We left off last week discussing that leadership’s most important job is preparing companies, employees, and communities to deal with tomorrow’s change. We talked about new industry coming to Montana over the last couple of decades. This week, let’s touch on leading change in the context of established industries.
Leadership includes helping that industry, its workers, and affected communities adjust, and prepare to thrive in a new future rather than simply giving up and leaving everyone to fend for themselves. Leaders help create a better future, even if it’s a slightly (or substantially) different one.
Some leaders might think that it’ll take 20 years for that robotic equipment to make these imagined industry changes become reality, so they think they have plenty of time. They might be thinking “I won’t even be in leadership or political office 20 years from now, so why bother even thinking about it?” However, when we look at the rate of change in the capability and price of robotic technology over the last five years, “that’ll probably take 20 years” starts to seem a bit ridiculous.
I wouldn’t be surprised to see intelligent robots whose harvest is planned by a professional forester who reviewed robotically collected timber data from the site. This might involve some sort of mapping expert, even though the foresters I know are mapping experts. Maybe there will be someone to guide those robots similar to how a drone pilot guides a drone flying over the dangerous territory.
Perhaps this robot will be able to sense certain kinds of animal habitat, human habitat, watersheds, legal boundaries, bodies of water, etc. Maybe it will be able to detect data on animal movement (etc) and send it back to the “home office”. It’s possible that combining that data with other piece of data from some other machine or location could prove valuable to the logging company, the landowner, or someone else. Land has many uses and so does the data observed about it.
Where do the jobs go?
Somebody’s going to need to know how to repair those robots. We’re going to need to know how to train a company’s people to operate and maintain them, program them, etc. The vendor who creates them can educate them on all the different species that they would want to sell them to, you know, for customers who would need them. But there’s always localized information about that sort of thing.
“Localized information” could be data that comes from and/or is refined by people – perhaps from the same people who have walked that land for years. It may involve localized robotic programming or data curation of some kind involving a species expert. Robots will need educated timber firmware or something like that. The data will constantly change as weather, moisture, harvest, growth and other data changes.
Where does that leave the truck driver and the folks that are out in the forest doing this work? While some of it is dangerous, high-risk work, it’s also good paying work. Leaders can’t abandon those people, but they also can’t stop the change. Helping employees, communities, and companies adjust to these changes on a reasonable timeline before a crisis occurs is what change-ready leaders must do.
This is not just a leadership challenge. It’s a challenge for education and financial systems. The ability to see where their industry is going, and help students and employees avoid getting themselves pigeonholed in a career that’s disappearing is the responsibility of everyone involved – education, leadership, and the employees themselves.
There are numerous financial implications. You buy a house, a car and perhaps you buy or lease a logging truck. You hire a bunch of folks to get out in the woods and do the work, and then you find a competitor found a way to get their hands on one of these automated timber robots. Their margins might suddenly be much higher than yours. Either they make far more profit, or they undercut your price. You’re stuck because of your overhead.
What do you do? You don’t have a few million to buy robots. One solution is to look back at how these problems were solved in the past. We’ll take that on next week.
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