Who has ever played the game of drawing a line from the mouse to the cheese through a seemingly impossible maze? Do you still remember the trick? Spoiler, don’t start at the mouse but begin at the cheese. Start with the end result in mind.
Running a transformation journey in business isn’t any different. True, the endpoint may be a moving target, but still, to a major extent, your journey is defined by a business objective.
Why am I bringing up this topic? At the Field Service News Live Symposium in Birmingham, UK we talked a lot about AI. If AI is the cheese, then the technician is the mouse. We know that no matter how much technology we deploy, as long as we live in a world of physical objects, ultimately somebody will need to hold the wrench.
Preparing Technicians for work with AI data
That somebody who is holding the wrench is Jack Ogden. Jack works for Beckman Coulter and was awarded technician-of-the-year by Field Service News. To me, technicians are the true heroes in the service domain. They keep the world running. They are the heroes on site. To be successful they need the right tools, correct information, and empowerment to perform their magic in the face of ever more demanding asset owners. What do we give them? State-of-the-art field service management software? AI?
I believe AI has substantial potential in the service domain. Though I may not have a comprehensive understanding of what AI means, the fact that during the service lifecycle massive amounts of data are generated, I can imagine that deep learning and mining tools can lead to better and optimized decision making.
In a recent ride-along I did, the focus was on having reliable asset data that allowed them to be more efficient and proactive. The sheer amount of assets and the long lifecycle makes it practically impossible for humans to consume the data and to prepare for each eventuality. Tribal knowledge and experience drove historical service execution. Today’s ambition for this company is to empower every service employee to make informed decisions.
Navigating the maze
When we were children we found joy in navigating the maze. Through trial and error, we ultimately found a strategy. In business, the stakes are a little higher. Still, there is a lot of trial and error with new technology adoption. But we can be smart at it when we have the end goal in mind.
With AI we don’t want to repeat the same mistake a lot of companies made with big data—big investments that result in unused data lakes. According to Forrester, between 60% and 73% of all data within an enterprise goes unused for analytics.
If you aren’t sure exactly what the end result is, you can define it as the business desire to facilitate informed decision making over assumed decision making, as we did on the aforementioned ride-along.
Making an impact with asset data
AI has the capability to convert huge amounts of data into intelligence. The quality of intelligence is based on what you feed into the algorithm. Simple rule: garbage in, garbage out (GIGO). Before you start you have to be very conscious about your data. This was illustrated in 2018 by a failed AI implementation at Amazon. When you use historical data to teach the algorithm, beware that the decisions made by AI will be a compound of all bias and mistakes made in the past.
This brings me back to Jack. If Jack wants to make an impact, he needs asset data. If the volume of asset data is big, it would be a great help to Jack if AI could make suggestions. Not decisions, but suggestions…because ultimately it is Jack who is holding the wrench.
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