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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.



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 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 (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 new knowledge. This is how AI learns and infers new results or decision paths that weren’t previously programmed. 

Almost none of the FSM vendors today have an inference engine in their offerings. That’s why it’s more accurate to say FSM is mostly based around narrow AI, not general AI. While narrow AI powers a vast improvement over manual processes, we’re still far from leveraging the true power of AI in field service.

RELATED REPORT: How Field Service Organizations Can Build an Industry-Leading Customer Experience



Let’s also clarify the difference between AI and machine learning (ML) since those two terms are often used in tandem in FSM solutions. 

ML is a subset of AI: it’s the ability of a computer to learn on its own by using algorithms. These algorithms can predict issues that could emerge by relying on knowledge, historical data, etc. ML can also recognize patterns and solve key FSM problems based on previous scenarios.

Another way to distinguish AI and ML: AI is the decision making, and ML is the knowledge gathering that the system uses to make the decisions. ML takes data and looks for underlying trends based on inputs. 

Let’s say an online form asks you a question, and then it asks you different questions depending on how you answer. If the form is just following a pre-coded “path” of questions, that’s not ML. If it’s adapting the sequence of questions on its own, that’s ML. 

Here’s another example of ML if it were to be used in FSM. If a customer always asks for the same technician, the system could learn this pattern and try and schedule that technician for that customer’s jobs, even if the customer isn’t explicitly asking for them. The key is that the system can a) recognize this pattern and b) seek appointment slots to satisfy the pattern without human input.

 RELATED BLOG: 5 Field Service Mobile App Capabilities Your Techs Need Today


AI in FSM relies on a wealth of field service data to automate more broadly and effectively. This is where longstanding FSM vendors have an advantage: they can draw from a rich history of customer interactions. They can take different scenarios, load them into an ML engine and build complex, highly automated responses to customer requests. This will “simulate” AI. 

Over time, they can develop into highly sophisticated automation platforms that can perform an array of tasks without human intervention. This can include automatically developing the rules that govern technician routing. 

But in the meantime, when you see a vendor touting their “AI-driven route optimization,” some skepticism is warranted. 

What can field service organizations do with narrow AI and ML today? Here are a couple of valuable examples.


Organizations can use AI to capture and analyze data in large quantities, letting them generate insights they can quickly apply to their field service operations. One of the most promising FSM use cases for this is with Internet-of-Things (IoT) devices. 

What an AI tool can do with IoT is generate profiles of deployed devices and infrastructures, which the organization can use to predict when those devices will need maintenance. It’s cheaper to send a technician to replace a motor in an appliance at the first sign of deterioration than it is to wait until a more observable breakdown occurs, and then replace the whole thing. 

AI can also help identify trends in deployed devices so that solutions can be applied at scale, like a software patch. That trending data is also critical for business decisions; if you’re a warranty provider, you’ll want to ensure that it’s still profitable for you to provide coverage for a product as it experiences an uptick in issues. 

According to the same WBR survey mentioned above, 51 percent of respondents said their field service organizations are currently leveraging IoT, and 31 percent plan to in the next 12 months. AI and IoT are growing together in field service, and they’re already connecting in ways that create real business value.



For an AI use case that could prevent truck rolls, we’re starting to see bots, portals and other tools that help customers troubleshoot their own devices. The AI can identify the asset, perhaps a certain model of a wireless router, and determine what type of guidance they should give the user based on the user’s predicted level of expertise.  

Setting aside the hype (and sometimes confusion) around AI and ML, businesses can see real benefits from leveraging the technology, whether it’s in the examples above or numerous other use cases. The business impact on FSM can include: 

  • Enhanced efficiency   
  • Reduced human error   
  • Visual quality control 
  • Increased mobility   
  • Optimized resource management   
  • Lower costs   
  • First time resolution and fix rates   
  • Predictive maintenance   
  • Improved customer satisfaction  
  • Increased revenues   



We have more data on how field service organizations are leveraging AI and ML, as well as IoT, augmented reality and other game-changing tech. It’s in a free WBR report you can download here.

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CSG Insights Team