The field service industry is changing at a rapid pace thanks to the ongoing advancement of field service management (FSM) technology. While it takes various layers of technology to complete a full suite of FSM tools, artificial intelligence (AI) plays a significant role and can have a major impact. The acceptance and usage of technology within FSM organizations will continue to drive business growth. According to a recent report by MarketsandMarkets the FSM market is expected to grow from USD 2.56 billion today to USD 5.08 billion by 2023.
At the center of this growth is AI and its ability to continually learn and resolve problems. Field service is an area in which the application of this technology is particularly valuable. On an average day, a field service company may have 5,000 geographically dispersed issues to tackle -- whether routine maintenance or emergencies - and just 500 resources to handle them. How can any organization quickly prioritize the issues and effectively dispatch an available technician with the right skills for the job, while taking into consideration geographical location, overtime, parts availability, driving distance and many other factors? This is the type of multi-dimensional problem AI handles best.
Although AI technology is already making an impact on the field service industry, this is just the tip of the iceberg. With AI improving productivity, enriching job functions, creating new roles and possibly even new industries, AI will be behind the introduction of 2.3 million new jobs by 2020, according to a recent Gartner report. Let's look at how AI is transforming field service today, and what we can expect in the not-so-distant future.
AI is changing how technicians are sent out on jobs. (Image: Pandu Agus Wismoyo, Unsplash)
Resolving issues before the customer knows they have a problem
Before the onset of connected devices and the Internet of Things (IoT), your washing machine or dishwasher would break down without warning, and you would have no insight into the problem or how to fix it. Similarly, a company's HVAC might malfunction, disrupting business and impacting revenue. Today, by adding sensors and digital intelligence to equipment, these machines become connected and able to constantly monitor their own health and relay information to a hub -- with no human involvement. This information helps identify problems quickly and arm technicians with the right tools and parts, ensuring first-visit resolutions are the norm instead of the exception.
Forward-thinking organizations are already fitting machines with IoT sensors to identify issues and fix broken equipment -- but there is huge potential in the emerging predictive maintenance model that allows companies to get ahead of problems and tend to them before the inconvenient, and sometimes revenue-affecting, disruption.
As more connected appliances, devices and equipment are deployed, organizations will be able to aggregate historical performance data on hundreds of thousands of units, offering the ability to learn and identify patterns in performance to predict and prevent problems. Ultimately, we can expect artificial intelligence to bring equipment downtime to zero.
AI is used frequently in large call centers to enable customers to talk to the most suitable agents. (Image: Thought Catalog, Unsplash)
Optimizing scheduling through data analysis
If you called your telecoms company 20 years ago for service, you would first have to endure hold music while your call was properly routed, then talk to a dispatcher who would manually scramble through employee logs and schedules to find an available technician, and even then you might have to wait a week (or more) for a fix. Today, companies are using AI to scan hundreds or thousands of employee logs to identify the right technician for the job who is geographically closest to the customer for the fastest response time. Though not an easy task, this level of effort is critical for customer retention. According to a recent survey, more than 60% of respondents said that a long wait time between making a service appointment and the actual visit results in a poor customer service experience, a major driver for switching to a competitor.
AI-driven schedule optimization is already delivering real value to the field service industry but there is plenty of room to improve the often frustrating process of scheduling appointments, and reduce wait times to improve customer experience.
How does it work? Service organizations can use machine learning algorithms to analyse data, and specifically which characteristics of that data have predictive importance. For example, one can analyse the amount of time it takes to complete a certain task and the extent to which different factors -- like weather, or the condition of the equipment -- impact that duration. One can also consider situational factors, such as whether a technician needs to seek permission to access the property. This capability gives service organizations an unprecedented ability to predict, in our example, the length of time it will take to perform any task -- which in turn enables a more precise schedule and the ability to make firm promises to customers about service availability.
As people increasing use social media and bots for company interactions, good customer service is more important than ever. (Robert Bye, Unsplash)
AI delivers a personal touch
Another important advantage that AI technology is bringing to the field service industry is its ability to deliver personalization. In an era where service is increasingly commoditized, customer experience is a critical competitive differentiator. In addition to learning more about machine performance and technician abilities, location, etc., AI technology can acquire information about an individual's preferences and behavior. For instance, does the customer prefer to schedule appointments in the morning or afternoon? Is their home gated? How often have they cancelled appointments and with how much warning? How much advance notice is preferred before a technician arrives? Is there a specific technician they typically request? Again, it's all about data -- as service companies learn more about their customers, they can better leverage AI to provide a personalized service that customers expect.
Service providers that integrate AI-powered applications in their business strategy will have a competitive advantage when it comes to delivering exceptional customer experiences. Consumers, as well as businesses, expect speed and accuracy from their service providers, and the technology is here to help them meet increasingly high expectations, adhere to service level agreements and hit business performance goals.
Paul Whitelam, Senior Vice President of Global Marketing, ClickSoftware