments a wider strategy or can it operate in a silo.
Question: Should we think of AI as a standalone technology or part of a wider technology stack?
Marc Tatarsky, SVP Marketing, FieldAware
For most applications, AI must be part of an integrated solution to provide value to the user/business. While many AI components can operate independently, it is the integration of these solutions and their decisions into a broader technology ecosystem that enables process transformation. Business systems like Field Service Automation can benefit from the power of AI and deliver improved business processes/decision making.
Also, the solutions need to be integrated with other data sources and databases to bring in input from across the organization and source “intelligence” from a broader database of signals and answers.
Integration is particularly necessary for one of the more promising areas for field service management – predictive maintenance. For an AI/ML solution to begin to work, the organization must have IoT devices monitoring the critical equipment. That data must be “brought” to the AI/ML program for analysis and interpretation on a real-time basis. Once a trigger is identified, and a recommendation is determined, that “result” must then trigger another business workflow, whether in a field service platform or an analytics platform or any other workflow used in the organization.
John Deere’s Innovation Service Group is an excellent example of how multiple data sources, workflow systems, and AI/ML systems are working together to improve how agricultural equipment is transforming how industrial farming will be conducted in the future.
Amit Jain, Senior VP of Product, ServiceMax
As mentioned above, AI is essential for dealing with the large volumes of data that we now have to process – so from that standpoint wherever there is a need to interpret data, there’s a need for AI.
Ultimately, I think the days of viewing technologies in isolation are rapidly becoming a relic of the past.
In today’s modern systems and solutions, you should expect to see a wide range of technologies all sitting alongside each other working in harmony towards an improved outcome.
These include IoT, Cloud, Mobile and of course, AI.