The Impact of AI, IOT, Machine Learning and Augmented Reality on Field Services

As we’ve discussed in previous blogs, it was just a matter of time before Field Services organizations moved from the early adopter phase to implementing and leveraging artificial intelligence (AI) and machine learning (ML) to automate processes and gain insights that enable greater predictivity for strategic business decisions. This post will share some examples of how the Field Services industry is using AI, ML, Internet of Things (IoT) and Augmented Reality (AR) to survive and even thrive in the massive “New Normal” disruption caused by the COVID-19 pandemic.

Previously, we discussed the benefits of AI and ML related to workforce productivity, customer satisfaction, first-time fix, and how Field Service organizations can improve their metrics in these key areas.

Now, let’s fast forward to today and the disruption from the COVID-19 pandemic. Three key topics should be top of mind for Field Service organizations going into 2021:

1. Utilizing IoT–connected devices with AI and ML to pivot from onsite, human-centric resources to intelligent AI and ML-enabled predictive, rather than reactive, tasks.
2. Shifting to a fully digital service experience using IoT, AI, ML, and augmented reality technologies.
3. Moving to robust, autonomous service apps that “Uberize” service delivery as the Field Service industry continues to experience a shortage of technicians.

Utilizing IOT-connected devices with AI and ML

Previously, we started by looking at service planning to meet customer service demand. Several variables should be taken into consideration when planning services based on demand. AI/ML can make a big impact here by using a combination of heuristics and predictive techniques to establish which underlying variables have historically been the most important factors in predicting actual demand. This enables Field Services organizations to effectively plan resources, mitigating overtime cost and minimizing the need for having to recruit contractors last-minute to meet customer-service needs.

Applying ML to historical data captured from service planning and customer demand forecasting helps realign territories to ensure adequate coverage is provided to meet customer demand. With the addition of IoT devices to the equation—measuring temperature, vibration, humidity and other environmental metrics, not only can we improve service and downtime from planned tasks, we can also routinely predict when a component will fail, based on continual, real-time measurements, and when we will need to plan for replacement of the failing or compromised component.

Using AI/ML and IoT together enables preventive maintenance to become predictive—by taking actual equipment condition into account when determining repair/replace schedules. Organizations that have implemented AI/ML use IoT censors to monitor equipment performance, capture data, and analyze it to identify any abnormalities. When performance falls below specific thresholds, work orders are automatically generated and sent to Field Service technicians to perform.

Shifting to a fully digital service experience using IoT, AI, ML, and AR technologies.

As we entered 2020, Field Service organizations were well on their way to providing paperless, digital processes for work order management, route planning, and invoices. The disruption caused by the COVID-19 pandemic forced these same organizations to pivot to completely paperless contactless delivery of service, electronic signatures, automated payments and constant customer communication. Although these moves were prudent from a cost and ROI perspective, they were also critical to ensure continuous operations and customer satisfaction while maintaining the safety of technicians and customers alike as a critical focus.

Field Service Organizations that made early investments in advanced technologies like AR and IoT benefited during the pandemic disruption, some of these benefits are as follows:

• Improving First Visit Fixes (or no visit at all). Sending technicians to a work site can involve significant cost. And in the current environment, safety and risk are additional concerns. By using AR in combination with real-time and historical IoT data, Field Service organizations have significantly reduced (or eliminated) the frequency of truck rolls by field service technicians by performing virtual service calls and guiding the customer through issue fixes via video stream without having to travel.
• Reduced Training Time. Field Service AR can be reduced by video-recording a service instance for future reference and adding to libraries of how-to guides for technicians to quickly reference visually—much like searching for a YouTube video to perform a simple fix on a car. These videos capture the skills and knowledge of the most experienced technicians and engineers and provide a training source for future technicians.
• Reducing Equipment Downtime. Providing customers visual instructions or a video tutorial for making modifications and repairs via a Field Service AR system can prevent travel delays and valuable downtime. This improves customer satisfaction and reduces production outages.

Moving to robust, autonomous service apps that Uberize service delivery

“According to McKinsey, Field Service organizations can reduce their personnel costs by up to 30%.”

According to McKinsey, as Field Service organizations make the move to fully-digital service delivery utilizing automation, personnel costs can be reduced by up to 30%. McKinsey also states the current shortage of field service technicians will be further exasperated—with up to half of all field service workers being freelance by 2025.

Field Service organizations that are investing in advanced technology to increase efficiency will need to insist that the applications and tools used by their workforce (both internal and external) be flexible enough to support variations of constituents. These constituents would encompass FTE resources, contractors, freelancers, and customers (as well as others, potentially). Much like an Uber driver, a field service technician may be freelancing for a number of service providers. The mobile tools must be safe, secure, and feature-rich, acting as a productive resource leveraging the same tools an FTE would have at their disposal, such as:

• Training, collaboration tools and documentation available on demand.
• Certification processes and the ability to inventory skillsets.
• Secure role-based access to customer equipment and IoT devices.
• Available mobility tools and wearable devices required for safety and services.
• The ability to perform AR and video-share with subject matter experts.
• The ability to perform all digital services to complete a job and obtain digital signoff.
• Ideally, the ability to let the customer rate their experience for future service calls.

Some practical use cases to consider:

• Training and Knowledge Management: Training and knowledge management is a prime example of something that can benefit from AI and ML. Providing online digital assets combined with digital manuals, historical service records, technician notes, videos of actual service repairs, and meta data, AI improves the performance and quality of training new technicians and helps them better navigate the information via an intelligent/learning search and by enabling the creation of interactive training tools based on real-world service data.
• Inventory Optimization: Inventory history and work orders—including failure codes that are already being collected—can be used as a foundation for new AI-based applications. An AI-enabled forecasting tool coupled with IoT data from an edge device can predict which equipment is likely to fail, what service will be needed, how frequently failures will occur, and what the replacement part or service will be—and will continue to learn over time.

We work with countless Field Services organizations. One common objective shared by all has been the optimization of field services delivery. To do this effectively, Field Services organizations need to understand what to optimize for. But without having all the Field Services engagement data—from planning to execution—fully understanding the impact of such a move can be challenging.

Naturally, this has implications for other service metrics: utilization and customer satisfaction rates will fall because time-to-service increased (though field service employee satisfaction may improve).

The ability for Field Services organizations to respond quickly and make informed decisions in the face of continually changing business environments and conditions is critical. Leveraging AI, ML, IoT and AR can help by analyzing data captured throughout the Field Services Lifecyle and enabling the adjustment of service and resource models, resulting in operational efficiencies and—more importantly—greater customer satisfaction.

If you haven’t given these technologies much thought yet, it’s time to start. Industry-leading companies are already having great success with these solutions. You can, too. For more info on why the time to update your Field Service Strategy is now, please click here to read our recent white Paper: 5 Reasons to Rethink Your Field Strategy NOW.

Please don’t hesitate to contact us for more information. Our Enterprise Mobile Field Services Modernization Strategy Kickstart may be just the boost your business has been looking for.