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Three Keys to Leveraging Artificial Intelligence in Field Service

The world of field service is evolving quickly. While the demand for field service work is skyrocketing, most providers are finding it difficult to scale to meet the ever-growing demands of the market. It appears that there are a couple key factors holding back growth—a limited availability of talent to provide services needed , and major constraints on the supply chain.

As field service providers look to scale their businesses, they are turning to technology like Artificial Intelligence to help them face the issues at hand and get the most out of their available resources. After all, who wouldn’t want to be able to do more with less? But, there’s a question that remains to be answered:  Will technology be able to deliver on its advertised promises?

There are endless technologies on the market that claim to leverage Artificial Intelligence (AI) and Machine Learning (ML). If you’re currently evaluating technologies to improve your business, here are three key considerations to keep in mind when making those important decisions.

1.  Identify Your Problems First. Technology Comes Second.

When you start your search for technology to accelerate your business, it’s extremely important to initially define your pain points.For example, is your upsell rate too low? Are you experiencing too many second truck rolls?

Many companies start the search technology-first. They start with ideas like “we should be using Augmented reality”. The problem with searching for technology is that you will definitely find it! The problem comes when attempting to apply that technology to your business.

It’s important to understand your problems first so you can identify a technology solution that is built to remedy those issues. If you choose a solution that promises the technology features you desire, but doesn’t properly address pain points, you’ll be left with more problems than when you started. There is a very large gap between a raw technology and a fully formed business solution. It’s important to find a technology that has already traversed that gap.

2. Don’t Make Your Technician’s Job Harder

The key to success in any field service business is adoption. Even more so when you are looking to adopt a technology that relies on the data collected in the field. It can be tempting to want to turn your technicians into walking data entry clerks, with 100-step checklists that gather every datapoint in every single circumstance. Please, don’t do this. If your entire workforce doesn’t immediately resign and go work for your competitor, you would almost certainly end up with completely useless  data being entered. We’ve all heard the old adage, “Garbage in, garbage out.”

When looking for technologies that leverage data, make sure it offers an intuitive and easy-to-use experience for your technicians. Technologies that seriously consider user experience will be the cornerstone of successful data capture in the field.

3. Find Short-Term Wins

Long-term strategy is important. As a matter of fact, it’s critical to the continued success of any technology adoption. However, oftentimes ML/AI projects promise wins that will only come to fruition once months, or even years, of data is collected and analyzed. This can be detrimental to your technology project. Machine Learning technology is often expensive or difficult to implement (which can be its own red flag,) but if your company isn’t getting any value in the short-to-medium term. It’s very easy for a ML/AI  project to lose support and die before you can deliver the promised long-term value.

When you’re roadmapping your strategy, make sure to find ways to provide small wins as soon as possible to stakeholders. When you’re asking a group of people to change their processes, focus, and do your best to provide value back to those users as soon as day one, if possible. These small wins along your path can keep your project alive and momentum strong. Detours are also encouraged along the way to your ultimate objective, so long as they provide valuable returns.

Conclusion

There is no “one size fits all” approach to adopting machine learning and artificial intelligence. There isn’t a single  technology or provider that will solve all of your problems. The key takeaway is to make sure you stay laser focused on using technology to provide tangible business value to your company.  Most importantly,  remember—the best technology in the world that no one adopts is actually the worst technology in the world.


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