- February 27, 2019
- Posted by: David Marshall
- Category: Business, Management, Measurement
While I’m generally high on digital transformation in this blog and talking about the upsides and positive benefits that come from it, I’m well aware that there are downsides to digital transformation.
For one thing, artificial intelligence is not the panacea to all of your problems. You can’t just get an AI package, dump all your data into it, and have the answer magically appear on your screen two minutes later.
But a lot of companies think this is the solution to all their problems, even if they don’t actually quite understand what their problem really is.
For example, if you have a lot of waste in your manufacturing operation, and you want to reduce your disposal costs, rather than looking for someone who can manage the waste for less money, you could look for what’s causing the damage to begin with. An AI system can help you find the cause of all your symptoms as long as you’re actually measuring as much of the manufacturing process as you can, placing monitoring and measuring devices on your machines.
Another issue is that manufacturers don’t always understand what it is they want outcome-wise, which is different from not knowing the problem in the first place. If you set up an AI program without understanding the outcomes you want, and then applying the intellectual energy in designing the program, all this will do is give you bad data faster.
A badly-applied digital transformation can send you off in the wrong direction completely. One of the mistakes companies make is that someone in the organization decides to implement a new enterprise computer system. The organization will commit to it, but there are an awful lot of moving parts in an enterprise computer system. So if you don’t have intelligent, highly-motivated people who are committed to the development and have a firm understanding of the outcome you want, you’re going to have a mess on your hands.
You’ve probably heard of companies that bought the SAP computer systems and it almost put them out of business. They ended up spending tens of millions of dollars on it and eventually threw it out. That doesn’t mean the system was bad, rather the application of the product — the understanding of their outcomes and the problems they wanted to solve — were not clear.
AI means different things to different people, but you need to have a clear understanding of what problems you want to solve, what outcomes you want to see on the other end, and of course, to make sure you have buy-in from the top executives in the company.
I’ve been a manufacturing executive, as well as a sales and marketing professional, for a few decades. Now I help companies turn around their own business. If you would like more information, please visit my website and connect with me on Twitter, Facebook, or LinkedIn.
Photo credit: MaxPixel.net (Creative Commons 0)