Nearly all field service organizations now use artificial intelligence. According to a new survey of more than 2,300 field service professionals across nine countries, 95% of organizations report using AI, and 85% say they plan to increase their AI investments over the next two years. The research signals that AI has moved from experimental to foundational in field service operations. But the same study also reveals serious obstacles: employee turnover is rising, training lags behind deployment, and many organizations still depend on spreadsheets and paper logs. These legacy issues are preventing some companies from turning AI into measurable financial results.

Key takeaways from the research

• 95% of field service organizations use AI, and 85% plan to increase investments by 2027.
• AI-driven scheduling and dispatch is linked to 57% higher revenue per job and 57% higher mobile worker productivity.
• 66% of leaders report higher mobile worker turnover, citing insufficient training and support as the top cause.
• 61% of organizations say mobile workers have limited access to the customer data needed to act on AI recommendations.
• Only 16% have field and back-office technology on a single platform, while 52% still use spreadsheets and 43% use paper logs.

AI adoption has reached critical mass

Field service encompasses maintenance, installation, repairs, and on-site customer support across industries such as manufacturing, healthcare, utilities, telecommunications, and technology. The ability to dispatch the right person with the right parts and information at the right time is essential to customer satisfaction and revenue growth. AI can help by predicting equipment failures, optimizing schedules, recommending solutions, and guiding mobile workers through complex tasks.

According to the study, AI is now a core part of that work. More than half of field service organizations use AI for customer communication (54%), and 51% use it to support mobile workers in the field. AI tools are helping technicians understand job context, customer expectations, and immediate requirements before arriving on site. This contextual awareness is critical because speed and accuracy are the most important drivers of customer loyalty.

Measurable revenue gains for connected systems

The performance gap between organizations that have connected systems and those with fragmented technology is stark. Companies using AI-powered scheduling and dispatch report 57% higher revenue per job. They also report 57% higher mobile worker productivity and 49% lower labor costs. These gains are not merely incremental. They reflect the operational leverage that comes from matching the right technician with the right job, reducing travel time, and minimizing missed appointments.

Scheduling and dispatch are particularly well suited for AI because they involve many variables and constant updates. AI can analyze traffic, weather, part availability, skill requirements, customer preferences, and service-level agreements to create dynamic schedules that human dispatchers could not optimize manually. When field and back-office systems are integrated, AI recommendations become actionable and the resulting performance improvements show up directly in revenue and customer satisfaction metrics.

Business goals drive adoption

The study asked organizations about their primary business objectives for AI adoption. Increasing customer satisfaction was the most cited goal, at 35%. Improving mobile worker productivity followed at 31%, improving safety outcomes at 27%, shifting from reactive to proactive and predictive maintenance at 26%, and increasing revenues at 25%.

These goals are interrelated. Predictive maintenance, for example, can prevent breakdowns and improve safety while also increasing customer satisfaction. Better productivity can reduce labor costs and boost revenue per job. The common thread is that AI is expected to improve business metrics, not just automate isolated tasks. The research suggests that clear objectives and leadership commitment are essential to successful implementation.

ROI is measured, but attribution is difficult

Eighty-five percent of field service leaders say they measure return on investment for their AI initiatives. The most commonly reported benefits are higher mobile worker productivity (43%), improved customer satisfaction (40%), fewer safety incidents (34%), increased revenue from field operations (39%), and faster response times for customers (39%).

However, 40% of organizations say they struggle to measure whether AI is actually working. This disconnect is rarely about the quality of the AI itself. It often stems from fragmented technology landscapes. When data sits in separate systems, it is difficult to connect AI outputs to business outcomes. Only 16% of leaders say their field and back-office data lives on a single platform. The survey also found that 52% of organizations still rely on spreadsheets and 43% use manual paper logs, making it even harder to track the true impact of AI.

The workforce gap: training lags behind deployment

One of the most urgent findings is the impact of AI deployment on mobile workers. Two-thirds of leaders (66%) report increased mobile worker turnover over the past two years. The number one reason cited for this turnover is insufficient training or support when new technology is introduced. Many employees are not leaving because they fear AI; they are leaving because they are not given the tools and knowledge to succeed with it.

The problem is not unique to field service, but it is especially acute for mobile workers who operate with limited access to peers and supervisors. A technician in the field cannot easily ask a colleague for help with a new AI-powered application. Without proper training, AI-driven recommendations may appear confusing, untrustworthy, or even harmful. The study suggests that organizations must treat workforce preparation as a critical component of AI strategy.

Investment in training should include not only the mechanics of using AI tools but also the judgment required to evaluate AI recommendations. Field service workers need to understand what data the AI is using, what its limitations are, and when to override it. They also need continuous learning opportunities as AI capabilities evolve.

Data silos and legacy infrastructure remain major barriers

Even with better training, AI cannot deliver value without access to relevant and accurate data. The survey found that 61% of organizations say mobile workers have limited access to the customer data they need to act on AI recommendations. Trapped data means that workers cannot respond quickly, cannot personalize service, and cannot make full use of the AI tools they are given.

The scale of the integration problem is significant. The average enterprise operates more than 1,000 software applications, yet only 28% of firms share employee and customer data across the business. Field service organizations often run separate systems for mobile apps, inventory management, GPS tracking, connected sensors, work order management, and customer relationship management. These systems may not communicate with one another, leaving technicians without a complete view of a customer's history or equipment status.

The report also identifies specific operational gaps caused by these silos: 49% of organizations 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 payment in the field. Each of these gaps represents lost revenue and a weaker customer experience. System integration is the root cause. When data flows freely, AI can help workers identify upsell opportunities, create accurate quotes, and process payments without requiring a second visit or a follow-up call.

What leaders should look for in AI partners

As field service organizations increase their AI investments, they are also evaluating technology partners more carefully. The study asked leaders what matters most when selecting AI agent partners. Transparency into how AI makes recommendations ranked first, at 34%. Data security and privacy came next at 33%, followed by quality of outgoing support (33%), external validation (32%), and speed of deployment and time to validation (32%).

Cost is not the only priority. Leaders understand that AI agents will act as digital labor, representing the organization to customers and employees. They need to trust the system's reasoning, protect sensitive data, and ensure that AI deployments can be rolled out quickly and validated with measurable outcomes. Partnerships will matter as much as technology.

Looking ahead: from technological to relational transformation

Based on current adoption trajectories, the survey projects that 100% of field service organizations will use AI by 2027. But adoption alone is not success. The organizations that see lasting benefits will be those that prioritize training, break down data silos, integrate their systems, and build a culture focused on outcomes rather than tools.

The research suggests that AI in field service is not just a technological shift; it is a relational one. Employees who are supported with training and trusted data will deliver better service to customers. Customers who feel understood and served quickly will reward companies with loyalty and advocacy. The firms that effectively embrace AI will have the best people singularly focused on building trustworthy and long-lasting relationships.

Source: ZDNET News