The tech industry has undergone significant change in recent times. Disruption to business during the pandemic led to a sudden acceleration in digital transformations. Connectivity became critical to ensuring companies continued operating.
Five years on and networking technologies are relied upon more than ever for communication and collaboration between employees in multiple locations. Companies are having to oversee whole systems of distributed IT assets.
This is where field service management comes in. Tracking, monitoring and securing assets can be challenging, but FSM solutions enable companies to make knowledge about the operational status of these assets available to field service personnel.
According to a 2024 Verdantix report, the global field service management software market will grow from a valuation of $3.83bn in 2022 to to $7.16bn 2028 as more companies look to software solutions that boast enhanced capabilities.
Automation in field service management is nothing new – companies have been utilizing field service management software for efficiency gains several years now. However, AI is taking field service management one step further.
Automating scheduling and dispatching
While IT budgets are expected to increase in 2025 after a couple of fallow years, the industry is facing price hikes. On top of this, American businesses face the possibility of having to pay more for imported IT equipment in the wake of the Trump administration’s tariffs, albeit there’s a temporary reprieve in place. Companies are looking to optimize their costs and are trying to eke out as much as they can from their field service management software so they can deliver more for less.
AI is being deployed in field service management solutions to handle the various stages of field service, from simple form filling to booking a technician to install of fix equipment, Lee Blackwell, head of field operations at Infinity Group, tells ITPro.
“AI-enabled field service management helps businesses to automate time-consuming manual tasks that deliver no value for stakeholders and customers and can make field technicians’ jobs more efficient,” says Blackwell.
Field service management software can leverage real-time data to coordinate resources and ensure optimal scheduling and dispatching. For example, real-time traffic data can be used to determine the best travel route to a job so that the right technician is dispatched depending on their location.
AI can also improve response times by dynamically adjusting schedules based on real-time events, such as traffic delays. If a technician is dispatched but then held up, the nearest available technician could be alerted to respond to the job instead.
“Scheduling and resource allocation are notoriously difficult to solve using traditional computing methods, but are something that AI excels at,” says Jeff Watkins, CTO at CreateFuture.
Predictive maintenance
Smart scheduling, routing and dispatching can be made even smarter when combined with insights from Internet of Things (IoT) devices about the performance and condition of assets.
Equipment connected with sensors can provide data that AI can use to identify patterns of behaviour and turn these into actionable insights. The sensors can automatically report the condition of equipment to a system that then automates the dispatching of technicians.
This equipment can either be fixed based on their performance level or machine learning models could be trained to predict when a fault might occur and then a technician could be alerted and dispatched before it actually occurs.
“Predictive maintenance is one of the biggest wins [of AI-enabled field service management]," Watkins tells ITPro. "By spotting potential problems early, businesses can address them before they lead to costly – both monetary losses and reputational damage – outages."
Blackwell says that “moving from a reactive model to one that enables a proactive response can deliver incredible benefits for both field technicians and customers”.
Furthermore, adds Watkins, generative AI assistants, where retrieval augmented generation (RAG) has been integrated into large language models, can inform predictive maintenance strategies by helping field technicians to better understand why faults occur.
“Being able to question and intelligently search through vast swathes of field manuals, schematics, and existing problem reports can make root-cause analysis of issues in the field far quicker,” explains Watkins.
RAG-based generative AI assistants can result in faster resolution times and fewer errors and can also potentially fill the knowledge gap. The field service has an ageing workforce and seasoned personnel are being replaced by less-skilled, younger technicians that don’t have the same level of experience to lean on.
The need for better infrastructure
By 2028, two-thirds of businesses are expected to be using AI to manage their field services, according to research by ISG for its latest Buyers Guides for Field Service Management.
The more AI-enabled field service management software and IoT devices there are in the field, the more computing power that will be needed.
“Connectivity will be key to the future of field service management,” concludes Watkins.
“Rolling out better 5G and satellite internet infrastructure alongside cloud and edge computing capabilities will be crucial for AI-enabled field service management to reach its full potential,”
