Posey's Tips & Tricks
The Future for AI Adoption, Part 1: Hype
Here's how a new tech takes off in the early stages of its life.
As someone who has spent 30+ years working in IT, I have seen plenty of technologies come and go. Every once in a while however, a technology comes along that proves to be wildly popular and receives mainstream adoption. In some cases, these technologies even go so far as to become a ubiquitous part of our daily lives. I'm talking about things like Wi-Fi, smart phones and even the Internet itself.
As it stands right now, AI seems to be poised to be the next every day use technology for the masses. Conversely however, there are those naysayers who claim that AI will be nothing more than a fad. So will AI become an every day use technology, or is AI an overhyped technology, much like 3D TVs or Google Glass? More importantly, where are we as a society in the AI adoption process?
One of the things that I have noticed over the years is that every time a new technology becomes almost universally adopted, the adoption process seems to follow a very distinct pattern of events. The same can also be said for overhyped technology products that have failed.
Of course, I am not the only one who has noticed that there is a pattern. Gartner has created a chart that it calls a hype cycle. It's essentially a map of the technology adoption process that is intended to help organizations understand the risks associated with adopting new technologies so that they do not adopt a new product too soon or give up on a recent adoption too quickly. The Gartner Hype Cycle consists of five main steps:
- The Innovation Trigger - this is basically just the announcement of a new technology. It could come in the form of a press release, a product announcement, or even a startup company that manages to attract lots of attention. In any case, the Innovation Trigger is the thing that gets people talking.
- The Peak of Inflated Expectations - This refers to a situation in which organizations rush to adopt a new technology, but without having a lot of proof that the technology can actually deliver on its promises. Cloud computing is a great example of this. Organizations rushed to the cloud based on the idea that running workloads in the cloud would save vast amounts of money. Often times however, these cost savings never materialized. In some cases, it actually cost more to run certain workloads in the cloud than to run them on premises.
- The Trough of Disillusionment - The Trough of Disillusionment occurs when all of the hype begins to wane and reality starts setting in. Even if the technology works exactly as it should, the odds are slim that the technology, no matter how good it might be, will ever be able to live up to the massive amount of hype that has been generated.
- The Slope of Enlightenment - The Slope of Enlightenment occurs when those who have adopted the technology in question begin to realize that even if the technology didn't live up to all of the initial hype, there are benefits associated with using the technology. At this stage in the process, organizations begin to discover some beneficial use cases for the technology and start getting a better feel for how best to incorporate the technology into the organization.
- The Plateau of Productivity - The plateau of productivity is the last stage in the cycle. Think of this as being the point at which the rest of the world catches up to the early adopters and begins to realize the true benefits of technology outside of the initial hype. This is often the point at which the technology begins to become mainstream. It's also often the point at which vendors begin to release a next generation version of the technology.
The Gartner Hype Cycle isn't perfect. For one thing, it assumes that the technology in question will receive mainstream adoption, which doesn't always happen. More often, technologies fall by the wayside without ever hitting critical mass.
Of course the opposite can also happen. Every once in a while, a technology is almost universally adopted. Every time that I have seen this happen, there is a very predictable chain of events that occurs. As such, I want to talk about this chain of events as it relates to AI adoption. I will explain how all of this works in Part 2.
About the Author
Brien Posey is a 22-time Microsoft MVP with decades of IT experience. As a freelance writer, Posey has written thousands of articles and contributed to several dozen books on a wide variety of IT topics. Prior to going freelance, Posey was a CIO for a national chain of hospitals and health care facilities. He has also served as a network administrator for some of the country's largest insurance companies and for the Department of Defense at Fort Knox. In addition to his continued work in IT, Posey has spent the last several years actively training as a commercial scientist-astronaut candidate in preparation to fly on a mission to study polar mesospheric clouds from space. You can follow his spaceflight training on his Web site.