Using AI to build a modern field service organization.

Field service organizations have traditionally operated under the break-fix model, responding to device failure after the customer reports an issue. This operating model has grown costly and inefficient and has proven less than effective in satisfying the customer's rising needs.





As the field service industry rapidly evolves toward a proactive and predictive model, cutting edge technologies including automation, artificial intelligence (AI), new learning tools, and augmented reality are helping transform field service by providing optimal device uptime, along with greater visibility, efficiency, and profitability.


Why AI for field service?

The ability to capture, digest, and derive actionable insights from data is critical in modernizing field service organizations. Intelligent, predictive systems driven by AI can automate manual, time-consuming tasks like collecting data, diagnosing problems, and identifying the best solutions to resolve issues. AI optimizes resource management, empowers field teams through mixed reality and mobilization, and improves customer service with proactive and predictive service. In short, AI is providing organizations with the ability to optimize every facet of field service delivery.


While service organizations lay frameworks of sensors and solutions to capture data across every facet of their organization, AI and machine learning represent the next steps that organizations are taking to leverage the value of the captured information. The ultimate goal is to transition from a reactive, break-fix service model to one that is proactive and predictive, achieving a near-constant uptime.


Three ways AI can transform your field service operations

Optimize resource management

Effectively managing resources, such as inventory and technician time, is at the heart of the challenge for any field service organization, creating a powerful, competitive advantage when performed well.


In a traditional field service organization, technicians are dispatched based on availability, not proximity to the customer or experience with a specified device. A technician may arrive onsite with limited access to customer information and device history, thus, impacting the ability to complete the repair on a first visit and driving up overall costs for the customer and the field service organization.


As an example, let's look at a manufacturing customer experiencing a boiler failure. When a work order is scheduled in an intelligent system, assignments are optimized using multiple factors such as a technician's experience in handling the specific failure, the customer's preferred technician, or the proximity to the site. Leveraging machine learning, this intelligent system can automatically assign the work order to the best available technician matching these criteria.


If the device affecting the boiler were an IoT-connected sensor, a real-time alert would be received, triggering an automatic service request. The system would attempt to self-heal the issue first and if unsuccessful, a technician would then analyze the data and commit a repair remotely, often without the customer ever knowing there was a problem. Sending the technician for an onsite visit would be the last option if the device could not be repaired remotely.


As more requests come in, the system would then optimize the technician's schedule to create the most efficient path to navigate, allowing the technician more time to perform additional calls per day and driving greater revenue. Real-time inventory management ensures that the replacement part is available on the scheduled date and that the technician has access to the tools needed to complete the repair. The system can identify the best parts to replace, where they should be purchased, and provide more accurate lead time predictions for the organization. Field service managers and technicians alike can synchronize and track inventory down to the truck level with real-time visibility to increase first-time fix rates.


Empower field technicians

To be the most effective and prevent costly return visits, technicians must have complete access to the information and real-time guidance they need. In our boiler repair example, the technician could utilize a digital twin of the device to learn about its status and operating condition and to train on the particular problem that requires repair. AI's cognitive capabilities can even help optimize repairs before a technician arrives, taking care of routine diagnostics and testing for common or similar issues. These capabilities ensure the technician is better prepared for the work and that his or her time and the customer is used efficiently.


The technician's mobile service app empowers the technician to better manage appointment schedules and access turn-by-turn routes to the customer site. Onsite, this app can highlight the top two to three possibilities that might be wrong with the device. Chatbots can assist in locating customer, product, and work order information. Mixed reality tools like HoloLens, can create 3D renderings overlaid directly onto the boiler, highlighting missing or broken parts and allowing the technician to view performance data. AI can use data from predictive analytics to make just-in-time recommendations. A headset can identify irregularities and help maintain focus on the right issues without having to stop and troubleshoot, ensuring work is completed correctly the first time. And the technician can obtain assistance from a more experienced technician through a video call if the issue is beyond their current skill set.

Improve the customer experience


Field service customers need stability and reliability in their businesses. They want visibility into their assets and need to minimize downtimes when breakdowns occur.


Prior to AI-empowered systems, the customer in the boiler example would have to actively reach out to report that the device had failed. Depending on the type of failure, work using the boiler could be slowed or blocked for days or weeks until a technician could complete the repairs. Without intelligent support, the technician may need to return for follow-up visits, unnecessarily wasting both time and money.


AI allows for automated, remote self-healing and predictive forecasting, monitoring and analyzing connected devices for potential issues. If one is identified, the system can remotely attempt to resolve problems through self-healing processes, like having the boiler restart itself during an off-time to mitigate an overheating failure. Using historical device data and predictive analytics, the system might make a recommendation to schedule a technician site visit to head off future problems, notifying the customer of the work order. The customer could then plan around the scheduled downtime and even track the technician's arrival to the appointment in real-time.


During the visit, using recommendations from the system, the technician would be able to discuss additional products and services with the customer that meet their specific usage and operating needs. The customer gains more control over their assets, and the field service organization is empowered to provide improved service delivery capabilities.




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