The eternal hunt for field service excellence has recently been bolstered by the rapid rise of Artificial Intelligence as a major tool in the arsenal of the field service organisation writes Aly Pinder…
The emergence of the Internet of Things (IoT) has led to the next big challenge for service organizations and manufacturers. How can we make sense of the data we now have access to? From executives to the front-line field service technician, the ability to turn data into actionable insights will become the measuring stick for sustained success.
Leverage
To take this leap from data points to insights, organizations are ramping up quickly to leverage artificial intelligence (AI) to ensure volumes data (flow, sensor, vibration, temperature, or other data) can be mined quickly, accurately, and autonomously. When asked in a recent IDC survey, manufacturers listed Big Data and AI as a 4.06-level of importance (on a 1-5 scale, 1-not at all important,5-very important) regarding technologies integral to their service innovation journey.
The increase in importance should come as little shock to most as technology become ubiquitous in our daily lives, however what is interesting is the impact AI is having on service broadly and field service specifically.
As organizations evolve service business models to be less reactive and more proactive or predictive, the ability to leverage real-time data across a complex network of inputs is becoming critical for this transition.
Being reactive or break / fix merely requires a customer or an operator to call the service desk and report an issue. But in order to truly be predictive or prescriptive with service prior to a failure, organizations must leverage performance data to allocate resources, trigger a service event, and schedule the service to be delivered.
Organizations are looking to AI to explore field service excellence in some of the following ways:
- Better service planning and execution – How often have we talked about the “rights” of field service; right part, right tech, right skills, right time, right resolution. As much as we’ve commented on this and attempted to reach this utopia, many organizations still miss. AI connects the dots between each of the inputs across field service execution to provide the intelligence necessary to make the correct decisions, each time.
- Customer experience optimization – Even at a global scale, manufacturers and service organizations are finding they need to personalize service experiences for their customers. AI is enabling organizations to segment customers and deliver the level of support desired. Not every customer wants the closest technician, some just want to see the tech they’ve built a relationship with over the years. AI can and should be used to identify customer needs along with how best to resolve an issue. Should you resolve an issue remotely, or send a field service technician, or notify the customer directly with a customer support agent to walk them through the fix?
- Self-healing and suggestive preventative maintenance – As service organizations embrace servitization or product as a service models, they will need to deliver uptime and outcomes. Analyzing asset performance data and anomalies at scale provides the bridge to these new autonomous field service business models. But AI also provides the reporting capability to support the dashboards and details which will validate these premium services. Without capturing data points and rationalizing the service being delivered, customers may not understand why they are paying for service when they don’t actually see a failure occur.
I look forward to seeing how field service organizations take advantage of AI to take this leap and meet customer expectations for an enhanced service experience.
