In my earlier blog, I asserted that the future of service is predictive. Artificial Intelligence (AI) — a very promising technology that derives insights from data — is poised to revolutionize service efficiency and customer experience for field service. And mobile is the perfect channel for delivering this service intelligence to the field, especially since technicians use their mobile apps heavily for delivering service. Thus, it’s not a surprise that the race for service intelligence is on.

So far, as an industry, we’ve barely scratched the surface of possibilities, but here are the most pragmatic AI use cases for field service mobility that service organizations can take advantage of today:

Automated Parts Predictions

Parts and labor are the two major cost pillars of field service. As a service leader, if you can impact one of them, it is a major financial victory. On a related note, Aberdeen research states that the No. 1 customer complain is not having the right parts for the job. If the technician doesn’t have the right part, not only do you get a dissatisfied customer, but the technician’s productivity is also impacted, incurring an extra $150-$1,000 cost for an extra truck roll.

Instead, the AI technology allows us to look at the work order and earlier service history to predict which parts will likely be needed, even before the technician starts for the job. With that prediction, the AI engine can send a push notification to technician to order the required parts ahead of time, while accounting for the backlog and shipping times. I daresay that this single use case alone can be pretty game changing for field service!

Automated Knowledge Base Suggestions

Undoubtedly, knowledge is power. In fact, solving customer problems is the most enjoyable activity for technicians. But with increasing complexity of serviced equipment, AI can be of great assistance by helping the technicians to diagnose and solve any issue.

Picture this: before the technician starts for the job, the AI engine sends the most likely diagnostics and suggested solutions for the work order to his phone. This is highly convenient and useful for the technician as he/she may not be aware of the latest and greatest resolutions used for a particular problem by his/her peers. After reviewing the suggested solutions or after closing the actual work order, the technician can rate the helpfulness of the suggested knowledge base articles, thereby improving the accuracy of the recommendation engine.

Automated Knowledge Base Articles Creation

Another interesting use case for the AI engine is to periodically scan the closed work orders for resolutions and when it detects a trend for similar work orders, a draft knowledge base article is automatically created for the review of knowledge base administrators. They can weigh in with their experience, add more details and publish a new knowledge base article, thereby mitigating the risk of tribal knowledge.

Automated Job Routing to Third-Party Technician Jobs

Gartner predicts that 40 percent of the service work is going to be done through third-party technicians by 2020. Given the expanded future role of third-party technicians, keeping them engaged with the right jobs to them is key to building a mutually profitable relationship. But your dispatchers usually don’t know the third-party technicians and their skills as well as they know your own ones. How do you make sure you are assigning them enough of the right work orders?

The AI engine can learn from the earlier work history of third-party technicians, build a recommended profile of the work orders they are well equipped to handle, and send them a mobile push notification when a qualifying work orders become available. With every additional work order handled, the AI engine learns more from the actual work history to make its prediction model more accurate. This use case expedites the dispatch process, increases the first time fix rate and makes the customers happy.

Parting Thoughts

In conclusion, the possibilities of applying AI to field service mobility are immense. I think the abovementioned use cases can serve as low hanging fruit for service organizations to amplify their service efficiency. But progressive service organizations will keep pushing the boundaries of service innovation by constantly applying the principles of automation, prediction and personalization to their mobile solutions. After all, to survive and prosper in any discipline, you do have to execute differently.