By Tom Paquin

This is part of an ongoing series of articles about the current State of Service going into 2022, along with the contributing elements that have and will continue to impact the industry in the years ahead. Read this to get caught up:

• The State of Service
• The Post-COVID Service Technology Stack
• The State of Field Service Management Software

A few years back at Field Service USA, a group of service professionals were discussing how comparatively subdued talk of IoT was, that year. This was the first indication in a long time that the concept of connected assets was finally moving from buzzword to ubiquitous component of service delivery. Comparatively mundane. But that is not to say that the story of IoT has ended, nor that we’ve reached a fully saturated market. It merely represents how our perspective has moved away from “let’s connect everything” to “What can I connect to be more successful?”

And that defines the current state of connected IoT—practicality trumps novelty for connected assets. Organizations are making informed decisions about what to connect to—not going to market with app-enabled coffee mugs.

Oh wait they’re still doing that? Never mind. BUT—in industrial applications, IoT adoption now is engineered around a few core capabilities. Let’s discuss some of them.

True Predictive
We often talk in abstractions about how predictive systems work with connected assets: If you see one weed on your lawn, we can extrapolate that in a week, you’ll have a dozen weeds. But what does that actually measure? The reality is that for most industrial assets, the amount of data generated by any one device is far too much to manage. So how do you extrapolate from that data what normal processes look like, build benchmarks and contingencies for repair, and make the right decisions for your customers?

At the very base level, you need to ensure that your assets are accurately measuring data. Once that has been established, you need a way to clean that data fast and actionalize it without it sitting, untouched, in a data lake. The solution for that is of course thoughtful AI processing, which, when given the opportunity to study practical business processes can cleanse data and form a complex web of conditional guidelines that can, in turn, automate job assignment, or inform remote repairs.

Augmented Augmented Reality
With those systems effectively in place, calibrated to understand your processes, you now have a new channel of service resolution that goes a step beyond basic remote assistance and telestration. By combining visuals for repair with back-end conditions, you have the ability to show someone how to resolve issues while understanding, on the back-end, what isn’t working, how to get it up and running, and what it’ll look like when it’s working. There are a myriad of business efficiencies that are inherently derived from this. Leveraging them will allow an organization to offer more value to their customers while simultaneously improving margins.