Almost all field service organizations — 95 percent — now use artificial intelligence, and 85 percent intend to increase their AI investments over the next two years, according to a broad survey of more than 2,300 field service professionals across nine countries. The research paints a picture of an industry at a turning point: AI has reached critical mass, but legacy challenges around workforce training, data access, and system integration remain significant obstacles.

Key takeaways

• 95 percent of field service organizations use AI.
• Firms that deploy AI effectively see higher revenue per job and higher mobile worker productivity.
• Training issues and data silos can slow adoption and undermine ROI.

AI is delivering measurable results

For companies with connected systems, AI is generating clear business value. The survey found that 57 percent of organizations using AI-powered scheduling and dispatch report higher revenue per job and improved mobile worker productivity. Faster response times are also correlated with revenue growth, with 39 percent of field service leaders saying their organizations have seen increased revenue from field operations. Lower labor costs were cited by 49 percent of respondents as a benefit of AI in scheduling and dispatch.

These outcomes are not accidental. Field service organizations with unified data platforms and clear business objectives are better positioned to translate AI recommendations into operational improvements. The ability to route the right technician to the right job at the right time, with the right parts and full context, directly affects customer satisfaction and profitability.

Workforce strain and the training gap

Despite the positive results, the transition to AI is not frictionless. Two-thirds of field service leaders report increased mobile worker turnover over the past two years. The most commonly cited reason is insufficient training and support when new technology is introduced. Organizations are deploying AI solutions faster than they are preparing employees to use them, creating frustration and burnout among technicians expected to adapt without adequate guidance.

The lesson extends beyond field service. When employees do not understand how AI tools work or how to interpret their recommendations, they lose trust in the technology. That trust deficit can lead to resistance, lower adoption rates, and ultimately higher turnover. Companies that treat AI as a purely technical deployment often overlook the human change management required for sustained success.

Data silos and legacy technology

Training alone is not enough if workers cannot access the data they need. Three-fifths of organizations — 61 percent — say mobile workers have limited access to customer data necessary to act on AI recommendations. Data remains trapped across separate systems, preventing AI from delivering its full value.

The root cause is fragmented technology. Only 16 percent of field service organizations have field and back-office functions integrated on a single platform. Fifty-two percent still depend on spreadsheets, and 43 percent rely on manual paper logs. These legacy practices make it difficult to connect mobile apps, inventory management systems, GPS tracking, sensor data, and customer databases.

System integration issues also explain why many leaders struggle to measure AI return on investment. Although 85 percent say they measure AI ROI, 40 percent find it difficult to determine whether AI is working. With data scattered across more than 1,000 software applications in the average enterprise, and only 28 percent of firms sharing employee and customer data across the business, connecting AI outcomes to business metrics becomes a formidable challenge.

Business goals drive adoption

Field service organizations are not adopting AI for its own sake. The top business goals cited by survey respondents include increasing customer satisfaction (35 percent), improving mobile worker productivity (31 percent), enhancing safety outcomes (27 percent), shifting from reactive to proactive and predictive maintenance (26 percent), and increasing revenues (25 percent).

The push toward predictive maintenance is especially significant. Instead of sending technicians only after equipment fails, AI can analyze sensor data and historical patterns to predict failures before they happen. This shift reduces downtime, extends asset life, and lowers emergency repair costs. It also makes field service a more strategic function within the broader organization, as data from connected equipment becomes a source of competitive advantage.

Customer communication is a leading use case. More than half of field service organizations use AI tools for customer communication, and 51 percent use AI to support mobile workers in the field. The technology helps technicians understand the full context of a job, including customer expectations, site conditions, service history, and immediate requirements. This contextual awareness is especially valuable in an industry where speed and accuracy define customer loyalty.

AI ROI and revenue gains

The report shows that field service leaders are measuring the impact of AI across multiple dimensions. Key benefits include higher mobile worker productivity (43 percent), improved customer satisfaction (40 percent), and fewer safety incidents (34 percent). In addition, 39 percent of organizations report increased revenue from field operations, likely driven by faster response times and more efficient job completion.

AI-powered scheduling and dispatch stands out as the area with the greatest impact. Revenue per job has increased by 57 percent in organizations that use AI for these tasks. The gains are attributed to higher mobile worker productivity (57 percent) and lower labor costs (49 percent). Based on current adoption trajectories, the report suggests that 100 percent of field service organizations could be using AI by 2027.

But measurement remains a weak point. Although nearly all leaders track some form of ROI, many lack the integrated data architecture to tie AI investments directly to financial outcomes. Without a single source of truth, they cannot distinguish the effects of AI from other operational changes. This makes it harder to justify further investment or to identify underperforming AI use cases.

More work to do: training, data, and integration

While the adoption numbers are impressive, the report emphasizes that dissatisfaction is less about AI technology itself and more about how organizations prepare their workforce. Employees leave when they feel unsupported. Companies must prioritize AI-related training and create a culture that encourages continuous learning.

Data silos present an equally urgent problem. Even well-trained technicians cannot act on AI recommendations if they lack access to relevant customer data. AI tools require context; they need accurate, timely data to generate useful recommendations or execute tasks. Without a unified data foundation, AI investments will yield suboptimal results.

The report also highlights operational gaps related to system integration. Forty-nine percent of workers lack a clear process for converting service visits into sales leads. Forty-four percent have limited ability to quote prices in the field, and 38 percent struggle to accept payments on-site. These limitations are not caused by a lack of technician skill but by disconnected systems that prevent the flow of information between frontline workers and back-office applications.

Strong partnerships are crucial

Field service leaders recognize that they cannot solve these challenges alone. When selecting AI agents and technology partners, they look for more than competitive pricing. The most important factors include transparency into how AI makes recommendations (34 percent), data security and privacy (33 percent), quality of outgoing support (33 percent), external validation (32 percent), and speed of deployment and time to validation (32 percent).

Service leaders are increasingly viewing AI agents as digital labor rather than simple tools. Investing in digital labor is a top priority, with 85 percent of field service teams planning to increase AI spending over the next two years. However, the research suggests that successful AI adoption is not primarily a technological transformation. It is a relational transformation that requires aligned strategies, training investments, data foundations, system integrations, and a culture focused on delivering positive outcomes quickly.

What field service leaders should focus on now

For field service organizations looking to mature their AI programs, several priorities emerge from the data. First, invest in training before deploying new tools. Technicians need to understand not only how to use AI but also its limitations and how to challenge its recommendations when appropriate. A well-trained workforce will adopt AI faster and extract more value from it.

Second, break down data silos. This means moving away from spreadsheets and paper logs toward integrated platforms that connect field and back-office systems. It also means making customer data accessible to mobile workers in a format that is useful in the moment of service. AI cannot thrive in an environment where its inputs are incomplete or stale.

Third, measure outcomes with clear metrics. Leaders should define what success looks like before deploying AI, whether that is higher revenue per job, lower labor costs, improved customer satisfaction, or reduced safety incidents. Integrated data architecture is essential to this effort, allowing organizations to link AI actions to business results.

Fourth, choose partners carefully. The survey shows that transparency, security, and validation matter more than price. Organizations that treat AI vendors as long-term partners rather than transactional vendors will be better positioned to evolve their AI capabilities as the technology advances.

Finally, recognize that AI adoption is a change management effort. Leaders need to communicate the benefits of AI to their workforce, address concerns openly, and build a culture where employees see AI as an enabler rather than a threat. The companies that succeed will be those that invest in people and infrastructure, not just technology.

The findings from the field service industry offer a broader lesson for every sector. AI can deliver substantial returns, but only when it is built on a foundation of skilled workers, accessible data, and integrated systems. Organizations that neglect these fundamentals will struggle to move beyond pilot projects and achieve meaningful, lasting impact.

Source: ZDNET News