Paul Whitelam, Senior Vice President of Global Marketing at ClickSoftware, explains how AI is transforming field service management to the benefit of customers and service providers
We live in an era of continuous technology advancement, and field service is reaping the benefits as the addition of Artificial Intelligence to FSM (Field Service Management) tools helps field service technicians and organisations provide exceptional levels of customer service.
On any given day, a service provider may have thousands of issues to resolve. Sites and resources are geographically dispersed, and issues can range from routine maintenance to emergencies that require immediate onsite service. With a finite set of resources to handle all the work, organisations need a way to prioritise calls and effectively dispatch the right technician with the right skillset.
The dispatch process doesn’t end there. FSM dispatchers also need to take into consideration the location of the site, potential resource overtime, parts availability, service level agreements and many other factors. This complexity can become overwhelming. Yet, when AI is infused into the scheduling process, this type of multi-dimensional problem solving can be handled efficiently, helping businesses to achieve their goals.
This is just the tip of the iceberg. So how else is AI is transforming field service today, and what we can expect in the not-so-distant future?
First visit resolutions
Before the arrival 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 system might malfunction, disrupting business and impacting revenue. Today, by adding sensors and digital intelligence to equipment, these machines become connected and able constantly to relay information to a hub, with no human involvement. This information Field service management powers up with AI helps identify problems quickly so that technicians arrive with the right tools and parts, ensuring first visit resolutions are the norm, not the exception.
There is also huge potential in the emerging predictive maintenance model, which allows companies to get ahead of problems and tend to them before inconvenient and costly disruption. As more connected appliances, devices and equipment are deployed, organisations will be able to aggregate historical performance data on hundreds of thousands of units, giving them the ability to identify patterns in performance and predict and prevent problems. Ultimately, we can expect artificial intelligence to bring equipment downtime to zero.
If, 20 years ago, you called your telecoms company for service, you would first have to endure hold music while your call was properly routed; then talk to a dispatcher who would manually work through employee schedules to find an available technician; 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 schedules to identify the technician with the right skills for the job who is closest to the customer for the fastest response time.
Though not an easy task, this level of effort is critical for customer retention. In 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 optimisation is already delivering real value to the field service industry, but there is still plenty of room to improve the frequently frustrating process of scheduling appointments to reduce wait times.
Service organisations can use machine learning to analyse data and, specifically, which characteristics of that data have predictive importance. For example, they can analyse how long 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. They can also factor in situational considerations, such as whether a technician needs to seek permission to access the property. This capability gives service organisations an unprecedented ability to predict the length of time it will take to perform any task, thus enabling more precise scheduling and firmer guarantees to customers about service availability.
The personal touch
Another important benefit AI brings to the field service industry is the ability to personalise service provision. At a time when service is increasingly commoditised, customer experience is a critical competitive differentiator. In addition to learning more about machine performance and technician abilities, AI-powered applications can glean information about an individual’s preferences and behaviour. For instance, does the customer prefer to schedule appointments in the morning or afternoon? 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 the level of personalisation that customers expect.
Service providers that integrate AIpowered 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.