Over the past few months, multiple executives of field service organizations (FSO) have been expressing interest and are excited to understand how their companies can leverage Generative Artificial Intelligence (GenAI) to benefit their field operations. This raises some very interesting questions and challenges. Considering that many FSOs are extremely risk adverse and struggle to make big organizational changes, is there a way to prepare teams to adopt this early stage technology?
AI has been around for a while and is already embedded within daily field service teams process.
Generative AI can provide even more value in some key use cases and ease those tedious processes and repetitive work, but there is a fair amount of work to be done to properly prepare for the full rollout of GenAI.
What exactly is Generative AI?
There are endless articles and content available, but in short, Generative Artificial Intelligence (GenAI) is a subset of AI that allows generation of content (text, images, videos) in a human-like pattern. It takes massive quantities knowledge base data and computing power to properly train these Large Language Models.
If we take a step back, we can see that FSOs have been leveraging multiple types of AI over the past few years. A few examples of these use cases are:
- Route Optimization – Leverages historical data on jobs, resources, skills, customers, traffic patterns and more to generate the ideal schedule for 1000s of field workers.
- Predictive Maintenance – Ensures assets and machinery are proactively serviced to prevent downtime or failure and extend lifespan.
- Remote Assist for Field Service Management – Virtual or real agents supporting field workers remotely, relying on image recognition technology to help make smarter decision on diagnosis and resolve service requests.
- Forecasting & Capacity Planning – Trained AI models on historical workload data and many external factors can predict seasonal demand for work and provide staffing recommendations.
These are just a few examples of how AI is already changing FSOs and many are on the path of adopting these currently.
The best tasks that can be automated with Generative AI are those that involve repetitive creation of reports or content with low creativity needed. When using GenAI there is a chance for inaccuracies in generated content and will require humans to review. GenAI can provide the most value for newer or unexperienced and newer workers, these are the people who can experience the largest productivity gains and benefit the most.
This helps give some guidance on use cases and the types of workers that can benefit from GenAI. At OverIT we’re already exploring and testing out these use cases with our customers.
- Execution in the field– Field technicians spend a lot of time capturing data of the work performed while onsite, and inputting into mobile forms or even paper. This is an error prone process but extremely crucial, for some companies without an accurate report they won’t get paid for the work.Generative AI can significantly speed up Work Order Debrief by capturing details in real time with simple voice or text prompts, validating the data and generating reports on mobile device.
- On-the-job-training – Junior workers needing to solve complex problems in the field can leverage a GenAI driven “chat-bot” to surface and summarize the steps needed to solve that particular issue. By training GenAI on company specific data, user manuals, asset performance and customer experiences history workers can get very specific instructions and guidance based on the issues they encounter. This can help resolve customer issues with unprecedented speed and improve customer satisfaction while providing training on the job.
- Interactive Schedule Optimization – Dispatchers and operation managers spend most their time organizing field workers’ schedules as unexpected changes occur throughout the day. Some of this can already be addressed today by leveraging Route Optimization, Generative AI can totally change this experience. The biggest hurdle to adopting Route Optimization is lack of detail into why and how changes to a schedule are made. GenAI can provide detailed insights and get real time feedback from dispatchers to ensure productivity improvements as the right schedules are created to meet the current business context needs or constraints.
How to prepare?
Around 40% of frontline workers are concerned about the impact of GenAI on their work. In reality, field workers have a lot more to gain from GenAI, than they could lose. Think of this as a Field Service Copilot, guiding and assisting field workers throughout their day.
First and foremost, FSOs need to be fully digitized, working on cloud-based field service management software to manage their schedules and debrief work. This can be a multi-year process with many complex stages of deployment and adoption. At OverIT we’ve had the privilege to take 100s of customers through this digital journey over the past 20 years.
We’ve learned that engaging field workers and dispatchers in the process early is crucial, getting their buy-in and helping them see how new technologies can make their jobs easier in everyday life. As they adopt these systems and recognize the value, they’ll be open for more change. The presence of GenAI in our personal consumer use cases will also make it more easily adopted in the workplace.
Augmenting GenAI with companies’ specific data, means that they first need to have all the relevant data stored and easily accessible to train their GenAI model properly. This requires having a strategy to capture and store data and then to consolidate systems so that the data is easily accessible.
Get Ready!
There’s work to be done, but many FSO companies are on the right path and asking the right questions. What will be the full impact of Generative AI on the workforce? We don’t think we fully know yet, there are lots of unknowns. In the end it’s just a tool, a very powerful tool with competitive dynamics, and now is the time to explore how we can use it to change our Field Service Management teams for the future.