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        AI Can Boost CX Outcomes in Field Service

        Field service operations are not often discussed as part of enterprise customer experience planning, but there is a strong argument that they should be seen as an important factor driving how customers perceive brands. Like contact centers, field service teams are dealing with the advance of startling new technologies that can be expensive and disruptive. The flip side of disruption, though, is that it presents interesting opportunities for improving customer-related outcomes.

        The basic challenge in field service is to transition from processes that are mostly manual to a more fully automated way of operating, much like the rest of the service environment. What makes field service so expensive is that it relies on actual human beings moving through space to customer locations, doing so while equipped with the precise information and physical equipment needed to address an issue. One missing part on a truck can short-circuit what would otherwise be a successful interaction, forcing another visit that adds immense cost and degrades the customer’s brand perception. Moving from manual to automated processes is the only way to scale operations without bloating costs.

        Other challenges exist as well. The industry is experiencing a shortage of skilled workers (both technicians and agents), especially in important, high-volume industries like telecom and manufacturing. Field service operations are charged with becoming more efficient and speedier while at the same time controlling costs. And enterprises are starting to look at field service through the lens of both brand management and revenue generation. This has sparked a debate about the role of field service in boosting enterprise CX, especially when a business realizes that customer encounters in the field are even more visible than those coming through contact centers. That puts a premium on analyzing customer responses and satisfaction measures.

        The same technology trends affecting other areas of service are poised to have an impact on the field service space. There’s no surprise that many of the advances are related to the development of artificial intelligence (both generative and predictive). Just as contact centers are seeking out use cases for AI where there are clear and measurable cost-related benefits, field service teams are looking at systems that provide automated assistance in real time to technicians at customer sites. This may be the most immediate, visible and beneficial use case. AI can also be used for onsite diagnostic purposes. Some software providers have explored AI tools that are trained on source data related to servicing specific types of equipment.

        AI also allows for more predictive maintenance of complex systems. When you can use analytics to assess where potential problems are likely to emerge, you can preemptively reach out to customers to schedule service, Ventana_Research_2024_Assertion_FieldService_AI_Automation_Pilots_47_Slowering overall costs and ensuring that fixes, when needed, are minimally disruptive to customer operations. This can also help businesses transition away from the traditional break/fix model of field service to a more effective model that uses predictive maintenance to engage with customers in novel ways. That, too, can change the underlying cost and revenue structures inherent in field service. Ventana Research asserts that by 2026, 4 in 10 enterprises offering field service will start AI automation pilot projects to reduce dispatch requirements and proactively engage customers early in the service process.

        These AI trends are evident in some of the recent releases by major software providers in the space. Salesforce, for example, recently announced that a beta of Einstein Copilot is part of the latest Einstein 1 Field Service edition of its toolkit. ServiceNow’s Washington release added features related to predictive parts forecasting, work order summarization and automated mobile data entry. Across the industry, AI and related predictive/analytic components are making their way into the default field service management platforms.

        Field service management software providers are doing more to integrate systems into business platforms and processes. We are seeing software providers build closer connections between FSM and back-office systems like ERP and customer relationship management (CRM). This lets onsite technicians better access real-time data about specific customers and not just the device or problem at hand. Closer integrations bring in purchase orders, inventory data, customer contracts and warrantees. It also makes it easier to manage the complex dance that occurs between the business that “owns” the customer relationship and any contractors that may be needed to perform onsite service tasks.

        One of the many sensitive issues bedeviling the consumer perception of a company’s field service is the lack of continuity of information between the brand the consumer buys from and third parties that deliver installation or service calls on expensive items like white goods or electronics. Having the technical ability to smooth that handoff makes it possible to better identify trouble spots in the service process. That, in turn, allows the business to make field service a driver of successful CX rather than a potential minefield of dissatisfaction.

        When technology moves forward, it provides enterprises an opportunity to reassess processes, expectations and goals. No surprise, then, that various AI and data-related innovations are encouraging experimentation with different models for field service. One is the move to predictive maintenance, noted above. Another is the use of outcome-based service contracts instead of the more common approach of charging based on how much time was spent on issues or the specific activities performed. It’s hard not to see this as a positive development. First, it ties planning and resource allocation to specific, measurable and obtainable results. And second, it encourages decision-makers to view the actions of service teams (including contact centers) based on key performance indicators that better reflect organizational goals like revenue, customer longevity and contract value than cost-centric measures of speed and quantity.

        Enterprises should consider some of the new capabilities in tandem with process improvements to drive better customer perceptions of their experiences, including the use of AI to arm technicians with faster diagnostic solutions. This will help solidify field service as a growth driver rather than a cost sink. Field service management software providers have been measured in adding AI and machine learning components in ways that are directly targeted to specific problems in the field service process rather than throwing technology at the wall to see what sticks. That’s a solid win for enterprises.


        Keith Dawson


        Keith Dawson
        Director of Research, Customer Experience

        Keith Dawson leads the software research and advisory in the Customer Experience (CX) expertise at Ventana Research, now part of ISG, covering applications that facilitate engagement to optimize customer-facing processes. His coverage areas include agent management, contact center, customer experience management, field service, intelligent self-service, voice of the customer and related software to support customer experiences.


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