By Sarah Nicastro, Creator, Future of Field Service
Artificial intelligence (AI) technology is steadily working its way into a lot of different applications and has certainly received a lot of media attention – including some over-hyping of its abilities as well as hand-wringing about unintended consequences.
San Francisco-based OpenAI has garnered a lot of recent coverage thanks to its ChatGPT, an AI-based chatbot with an uncanny ability to create literate responses to a wide variety of questions across areas of expertise. This type of advanced AI can potentially revolutionize some functions (like online help desk functions), or be used for less-than-desirable applications (generating more believable phishing emails or helping kids write term papers), and has even inspired some eccentric experimentation – everything from creating a biblical verse that explains how to remove a peanut butter sandwich from a VCR, to an unnerving exchange between a New York Times columnist and the Microsoft Bing chatbot.
In field service, AI holds a lot of promise, but companies are grappling to understand what’s fit for purpose today and what headline-inducing aspects are better left for the future. Today, a properly trained AI algorithm could be useful for troubleshooting, help desk, and predictive capabilities. Longer-term, advanced AI engines could diagnose and potentially even manage equipment repairs.
While we tend to get caught up in the more futuristic, modern interpretations of AI, there’s a good chance it’s already in use in some capacity in your business. Right now, most of the major field service software vendors have already incorporated some level of AI or machine learning (ML) functionality into their solutions, allowing organizations to use AI to improve and automate workflows.
Today’s AI ROI
These integrated, impactful AI capabilities are already helping field service organizations in a number of ways. Here are some examples:
Help desk chatbots. Many companies already leverage chatbots for website interactions that help guide users to the right resources. In field service, an AI algorithm can help guide customers through a lot of level-one help desk support questions to properly escalate their query. For phone-based systems, help desk staff can feed customer information into an AI-based solution that can more accurately help them triage the customer and make sure the right parts, technicians, and other resources are dispatched to help them.
Job scheduling and planning. AI-based planning & scheduling eliminates manual efforts to automate scheduling based on customized parameters (like customer status, complexity, parts inventory, location, SLA requirements, etc.), maximizing utilization of resources and efficiency. With these capabilities, dispatchers can focus on exceptions and customer experience.
Predictive maintenance. AI solutions can be trained to analyze failure rates for parts and equipment and make educated predictions about when a machine might experience a problem. This data can help guide technicians when it comes to pre-emptive part replacement or inspections, and ultimately improve equipment uptime. Not only are organizations using these insights to offer outcomes-based service, but also to incorporate back into R&D to improve product development.
Knowledge management. This is one area where AI can help create an all-new workflow (instead of just improving an existing one). Most field service organizations have a lot of repair data across disparate systems, but right now the only way to access it is to rely on the institutional memory of employees to help navigate through it. Using natural language processing, AI could sift through that data and respond to technician queries about prior repairs. This type of application would require more work on the front end to organize and clean-up data, but could be a boon in markets where a lot of veteran technicians are retiring and taking that information with them.
These are just some examples of how AI is providing practical value to organizations today. It’s worth noting that in all of these cases, AI is not replacing employees, it is augmenting their ability to make better decisions faster. AI can simplify complex or repetitive manual tasks, improve efficiency, increase productivity, and help create better customer experiences, but one of the major fears of the technology is that it exists to take the jobs of the frontline worker. When you think about the talent shortages companies face, it helps to frame the use of AI as a way to work smarter versus harder and to allow the role of the frontline to evolve alongside customer needs.
AI Into the Future
The important thing to remember about these current AI solutions is that while they can quickly ingest data and generate responses (schedules, maintenance recommendations, etc.), those results are best seen as suggestions that should be evaluated by expert staff members. AI and machine learning are vulnerable to the same types of mistakes and biases as the people who program them. They are evaluating the same data to reach a conclusion, just doing it faster and on a wider scale. The only way to compensate for potential flaws in the underlying data is to leverage human experience and expertise.
And speaking of human experience, another caution of AI is to remember the need to balance the increasing use of advanced technologies with maintaining the human feel. I spoke at a conference last year where a leader shared a very transparent tale of how her company had experienced great success incorporating AI into customer service, but even though it was working well (not the frustrating automated experience you may be familiar with) the customers really missed the personal touchpoints. So, the company reflected and revisited the ramping up of AI to ensure a better balance between tech and human engagement.