How AI could close the growing gap in field service efficiency

Field service teams responsible for maintaining complex equipment often operate with limited resources and assume they have already squeezed out every possible efficiency.

 

Aquant’s 2026 Field Service KPI Benchmark Report suggests otherwise. The data shows that scaling expertise across the workforce could unlock up to 26 per cent in service cost savings without increasing headcount.

 

The report analyses nearly 30 million service events, seven million assets and $8.3 billion in service costs across 161 organisations. The findings reveal a growing divide within the industry.

 

Equipment manufacturers that operate with a unified intelligence layer and agentic AI platform are outperforming those still constrained by fragmented data and knowledge concentrated among a few experts. As a result, organisations that appear similar on paper are operating with dramatically different cost structures.

 

A performance gap that cannot be ignored

 

The benchmark quantifies challenges that many service leaders already recognise.

 

Industry-wide, first-time fix rates average 77 per cent, yet leading organisations reach 88 per cent, while underperformers fall to 60 per cent – a 28-point difference in resolving issues correctly on the first visit.

 

Similarly, the average time to resolution sits at 4.5 days. Top performers close cases in 2.5 days, while lower-performing organisations take up to 10 days.

 

Failed visits – when technicians are dispatched but the issue remains unresolved – also carry a heavy cost. These visits represent 25 per cent of service costs for the median organisation, but climb to 44 per cent among laggards, compared with just 14 per cent for top performers.

 

Each of these metrics has a direct financial impact: repeat work, unnecessary dispatches and growing customer frustration.

 

The costly impact of unnecessary truck rolls

 

The benchmark also highlights a long-standing inefficiency within field service: dispatching technicians when an on-site visit is unnecessary.

 

According to the data, one in five service cases could be resolved remotely. Avoiding these visits could reduce service costs by up to 18.3 per cent.

 

Every unnecessary truck roll adds avoidable cost to operations. Yet many organisations still lack the consolidated service intelligence needed to determine when remote resolution is possible.

 

Why expertise, not headcount, is the real bottleneck

 

The benchmark identifies workforce stability and knowledge distribution as key drivers of performance.

 

Top-performing organisations retain 87 per cent of employees, compared with 66 per cent retention among underperformers.

 

Equally important is the difference in technician performance. In high-performing teams, the gap in first-time fix rates between the top technicians and the rest of the workforce is just 2.9 per cent. In lower-performing organisations, the gap grows to 10 per cent.

 

When knowledge is concentrated in a small group of experts, staff turnover becomes particularly costly. Each departure removes valuable experience, slows onboarding and leads to inconsistent service delivery. The report concludes that service excellence cannot scale when expertise lives in only a few individuals’ heads.

 

Makino: operational AI in practice

 

For Makino, a global leader in CNC machining, improving service performance was not a theoretical exercise but a business necessity.

 

Working with Aquant, the company transformed its service model within 18 months. AI agents embedded in its existing field service management platform converted two decades of service data into real-time guidance for technicians.

 

The results were significant. First-time-fix rates increased from 35 to 84 per cent, while cases resolved within two days improved by 39 per cent. Remote resolution also rose by 15 per cent, reducing the need for on-site dispatches and lowering internal service costs by 10 to 15 per cent.

 

Rather than adding another point solution, Makino consolidated its service data into a single intelligence layer using Aquant. The company introduced a simple “Ask Makino” button within technicians’ workflows, allowing engineers to ask questions in plain language and receive context-specific answers. This approach helps newer technicians access expert-level knowledge and complete jobs faster.

 

From assistive AI to operational AI

 

The report suggests that 2026 marks a turning point for the industry. Instead of relying on assistive AI tools that support isolated tasks, organisations are moving towards operational AI, where specialised agents drive outcomes across the service lifecycle.

 

In this model, a dedicated intelligence layer sits on top of existing systems and data, continuously learning from service events and co-ordinating actions across the organisation.

 

This approach enables:

• More effective troubleshooting, reducing repeat work and failed visits
• Stronger knowledge capture, ensuring every solved issue improves future outcomes
• Better preventive maintenance, minimising unplanned downtime
• Continuous feedback loops that improve training, workforce planning and customer experience

It also opens the door to supporting a wide range of service workflows across the organisation.

 

Aquant’s modelling suggests that if more companies consistently performed at the level of top-tier organisations, typical service operations could achieve double-digit cost reductions, with savings reaching up to 26 per cent in some segments – an increase from 23 per cent the previous year.

 

What this means for service leaders

 

For operations leaders facing pressure to control costs without compromising uptime, the benchmark and Makino’s results suggest a clear strategy: 

• Consolidate service data into a single intelligence layer using a purpose-built agentic platform rather than adding point solutions
• Using AI agents to distribute expert knowledge so technicians across the workforce perform close to the top 20 per cent
• Focus on the biggest cost drivers first – failed visits, repeat work and unnecessary truck rolls
• Treat workforce retention and knowledge distribution as critical operational KPIs

 

As equipment becomes increasingly complex and experienced technicians retire faster than they can be replaced, the challenge is no longer access to data. The real differentiator is how effectively organisations apply that data.

 

Companies that successfully implement operational AI and distribute expertise across their workforce will not only run more efficient service operations – they will set the new benchmark for customer experience in complex equipment industries.

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Call Roger, Aquant’s Voice AI, to explore key insights, review specific KPIs or ask questions about the benchmark report: +1 (740) 245-6261