Nelson Haung

May 3, 2019

Applying AI to Your Field Service Operations

Field service organizations today are playing a difficult balancing act. How can they effectively integrate modern technology into their operations while still maintaining a human workforce? Technology is emerging as a key driver for efficiency in everything from customer service to work origination – and large enterprises are responding by taking significant strides to digitize their operations.

In order to work faster and scale more efficiently, field service leaders are looking to manage the increasing volume of work by integrating AI and intelligent automation into their operations. The early return on these investments is significant – increased asset uptime, fewer site visits, and greater operational efficiency.

Common Applications of AI for Field Service Organizations

First-time fix rate is one of the key metrics that field service organizations track. Not only does it have a huge impact on customer satisfaction, but it helps keep costs low by reducing the number of truck rolls.

Back-office coordinators today are charged with managing both technicians and inventory. With so many moving parts to consider, there’s significant room for error that could potentially leave customers dissatisfied with their service. AI allows for an intelligent approach to technician scheduling and stock transfer that can lower mean-time-to-resolution.

In the case of an every-day fix or even emergency situation, your AI field service system can access historical information such as transit time or task duration to inform how best to solve any problem that arises. This allows organizations to operate with greater efficiency in terms of resource utilization, improving first-time fix rates and ensuring rapid responses to emergency situations.

AI can also innovate predictive parts management. Most field service organizations today operate on a reactive maintenance model where technicians visit sites based on a schedule tailored to service contracts or manufacturer recommendations. This creates additional overhead that’s often unnecessary as site visits result in mere confirmation that things are in working order, increasing the likelihood of future breakdowns. Specific assets end up being overlooked as maintenance schedules show little difference between locations with variable conditions such as climate or usage.

Implementing AI in Your Organization

Using AI for predictive maintenance can benefit every field service organization. Predictive maintenance helps avoid unplanned downtime, which experts found is 10X more costly than planned downtime. AI can automate these predictive maintenance efforts by looking at historical data, environmental factors, and real-time data from network devices to predict (and pre-empt) potential outages.

The benefits of AI are clear and numerous – but how do you go about actually implementing it across your organization? Your AI system will need to manage a high volume of tasks, as well as the spare parts and consumables used in the field.

Human input is also paramount to the success of implementing AI across your organization, so the fear that automated systems will replace employees is unfounded. AI and intelligent automation must be trained over time, a process that only works if humans are providing feedback of automated responses and recommendations.

The strategy most organizations land on is somewhere in the middle. By blending automated responses and AI-driven strategies with participation from your existing workforce, large enterprises can complete more tasks while using the same amount of resources, driving productivity and keeping both customers and employees satisfied with their service.

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