When we think of artificial intelligence (AI), we usually think of robots and autonomous systems interacting with the physical world, or a central brain that runs the entirety of business operations. While neither scenario has fully come to fruition, AI is already impacting a less obvious part of business — field service management. 

Despite recent IFS research showing that home service organizations have been slow to warm to the concept of AI, it only tells part of the story. AI in itself has only a 3 percent adoption rate. However, analytics leads the pack in terms of overall adoption rates at 44 percent, and the Internet of Things (IoT) is already at 14 percent.

Powerful tools such as IoT and analytics are already helping service firms digitally evolve. These tools also represent the foundation on which AI will establish itself to take service organizations to new levels of digital sophistication. 

What does AI really mean?

To understand the transformation AI can bring to a service business, I will take a commercial HVAC company using systems connected via IoT-enabled sensors as an example. For the last six months, it has benchmarked internal data about how its HVAC’s output changes in advance of a breakdown. 

That information is then fed into an AI-powered system. This AI system now has critical data so that when the sensors detect the critical changes, it will set up an automated alert. 

Setting triggers based on a specific set of criteria being reached is programming 101, but learning what those criteria are, and evaluating how to handle it, is what really defines AI. This sort of prescriptive repair will only increase as AI systems better learn and adapt.

You can’t pick up AI off a shelf and install it in your business the same way you could buy a computer or a phone, or connect to the Internet. AI has more in common with a programming language, which is why many organizations employing complex field service software don’t even realize they are using elements of AI in their business today.

A strategic enabler in the call center 

As Chart 1 shows, recent IFS research shows the top technology that home service organizations are implementing in support of digital transformation is call center technology. It’s clear to see ways that call center operations could be enhanced through advanced AI. 

Chatbots are the obvious place to start due to their varying levels of sophistication. They are helping manage simpler service needs that don’t require specific escalation, allowing support staff to focus on more complex issues. Improving AI and tying AI systems into connected devices has the potential to make remote resolutions quicker, easier and far less labor-intensive. 

This is, of course, hugely beneficial, but the most compelling use case for call centers is how AI can assist human technicians during a service call. 

Generally, people prefer to talk to humans when issues arise, and humans are better at conversing than AI. So, rather than actually fielding the call, enabling AI systems to listen in to human interactions and make recommendations can save time and improve service outcomes. There are two key ways in which this is being employed today. 

The first is through speech parsing and recognition, which allows AI systems to diagnose potential issues and provide solutions. Diagnostics are generally an act of isolating and identifying problems, and humans are fallible and limited to what they’ve seen previously. Atypical issues may not occur to a human, but an AI system can catalog a list of symptoms and make informed recommendations in real-time, taking away the need for a call center agent to consult reference material. 

AI also is valuable in supporting service outcomes in situations that require escalation. Certain systems can read conversation, tone and severity, and provide the appropriate directive. For example, if a customer’s machine has malfunctioned due to a technician failure to secure a part after routine maintenance, the AI system can take the necessary factors into account and suggest actions to take that have resolved similar complaints in the past. 

This allows phone operators to offer resolutions without putting the customer on hold to check with a manager. As complimentary offers are accepted or declined by customers, AI learns and improves its future recommendations.

AI sees it all for optimal service scheduling

Scheduling optimization is another AI beneficiary. Today, it takes your available technician, service appointments, anticipated time of completion of each job and automates scheduling. 

Since AI primarily works to enhance these systems through adaptive learning, it enables these systems to self-improve, working toward more accurate predictions of time to complete a job, resulting in smaller time windows for customers and much higher fleet utilization rates.

An AI system requires a full view of the service process to work in this capacity — from an employee’s average time to complete a job, to vehicle information and inventory positioning. Business systems must be prepared for this. 

It doesn’t just mean having software in place to manage operations; technologies need to be able to communicate, and the data collected from these technologies must be available in a central location and a common language. 

The AI-powered future for service 

Spending on AI systems is set to more than double over the next few years, notes International Data Corp. in its 2019 spending guide. It has already begun to make its way into numerous types of software in different ways; however, often these technologies are individual and disconnected from one another. 

True AI-enabled digital transformation will only be achieved when it is implemented across all business processes within an organization, automating systems and making repetitive tasks a thing of the past. In this way, it will allow managers, technicians and dispatchers alike to focus on customer engagement and complexities best suited for humans.

A field service management (FSM) system should be the coordinating factor of all touchpoints. It needs to be calibrated to accept future technologies, whether it be a new module built from the system itself or a separate system that integrates with your FSM platform. 

Data must be written in a common language and freely accessible. If work order history needs to be pulled into an AI system connected to routing management, this should be actioned quickly and easily. Smart organizations are taking a cloud-first approach to this, allowing for constant updates and easy integration. 

Our AI-enabled future is already upon us, but it is imperative that businesses prepare for the next batch of technologies and make significant investments in software such as FSM platforms today. Companies must stay on top of these advancements as they become available and ensure they invest smartly to digitally transform successfully. 

When AI becomes standard practice, everyone’s going to be sprinting for your customers. If you want to run with them, you must start by building the road.