3 Ways AI Can Improve your Field Service Performance Today

3 Ways AI Can Improve your Field Service Performance Today

It’s no secret that artificial intelligence is a crucial component in the future plans of many service leaders. In a recent Gartner survey of leading field service organizations, more than 25 percent indicated they had AI or machine learning projects planned for the next 12 to 18 months.

But some organizations have struggled to translate the huge potential of AI into practical applications that generate immediate results. Don’t get lost in theoretical applications or potential benefits reaped over the next 5 to 10 years. Here are three use cases where AI is making a big difference in outcomes right now.

Examples of AI in Service Field Management

1. Service Triage

Like an urgent care facility, service teams need to prioritize jobs, available staff, parts, and more. And like the medical analogy, some clients present with clear problems that are easily remedied while others require further diagnostics.

What if your service teams could easily identify the root cause of issues before arriving on-site, and for complex problems, arrive at a job with more context and historical info to complete the job quicker? Service organizations can do just that!

  • Gather historical data (including data in your CMS, parts databases, and even free text from work orders) and apply machine learning to unlock significant insights.
  • Empower customer-facing agents to troubleshoot the problem while on the phone or online chat by going through a dynamic checklist that prompts questions and offers the most likely resolution simply based on customer input.
  • Dispatch the tech with the best skillset to complete the job, and ensure they have the right parts upon arrival.


2. Job Prioritization

Order in, order out—that’s how jobs have historically been prioritized. But service clients aren’t diner patrons waiting on the lunch special. Once you have an intelligent platform that can interpret large amounts of data, you can make tremendous strides in efficiencies.

A system that uses AI and machine learning can prioritize jobs based on customer needs and preferences, KPIs, longer-term business goals, and even technicians’ skills. Customers get the service they want and dispatchers and technicians aren’t spinning their wheels. Here’s how:

  • As part of ongoing service best practices, intelligent systems will continuously analyze service data including your work orders, parts inventory, customer contracts, product catalog, and technician notes to create a smart order.
  • Algorithms continuously scan your service requests and automatically generate a priority list for your customer tickets based on insights gleaned from your service data.
  • Priorities should be configurable based on your business priorities. A few key factors to consider when creating your parameters are:
    • Customer status
    • Job complexity
    • Parts inventory
    • Depot location
    • Geographic location
    • Workforce capacity


3. Parts Pickup Optimization

You’re probably tired of talking about stagnant first-time fix rates but study after study points to a lack of correct parts as one of the biggest contributors to this stubborn service challenge. Unfortunately, according to Salesforce, rates have remained flat at about 70-75 percent, on average, though many companies are averaging in the mid-60s percent, which takes a toll on truck roll costs and customer relationships.

The best way to boost rates is to ensure the right tech is dispatched to jobs with the right parts on the first visit. Take the guesswork out of what’s needed. Here’s how an AI-powered solution can make quick work of this manual headache using intelligent service triage and beyond:

  • Analyze known information about equipment, appointments, and technician availability and location to determine the parts needed before a technician arrives on site.
  • Automatically stream that information into WFM or scheduling systems for a 360-degree view for manager and employee visibility.
  • Use that historical and real-time info to assign technicians to the depot with the correct parts for jobs, based on distance and inventory.


In the future, as AI systems get smarter and faster, it’s easy to anticipate other significant gains, like more accurate predictions of failures before they occur. But there’s no need to wait to see the impact of AI on your business; start with ideas like these, and drive immediate results today! For more information, check out Aquant’s Service Intelligence Platform overview or contact us for more information and to get started.