by Sarah Nicastro, Creator, Future of Field Service

From OpenAI’s evolution to DeepSeek disruption, AI persists as one of the most buzzworthy topics of 2025. While I’ve talked to service leaders whose responses run the gamut from hard-to-contain excitement at its potential to utter disdain for its all-consuming prominence, it is indisputable that AI is changing how businesses across industries work – and we’ve only just begun.

Where We Are

In field service, there are organizations leading the charge to adopt AI in meaningful ways and those that are more resistant to its inevitable impact – with the vast majority somewhere in between. Late last year, Future of Field Service conducted a service with our Stand Out 50 leaders and here are some of the interesting points they shared.

Fifty percent of respondents said that less than 10% of field service tasks are automated. This reveals the tremendous opportunity that exists to use AI to help make the lives of field technicians specifically and service operations on the whole much easier and potentially more satisfying.

When we consider how customer expectations have evolved, as well as how they will continue to – especially as their familiarity with AI expands in everyday life – it’s interesting to begin to envision all of the ways in which AI could enable service providers to work smarter. Here’s how the Stand Out 50 ranked the top seven customer expectations:

1. Demanding faster resolution
2. Wanting peace of mind/guaranteed uptime or performance
3. Desiring more data and insights to aid in improving their business
4. Expecting more data and insights about the service delivery process and value delivered
5. Seeking streamlined or different channels of communication
6. Higher standards of brand experience/soft skills
7. Seeking more sustainable providers/partners

Sixty-two percent of respondents already use AI in their service operations and shared a wide range of examples of how so:

• Chats and emails
• Triage in tech support; insights dashboard
• Using AI to review customer equipment on material through-put to ensure they get the best yield of products
• Service order summaries
• Document and data search
• Guided troubleshooting (pilot phase)
• Used in monitoring assets and in our scheduling tool
• Customer contact, scheduling & routing, predictive maintenance
• Primarily used for the service desk, with a goal of preventing calls from dispatch and resolving via phone or chat. AI is also being used to immediately dispatch to the field issues that cannot be resolved remotely, ensuring swift resolution and not requiring customer interaction for the call
• Generating service tickets from emails
• Diagnostics workflow, technical training, value-based selling, technical report dictation, material master data cleanup
• Self-service, self-training, knowledge management, process automation, data mining
• Generative AI for triage, AI for resource allocation, machine learning for predictive analytics
• Scheduling and optimization of our field interventions, optimizing work order quality by using AI to predict job duration, and supporting field force on the job with image recognition AI

As the use of AI and other technologies expands, organizations must consider the effect it will have on how service is delivered – and what that means in terms of changes needed in the customer narrative, commercial agreements, or both. The increase in both self- and remote service are great examples of how today’s technologies can be used to significantly reduce inefficiencies and provide faster resolution, but for organizations who still primarily have transactional, break-fix relationships with customers this can present a hurdle to overcome.

Fifteen percent of the Stand Out 50 respondents have extensive self-service options in place and state customers are responding well; 52% currently have some self-service capabilities and state it’s a focus to expand. Thirty-three percent of respondents have transitioned a significant portion of service delivery to be remote and another 30% are in the midst of transitioning a portion of service delivery to remote, while 26% use remote capabilities but for diagnosis versus resolution, and 11% state that they either have barriers to using remote capabilities or it’s not yet a focus.

While this data is representative of a relatively small group of service businesses, it shows some real-world examples of how AI is being adopted, how AI and other technologies are changing workflows and transforming service delivery, and how these new ways of working can raise questions that reach beyond service transformation to business transformation.

Where We’re Going

Regardless of whether AI elicits excitement or an eye roll from any given service leader, they generally agree that its use and impact is still in its infancy – and organizations have a massive responsibility to determine how to take today’s pilots and early use cases and rapidly expand on their success.

Forty-seven percent of the Stand Out 50 respondents listed AI as their next area of focus for technology investment and 76% believe Advanced AI will be critical for staying competitive in field service. When asked what areas of AI they feel hold the biggest potential for service organizations, respondents said:

• Fault/failure prediction – 33%
• Knowledge management – 30%
• Customer support (chatbots, self-service capabilities, etc.) – 19%
• Automated scheduling and resource optimization - 11%
• Automation of reporting – 7%

In many ways, the sky is truly the limit in terms of where AI can go in field service. In his 2025 predictions blog, Mark Brewer, VP of Service Industries at IFS, talks about some of the exciting advancements he expects this year – including more instances of agentic AI, the lift AI can provide for knowledge management, and the potential it has for fault detection. All of these uses – and more – have the capacity to bring a lot of positive change to how service organizations operate and the value they can offer their customers.

A great example I came across recently is CNH. The company used AI to condense 1.5 million pages of manuals into one AI-powered chatbot. According to this article, “CNH AI Tech Assistant tool is already at work at over 300 authorized agriculture and construction dealer groups, with global expansion underway. The first-of-its-kind tool was developed with dealer feedback and works by simulating conversations to provide a diagnosis and repair plan for CNH brands’ machines, enabling dealer technicians to save time on repairs by providing fast and accurate answers to technical questions.”

While this is just one of a multitude of ways AI can transform service, the bigger question to ask around where we’re going is how ready an organization is to change – and to what extent.

What We Don’t Yet Know

When it comes to what we don’t yet know, there’s both the logical and the philosophical. Let’s start with the logical. The Stand Out 50 respondents weighed in on their biggest concerns around the growing use of AI in field service:

• Accuracy & bias - 33%
• That we haven’t yet mastered “the basics” and must do so first - 23%
• Not having the data at the ready to support its use – 19%
• Security – 11%
• The hype surrounding it – 7%
• Keeping pace with the technological advancement - 7%

There are also some very crucial logistical questions I’ve alluded to already around how the use of AI will evolve the work frontline employees do (or don’t) as well as what the service value proposition looks like. So, when it comes to an increase in remote and self-service; how does less on-site work change what we need from our employees? Are they able to do that new work today, or do they need reskilling or upskilling?

Also, how does a more modern, remote-first service delivery model change the customer value proposition? For companies whose customers are still accustomed to paying for time technician is on site – that visible, tangible work – how do they reshape the commercial agreements and then communicate them in a way that resonates?

These are the questions where the most sticking points arise – the real strategic meat of what using AI to a significant extent will mean digging into and ultimately through. But perhaps the most important question that is left unanswered is a philosophical one: how do we make good use of sophisticated technology without it having a negative impact on how we value humanity?

This question was the premise of a recent podcast discussion I had with Arnaud Billard, Senior Director for Applications and Service for Europe at Cepheid. When Arnaud and I connected to land on a podcast topic, he mentioned that he is really struggling with what the future holds around AI and automation and, once we dug in, I admired the perspective he was sharing.

He clarifies at the beginning of the podcast, saying, “The struggle I refer to is not about resisting technology or AI particularly. It's more about how to navigate its evolution while preserving what makes service truly valuable, which is a human connection.”

For organizations who haven’t evolved their view of service beyond break-fix, the reverence for relationships may be less than Arnaud’s – but for many, this is a missed opportunity. “Relationships in service really matter and there is a component of trust that is very important. When you sell service, it’s intangible. It’s no longer just about fixing things; service professionals today are no longer only solving technical issues; they are acting as a trusted advisor. They gather insights, identify customer pain points, they contribute to company growth via customer intelligence. It’s one of the most overlooked aspects of service, I believe, but service is a source of innovation and growth,” says Arnaud.

If you share this view, then you can understand that the risk of overapplying AI in service for the sake of cost savings or efficiency gains is not only a risk to the business at present but also cuts off an incredibly valuable source of knowledge, relationships, and fuel for innovation. “I'm very conscious that AI can bring fantastic efficiencies,” says Arnaud. “However, to me, we have to find the right balance between enhancing service productivity without depersonalizing it. We need to ensure that we don't erode the very element that built customer loyalty and satisfaction. We must make sure that technology enhances our human capacities rather than diminishing them.”

I’d encourage you to have a listen to the full podcast discussion here. If you have thoughts on the now, the next, or the unanswered questions of AI in field service – reach out!