Enterprise customer experience (CX) usually focuses on the moment of communication between a customer and a business, assessing the customer’s state of mind and the outcomes of specific, limited interactions. But there’s another important context that drives customer behavior, and that’s the on-site experience of technical support. Field service is often out of the line of sight of CX planners, but it shouldn’t be.
Field service management (FSM) software comes from a very different lineage than the traditional communications and engagement tools that run contact centers. FSM does include some familiar elements like customer information portals and outbound notifications, but it is mostly concerned with the logistics and mechanics of organizing teams of people across distances; the specific tools and pieces of equipment needed for each job; and the detailed information required at hand on products, technologies and customer histories.
That puts a different emphasis on how broad-based FSM tools are developed and packaged. They have different origins than the telecom-based origins of other CX tools coming from logistics, fleet management and asset management. The current field service software market is bifurcated: on one hand are legacy field service companies that focus on one or more of the logistical aspects of the service process, like work order creation or technician optimization; on the other hand are larger “all-purpose” companies that have deeper business software platforms onto which they have grafted field service tools in order to streamline complex, interdepartmental business processes. The latter can be primarily customer relationship management (CRM), ERP or ITSM platforms—all are good at supporting the software functions that stem from field service activities.
In researching the software offerings of key industry participants, we have discerned several trends that illuminate how FSM is evolving. They also provide a framework for how an enterprise should structure field service operations and the surrounding software environment.
Our hypothesis going into our research was that we would find extensive adoption of artificial intelligence (AI) in field service. Field service depends on two things that AI has proven good at: parsing mountains of unstructured data (e.g., product and repair knowledge) and optimizing processes with complex variables (in this case, scheduling and tracking personnel, assets and supplies). Yet we found that despite assertions from providers that they are investing a great deal in AI, many platforms have not yet leveraged it outside of some very rudimentary use cases. Generally, the larger the software firm, and the more deeply entrenched in back-office software platforms, the more likely they are to have leveraged AI for optimization and for advanced uses like predictive maintenance. Software providers that focus on FSM as a point solution have been slower to turn to AI across the applications.
This is not to say that AI is absent. Instead, it has been injected into the process more as part of the interface layer of software than as a set of distinct features. It is being used to summarize and document events (like work orders, service histories or interaction wrap-up notes), but rarely to automate processes. In other words, quite a bit of generative AI (GenAI), but very little that could be called “agentic.”
A similar dynamic affects features related to augmented reality (AR) and Internet-of-Things (IoT) features. Aspects of those technologies have been around for years, leading us to expect that we would find them deeply embedded in most FSM tools. That is not the case. Most FSM solutions support IoT through integration with dedicated partners, if at all. And AR is supported by a handful of software providers, particularly the ones with large-enterprise industrial B2B customers.
Some providers have recognized that they can use IoT to pivot their buyers from traditional, reactive “break-fix” service to using service as a continuous process that involves predicting when maintenance visits are needed and alerting end customers to those needs in advance—boosting awareness and customer engagement.
Like contact centers, field service operations are being squeezed between their high cost and the need to become revenue-driven entities. The ultimate goal is to reduce the need for truck rolls, which are highly expensive and often have to be repeated because of poor information supporting the process. That encourages enterprise buyers to focus on the scheduling engine of the software. And that is where most providers have put their resources: into better optimization quality around tracking technicians, parts and tools; into managing constraints like SLAs, technician skills and appointment windows; and into performing at larger scale.
These help reduce or manage the cost side. But what we found was that most providers have done less to enhance the customer engagement components of their platforms, and this in turn has not helped buyers turn to revenue-generating actions or those that explicitly improve customer satisfaction and experience. The focus is on creating better outcomes through efficiency but has not generally been on creating an engagement pathway directly connecting customers to field service operations for things like upselling.
This is likely to become more important as these operational departments start being measured based on their business unit performance, not just the number of tickets and cases closed. ISG Research predicts that by 2028, field service operations will be seen as drivers of significant revenue for most enterprises.
It is also notable that many software providers ignore self-service as a component of their platforms. Some don’t include customer portals in their offerings. Some don’t provide customers with explicit self-help or diagnostic tools. Most don’t control the knowledge infrastructures that deliver useful problem-solving information to customers. And few maintain an active connection between the tools used to engage customers via a contact center and those used to engage for on-site service calls.
The clearest trend is that FSM is increasingly attractive (to both buyers and software providers) as a component of a larger enterprise back-office platform, not as the traditional set of point solutions. This encourages businesses to create shared customer and asset data models with their CRM or ERP systems, along with tighter integrations across the service landscape: workforce management, quality assessment, Contact Center as a Service (CCaaS), ERP and self-service. Buyers are increasingly favoring a strategy of creating broad service platforms. This has the side effect of making integrations less painful.
Based on these trends, we recommend buyers focus on a platform’s scheduling quality and mobile toolset. Most of the effective ROI a business gets from its investment in FSM software is still derived from better schedules, technician time optimization and making fewer repeat visits. These fundamentals don’t necessarily require advanced AI at the moment. Consideration of the outlying advanced capabilities in predictive maintenance, AR, IoT and agentic AI should be based on having specific use cases that need those functions, not on a generic sense that they “belong” in an FSM platform.
Broadly speaking, the FSM software and operational landscapes have not advanced as quickly as other segments of enterprise software. The exceptions—large enterprise platforms of which FSM is one small part—are pointing to where the industry as a whole is going. But most software providers are not there yet, and buyers need to be aware of the drastic differences between the big platforms and the standard FSM point solutions.
Regards,
Keith Dawson