Field service organizations have reached a critical milestone: 95% now use artificial intelligence, according to a comprehensive survey of more than 2,300 field service professionals across nine countries. The research, conducted by Salesforce, also found that 85% of these organizations plan to increase AI investments over the next two years. But while adoption is nearly universal, the study highlights persistent legacy issues that prevent many companies from maximizing their return on AI.
AI delivers measurable gains when systems are connected
The most striking finding is that AI is driving real financial outcomes for companies with connected systems. Among organizations using AI-powered scheduling and dispatch, 57% report higher revenue per job and increased mobile worker productivity. This suggests that AI is not just a novelty but a strategic tool that can directly impact the bottom line when deployed in core operational areas.
More than half of field service organizations (54%) use AI for customer communication, while 51% use it to support mobile workers in the field. The ability to understand the context of a job, customer expectations, and immediate requirements is making AI-powered solutions increasingly indispensable. Speed to value has become a key driver of customer loyalty, and AI helps deliver that speed.
The top business goals for AI adoption reflect a mix of customer-centric and operational priorities: increasing customer satisfaction (35%), improving mobile worker productivity (31%), improving safety outcomes (27%), shifting from reactive to proactive maintenance (26%), and increasing revenues (25%). These goals show that field service leaders view AI as a multi-faceted enabler, not just a cost-saving tool.
Workforce challenges threaten adoption
Despite the enthusiasm for AI, the survey reveals a troubling trend: two-thirds (66%) of leaders report increased mobile worker turnover during the past two years. The number one driver of this turnover is insufficient training or support when new technology is introduced. Companies are deploying AI solutions faster than they are preparing their workers to use them, leading to frustration and attrition.
This disconnect between technology adoption and workforce readiness is a critical blind spot. Field service professionals are not rejecting AI itself; they are reacting to being left unprepared. The research suggests that companies must prioritize investments in AI-related employee training to retain talent and ensure smooth adoption. A well-trained workforce is more likely to embrace AI as a helpful assistant rather than view it as a threat or a burden.
Training is not the only workforce issue. Data silos are severely limiting the effectiveness of AI in the field. Three-fifths (61%) of organizations say mobile workers have limited access to the customer data they need to act on AI recommendations. Even if workers are trained, they cannot deliver value if data is trapped across disconnected systems. AI tools need context, and context requires access to relevant, accurate, and timely data.
The problem is compounded by operational gaps. The report found that 49% of workers lack a clear process for converting service visits into sales leads, 44% have limited ability to quote in the field, and 38% struggle with accepting payments. These are not AI failures but system integration failures. Without a unified view of customer history, inventory, pricing, and billing, even the most advanced AI recommendations fall flat.
Legacy technology stacks remain a barrier
One of the most revealing statistics is that only 16% of field service organizations have their field and back-office technology united on a single platform. Meanwhile, 52% still rely on spreadsheets and 43% use manual paper logs. This fragmentation makes it difficult to measure AI ROI and to deliver a seamless experience for both workers and customers.
Although 85% of field service leaders say they measure the ROI of their AI investments, nearly half (40%) struggle to determine whether AI is actually working. The root cause is a lack of integration. Data is often scattered across hundreds of applications, making it impossible to connect AI outcomes to business metrics. The average enterprise has over 1,000 software applications, and only 28% of firms share employee and customer data across the business.
This legacy stack also hampers the ability to act on AI insights. For example, if a mobile worker receives a recommendation to upsell a service, they need immediate access to customer history, pricing, and payment systems. If that data lives in separate silos, the recommendation becomes useless. The integration challenge includes connecting mobile apps, inventory management systems, GPS tracking, sensor data, and backend databases. Solving this requires a strategic approach to technology architecture, not just incremental patches.
AI ROI is real but uneven
The research shows that AI can deliver substantial returns when deployed correctly. Key benefits cited by leaders include higher mobile worker productivity (43%), improved customer satisfaction (40%), fewer safety incidents (34%), and increased revenue from field operations (39%). Faster response times for customers (39%) are also a significant benefit, which directly contributes to loyalty and repeat business.
The biggest positive impact is seen in scheduling and dispatch. Organizations using AI for these tasks report a 57% increase in revenue per job, driven by a 57% increase in mobile worker productivity and a 49% reduction in labor costs. These numbers are compelling and explain why so many field service organizations are eager to expand their AI initiatives.
However, the unevenness of these results is a warning. Companies that have invested in integration and training are seeing the benefits, while those with fragmented systems are struggling to prove value. This divide is likely to widen as AI becomes more sophisticated. Leaders who fail to address foundational issues will find themselves at a competitive disadvantage, even as industry-wide adoption approaches 100%.
Partnerships and digital labor
Field service leaders are increasingly looking for external partners to accelerate AI adoption. Interestingly, cost is not the primary concern. The factors that matter most when selecting AI partners include transparency into how AI makes recommendations (34%), data security and privacy (33%), quality of support (33%), external validation (32%), and speed of deployment (32%).
This emphasis on transparency and validation suggests that trust is a major issue. Field service organizations want to understand how AI arrives at its conclusions, especially when those recommendations affect safety, scheduling, or customer interactions. They also want assurance that their data — much of it sensitive customer information — is secure.
The survey also highlights the concept of AI agents as digital labor, not just tools. With 85% of field service teams looking to increase AI investments, there is a clear recognition that AI will play a central role in the future of work. But the research warns that this transformation is not just technological; it is relational. Improved relationships with employees and customers require investments in training, data foundations, system integration, and a culture that values speed and positive outcomes.
Companies that successfully embrace AI will be those that keep their people focused on building trustworthy, long-lasting relationships. AI can handle routine tasks, optimize schedules, and provide recommendations, but human judgment and empathy remain irreplaceable in service interactions. The best approach is to use AI to augment human capabilities, not replace them.
The path forward is clear: field service organizations must address training deficits, break down data silos, and modernize legacy systems. Only then can they fully capitalize on the promise of AI. The technology is ready, and adoption is high, but the groundwork is still incomplete. Leaders who act now to build integrated, AI-ready operations will be the ones who define the future of field service.
Source: ZDNET News