AI in Field Service: From Buzzword to Business Impact
It’s hard to think of a field service technology that will see a faster adoption rate than artificial intelligence (AI).
For field service organizations using automated schedule optimization, machine learning (ML) will help schedule over two-thirds of field service work by 2025, according to Gartner. In 2020, that share was under a quarter. Quite a jump, wouldn’t you say?
In fact, according to a Worldwide Business Research (WBR) survey, 72 percent of respondents said their field service organizations are already leveraging AI and/or ML in some form, and another 27 percent are planning to implement the technology the next 12 months.
Even with the hype surrounding AI in field service management (FSM) solutions, the actual applications of it are, for now, limited to certain types of AI. Still, you often see broad categories of automation getting labeled as “AI” simply for streamlining manual methods. The overuse of the term can be confusing, so businesses in the market for automation technology are wise to ask vendors, “Is that really AI?”
So, let’s cut through some confusion around AI—what it is and isn’t in FSM. We’ll also look at some real-life use cases that can improve your field service operations.
CLARIFYING AI IN FIELD SERVICE
AI can apply to FSM solutions in a variety of ways. But first, important to understand a fundamental split—between narrow AI and general AI .
NARROW AI: WHAT WE SEE IN FSM TODAY
Narrow AI relies on algorithms and programmatic responses to “simulate intelligence.” It simply has the ability to recognize words, actions, images and even voice, on things that have already been programmed. If it encounters something that isn’t programmed, it can’t take a specific course of action.
That describes what FSM products do today. When they’re automating dispatch and scheduling, or even routing technicians using real-time traffic conditions, they’re executing a rules-based approach. The solutions aren’t “learning” or “adapting” without human intervention. This isn’t a bad thing; it just means there’s even more potential to automate various components of the FSM solution.
GENERAL AI: WHAT FSM COULD LEVERAGE TOMORROW
General AI (also called deep AI or strong AI) can learn or mimic human intelligence. It has an inference engine, which is the AI component that makes decisions. What an inference engine does is take a knowledge base and apply logical rules to it to deduce knowledge. This is how AI learns and infers new results or decision paths that weren’t previously programmed.