How Machine Learning and AI Are Revolutionizing Autonomous Field Services

It wasn't long ago that field service technicians had only themselves and their manually operated equipment to complete a job. When remote diagnostics tools came into the picture, a new chapter in efficiency and delivery on customer expectations emerged.

Additional developments included remote learning tools for technicians and even augmented reality for training and precision on the job. Software for improving routing and dispatching also emerged, as well as tools for improving visibility into customer data.

What do all of these have in common? Even the most sophisticated of these technologies depend on human cognition for them to function.

What are Machine Learning and AI?

Leading-edge field service experts use Artificial Intelligence (AI) to improve all aspects of service delivery, even without prior programming. AI solutions can simulate any number of scenarios designed to meet specific objectives. They represent the next and most prolific phase in the evolution of field service technologies.

It's important to note that Machine Learning is markedly different than AI. The advantage of Machine Learning itself is that these technologies can improve the ways in which they complete tasks without additional programming. That means initiating a task in and of itself can lead to improvements to the way in which that task is completed.

You may notice that Machine Learning still requires some form of cognition to both begin and then improve a process. Yes, " some form of cognition." As you can imagine, this implies that there exists a form of cognition outside of human beings that can begin processes--namely, AI.

Artificial Intelligence is a missing link in revolutionizing autonomy. Where autonomous devices all but completely absorb the necessary actions of a task outside of cognition, AI can take on the cognition portion as well--eliminating the need for human involvement.

Despite being modestly unsettling, AI is revolutionizing the business and will continue to do so as it evolves.

How do Machine Learning and AI Work?

It's difficult to grasp exactly what in field service "look like." Essentially, AI is a cognitive system that identifies the best possible path to a desirable outcome. Systems capable of Machine Learning can respond by delivering that outcome with no initial programming involved, and optimize the process continuously.

Another way to consider how Machine Learning and AI work is by comparing them to manual processes. Traditionally, field service teams or even sensors would collect data manually and enter that information into a database to be studied later. These tools manage the entire lifecycle of that data--collection, analysis, improvement, and application--increasing performance in specific areas on the path to a desirable outcome.

How Does Machine Learning and AI Help?

AI can be applied to several software-based functionalities of field service operations. One of the most common improvement areas is route optimization , which reduces travel time and improves logistics.

However, field service companies can use AI for more advanced purposes. They can also optimize their methods for achieving certain business objectives. This can include anything from achieving compliance statuses to growing revenue.

What's an Example of Machine Learning and AI in Practice?

In February 2017, PwC shared an example of Machine Learning and AI in practice. A family-owned business implemented AI in collaboration with PwC and Google Cloud. As a result, the company reduced redundancies and return service visits by up to 20% by ensuring jobs were always complete before technicians left the site.

The company also launched an AI-driven inventory system that helped them reduce inventory levels on their trucks by 35%. That helped make service calls less costly and more expedient, improving customer satisfaction.

Generally, the team worked with PwC and Google Cloud to identify problem areas and remedy them with AI. The intelligent systems integrated directly with the company's ticketing system to solve problems; these systems can be integrated with most other process-driven systems as well.

What's Next for Machine Learning and AI?

According to Fortune , 48% of human workers will eventually be replaced by robotics and software automation, and 76 million U.S. jobs will disappear in the next two decades.

Bleak as this may seem, new technologies require new responsibilities among humans, even if we cannot visualize them yet. The facts simply indicate that this and other industries are entering a period of disruptive and rapid transformation. The most forward-thinking field service organizations will determine what the future of the industry and its human staffs will be.

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Machine learning and AI are set to be hot topics at Field Service USA 2018, taking place at Palm Springs, California, this coming April.

Download the Field Service USA 2018 Agenda to learn more.