For better or worse, technology has taught us that we shouldn’t have to wait anymore. We can access the news instantly, wherever we are, on our mobile devices. We can watch a favorite movie on demand via Netflix or another streaming service. “One-click purchase” represents the amount of time it takes to buy. In fact, the ease and speed of e-commerce has gotten to the point that it’s practically a national holiday.
So why are we still being asked to wait at home between noon and 5:00 p.m. for a repairman to show up and figure out why our ice maker is out, why the cable refuses to pull up HBO, or why our exercise bike is on fire? Why hasn’t field service caught up to the rest of the world?
One answer is that the industry hasn’t adopted the kind of technology that will improve speed, accuracy and reliability. If field-service reps could communicate with the equipment they’re sent out to repair, they wouldn’t have to come in unprepared, assess the situation, and then begin repairs — all of which slows the whole process down and ensures that a five-hour window is often as accurate as it gets.
Enter the Internet of Things (IoT) and artificial intelligence (AI). IoT promises that field-service reps will be able to talk to machines to quickly identify issues; AI promises to make reps aware of problems before they even appear. Let’s look at four areas AI and IoT will change the industry.
1. Image recognition
AI will use image recognition to streamline the service process, whether that’s break-fix, preventative maintenance, or installations. AI can help the technician identify parts for repair and order them, or record what parts were consumed as part of the work order and automatically put in an order to replenish them. This is a notoriously error-prone process right now. As field service is closely involved in fixing or replacing parts at the customer’s home or office, image recognition has huge potential to increase the field rep’s accuracy throughout the asset service lifecycle.
2. Technician guidance
Businesses today want to be prescriptive about what their technicians do at every step of the work order — not just for compliance reasons, but for training and safety, too. It can be laborious for a company to stay current with new developments in the field. By using AI to analyze what technicians typically do for a certain type of work, for a certain type of customer, at a particular step, given a particular asset, AI can develop a set of best practices to guide future technicians through those same situations. This guided experience helps technicians get their work done more efficiently while increasing the rate of “first-visit resolution.” And that benefits all stakeholders — technicians, schedulers, and customers.
3. Life cycle prediction
The data that develops best practices for technicians can also develop predictions. IoT and usage-based preventative maintenance is great, but right now it’s still inherently reactive. AI can analyze so many more factors — customer history, recall information, temperatures, humidity, asset location, and so on — to then accurately predict issues before they happen. A life cycle for a product will emerge in which, say, an order is automatically generated for that ice maker to be serviced in the next three months since the data says the product tends to break down during that time frame. How much more satisfied will customers be if you reach out to them first in anticipation of a break-down rather than getting a frustrated call after the fact? AI will finally fulfill the promise of being proactive.
Imagine this predictive ability extending to the weather. AI notices that storms will roll in next week, and shuffles schedules accordingly — that can mean adjusting travel time based on weather and traffic conditions. We see a technician at work any time we open Google Maps and Waze. That’s all pretty straightforward now. But what if AI could match a technician’s skillsets with the skillsets that may be needed for a particular job? This disparate information — weather, traffic, skillsets, customer needs — will, when crunched by AI, improve field-service scheduling. The right technician at the right place at a precise time, rain or shine.
The view of the future is this: AI and IoT will integrate humans and machines to be more efficient, responsive and customer-oriented. And, contrary to fears, AI won’t replace those technicians. They’ll become more efficient, certainly, but the “people” component of field service is something that will be enhanced by AI. A networking of technicians and the machines they work on, facilitated by AI and the data it uses.
That not only means improved efficiency, but also better cross-sell and up-sell opportunities. If we know a certain type of switch is less reliable than another type of switch for a particular customer, we can use AI to suggest that better part. Because field service is no longer just about fixing and maintaining; it now offers the technician the opportunity to build a relationship with the customer — and to be a trusted adviser.
What that adds up to is a transformation of field service into an instantaneous industry. Maybe someday it’ll even have its own holiday.
Michael Chou is the VP of Product Management at Salesforce Service Cloud.