Can generative AI limit the impact of a shrinking, ageing field service workforce?

There’s an understandable nervousness among any workforce when you start talking about automation. It’s actually been 10 years since Oxford University academics Carl Benedikt Frey and Michael Osborne released their seminal paper entitled The Future of Employment which contained a list of occupations with an automation probability score. It was the paper that launched a spate of headlines claiming robots were coming for our jobs.

For field services teams, this has been a slow burn. As the paper suggests, “the manual tasks of service occupations are less susceptible to computerisation, as they require a higher degree of flexibility and physical adaptability.” And yet, the workforce is under pressure, not so much from automation but from the fact that it is ageing. To compound the problem, not enough millennials and Gen Zers are seeing the profession as an attractive career option.

According to recent research into the future of field service, almost three-quarters of companies (73%) identify an ageing workforce as a potential threat to their field service operations. Meanwhile, over two-thirds (67%) of companies feel that technology plays a key role in attracting new talent to a field service organization. The challenge is which technologies and to what extent an organization commits.

What is becoming increasingly clear is that AI and automation are going to play a significant role in field service operations. Generative AI is going to be at the heart of this. US-based organization OpenAI, the maker of ChatGPT, believes it has the ability “to limit the impact of a shrinking and ageing field service workforce in several ways.”

Next-generation workforce

Field service has a major workforce recruitment and retention problem. The majority of the workforce is nearing retirement which means that we are losing their expertise, their knowledge and their ability to complete work. It also leaves a void that is not being filled by new entrants who are not as interested in the type of work being done in field service. This is where the capabilities of ChatGPT can be transformational. But the outgoing workforce isn’t really where the potential lies. The real value lies in the next-generation workforce.

ChatGPT can help organizations capture and organize the unstructured knowledge and experience of the experienced workforce and make it accessible in a format that is desirable for the next generation of workers. As opposed to viewing paper manuals, calling a friend, and completing countless forms to close out a service job, consider an experience where an engineer has access to relevant and contextual information via a query, with work instructions overlayed visually via Augmented Reality, and work summarized and documented via generative AI.

While it might seem futuristic, it’s the type of vision for field service that organizations must have to create a marketable product for the next generation workforce. These potential entrants are digitally native, they’ve grown up on the tools of today and are constantly tinkering with the likes of ChatGPT.

Organizations must be able to provide these technologically-savant workers with the tools, information, and working models that they are already familiar with to make the profession of field service more relevant. If organizations can provide them with a way to engage with the experience and knowledge of former generations to become more proficient in the craft, open up new opportunities for learning and growth, and create working opportunities that could be remote-first or data-focused, then it changes the profile of what a career in field service looks like.

Role of technology

The point is that automation can enable field engineers to do their jobs more efficiently and effectively. Of course, organizations must be diligent about where and how generative AI is used, as it can give confidently wrong answers. Although humans are no different in that regard, it is important to install guardrails against generative AI introducing inaccuracies into the content it generates. By automating mundane tasks and improving asset intelligence, field service teams can optimize work schedules, parts ordering and parts delivery times. This reduces unnecessary truck rolls and improves customer satisfaction through more accurate and efficient maintenance work. Collaboration between support teams and engineers on a common platform and using real-time asset data also helps to identify opportunities for new contracts or device upgrades.

There are, of course, some things technology cannot replace, such as engineer experience, but this is about how to use technology to manage skills gaps, attract new engineers and even unretire some of the older ones. The technology is there to make the job easier and more effective. Regardless of age, that has to be a good thing. To learn more about AI use cases for service teams, register for our upcoming Design Time webinar.