Manufacturing gets most of the industrial AI attention. But field service operations—utilities, infrastructure maintenance, equipment servicing—face many of the same challenges: scarce skilled workers, complex scheduling, unpredictable conditions, and pressure to do more with less.

AI is quietly transforming how these operations work. Let me walk through what’s actually being deployed.

Intelligent scheduling and dispatch

Field service scheduling is a genuinely hard problem. You’ve got technicians with different skills, customers with different needs and time windows, travel times that vary with traffic, jobs that take uncertain durations, and emergencies that disrupt everything.

Traditional scheduling systems use rules and heuristics. AI-based systems can do better.

What AI scheduling delivers

Better matching: AI considers technician skills, certifications, experience, and performance history when assigning jobs. The right tech for the right job.

Route optimisation: Minimising travel time across the day’s jobs, considering real-time traffic, job durations, and time windows.

Dynamic reoptimisation: When emergencies occur or jobs run long, AI reoptimises remaining schedules on the fly.

Capacity prediction: Forecasting demand to schedule the right number of technicians and anticipate busy periods.

Real-world results

An Australian utilities company I’ve worked with implemented AI scheduling and saw:

• 15% more jobs completed per technician per day
• 22% reduction in travel kilometres
• Improved first-time fix rates (better matching of skills to jobs)
• Significant reduction in overtime

The technology comes from various vendors—ServiceMax, Microsoft Dynamics Field Service, IFS, and others all have AI scheduling capabilities. For some organisations, custom solutions deliver better results for specific needs.

Predictive maintenance for distributed assets

Utilities and infrastructure companies manage distributed assets across large areas—transformers, pumps, pipelines, communication equipment, you name it. Predicting when these assets will fail is valuable.

This is similar to manufacturing predictive maintenance but with additional challenges:

• Assets are distributed, making monitoring infrastructure expensive
• Connectivity is often limited
• Many assets are older with limited instrumentation
• Environmental conditions vary widely

What’s working

Condition monitoring with IoT: Sensors on critical assets transmitting periodic updates. Temperature, vibration, pressure, partial discharge, and other indicators depending on asset type.

Anomaly detection: AI learning normal behaviour patterns and flagging deviations. Particularly useful when you don’t have enough failure examples for failure-prediction models.

Remaining useful life estimation: For assets with sufficient data, predicting not just that failure is approaching but approximately when.

Risk prioritisation: When you can’t monitor everything, AI helps prioritise which assets to inspect based on age, criticality, usage patterns, and environmental factors.

Implementation reality

A water utility in regional Australia implemented predictive maintenance for their pump stations. They started with their 20 most critical pumps, adding vibration and power monitoring.

In the first year, they caught three impending failures that would have caused service disruption. The savings paid for the system.

They’re now expanding to additional assets, but carefully—the economics don’t support monitoring everything.

Work order intelligence

Beyond scheduling, AI can improve how work orders are created, managed, and closed.

Smart work order creation

When a fault is reported—by a customer, by a monitoring system, by an inspector—AI can:

• Classify the fault type based on description
• Predict likely causes and required repairs
• Suggest required parts and skills
• Estimate job duration
• Assign priority

This speeds up dispatching and improves the information technicians have before arriving.

Knowledge assistance

Technicians in the field face unfamiliar equipment and unusual problems. AI assistants can:

• Provide access to equipment documentation and history
• Suggest diagnostic steps based on symptoms
• Connect to remote experts when needed
• Capture knowledge from completed jobs

One telecommunications company implemented AI-assisted troubleshooting for their field technicians. First-call resolution improved by 18%—not because the AI replaced expertise, but because it made expertise accessible.

Automated documentation

Completing paperwork is a major burden for field technicians. AI can help:

• Voice-to-text for job notes
• Automatic photo annotation
• Pre-filled forms based on job type
• Compliance checklist automation

Reducing paperwork time means more time for actual service work.

Safety applications

Field operations involve real safety risks—working at heights, electrical hazards, confined spaces, traffic, weather. AI can help manage these risks.

Risk assessment automation

AI that reviews planned work against conditions and history to flag potential safety concerns. “This location had three near-misses last year during wet conditions. Rain is forecast.”

Fatigue and wellness monitoring

For operations where fatigue is a significant risk factor (think: long drives, remote work), AI systems can monitor for fatigue indicators and intervene.

Incident analysis

When incidents do occur, AI can analyse reports across the organisation to identify patterns—particular locations, equipment types, conditions, or procedures that correlate with incidents.

Customer-facing applications

For service organisations, AI also touches customer interactions:

Appointment scheduling

AI considering technician availability, customer preferences, job requirements, and travel optimisation to offer convenient appointments.

Automated updates

Customers increasingly expect real-time visibility into service appointments. AI-driven systems track technician location and job progress to provide accurate ETAs and status updates.

Issue triage

AI analysing customer-reported issues to determine urgency, route to the right team, and potentially provide self-service solutions for simple problems.

Implementation considerations

If you’re a utilities, infrastructure, or service company considering AI for field operations:

Data is prerequisite

These applications require data—work order history, asset information, technician data, customer records. If your systems are fragmented or your data quality is poor, that’s the starting point.

Integration complexity

Field service typically involves many systems—scheduling, CRM, asset management, GIS, inventory, HR. AI needs to connect to these systems. Integration is often the biggest implementation challenge.

Change management

Field technicians are often sceptical of new technology, especially anything that feels like surveillance or distrust. Implementation needs to focus on how AI helps them, not just how it helps management.

Start focused

Pick one application area—scheduling, predictive maintenance, or work order intelligence—and do it well before expanding. Trying to do everything at once usually means nothing works well.

The opportunity

Australian utilities and infrastructure companies face real pressures: aging assets, skilled labour shortages, customer expectations, and regulatory requirements. Doing more with less isn’t optional.

AI-enabled field operations can help—not by replacing people, but by making people more effective, reducing wasted time and effort, and preventing problems before they occur.

The technology is ready. The question is whether your organisation is ready to implement it thoughtfully.