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Gartner has a tool known as the “hype cycle.” It shows where a technology stands on a curve that represents the lifecycle of that technology — including the peak of its “hype,” or inflated expectations, followed by the low point of missed expectations, or the “trough of disillusionment.” The lifecycle finally ends as the technology achieves what they refer to as the “plateau of productivity.”

AI is currently at the top of this hype cycle, discussed in every tech news cycle and mentioned with predictions of dire consequences from some of the greatest minds today. So, the question I’m sometimes asked is, “Why now?” Aren’t we headed toward that “trough of disillusionment” where we realize that Siri won’t cook dinner and Alexa won’t take out the trash next week like we’d hoped?

Ten years ago, it was a safe bet to hold off on investing in technologies at the top of their hype cycle. There were still several years before that technology would pay off, and many industry shakeouts could happen between that moment and when the technology began to pay dividends. Of course, this varied, depending on how critical this technology was to your industry and how quickly the technology was maturing.

This is where things have changed. The rate of technological change is rapidly accelerating. This is sometimes demonstrated by the speed of technology’s adoption. For example, the landline telephone — a phenomenal invention — took around 50 years for mass adoption. Its more recent cousin, the cellphone, took about a decade. With AI, what once seemed like science fiction could be with us quickly. Marshall McLuhan, a Canadian professor, known, among other things, for predicting the World Wide Web 30 years before it was invented, was quoted as saying: “First we build the tools, then they build us.” Will AI be able to accelerate its own adoption? Will this technology accelerate building an even better version of itself? (The answer, in both cases, is yes.)

There are many different views on when Skynet (the main antagonist of the “Terminator” franchise) arrives, but it is clear that AI solutions like virtual agents, machine learning, and robotic process automation are already making an impact. Machine learning is dramatically improving predictions in everything from fraud to what that animal is in the picture you just took. Virtual agents are exploding in usage. Look at Alexa, the most famous of the stand-alone digital assistants. Released in 2014, Alexa is expected to sell 10 million units in 2017. Now even light bulbs have Alexa built in. I’ve already written a blog about why AI is back; this blog is more about how quickly it’s moving.

If you don’t engage with AI now, you might never catch up. Of course, this means there will be risks. The chatbot company you select might be bought out and shut down tomorrow. You might try something and fail. That’s reality for innovative first-mover technology teams. The good news is that it doesn’t matter if your chatbot of choice gets bought out. What matters is that you have a chatbot. Look at remote smartphone check deposit. This is a great example of how quickly the innovations of today become table stakes tomorrow. The first smartphone deposit app was released in 2009 by USAA. By 2014, people were choosing to not bank at a bank unless they could do remote deposit. One innovative feature proved to be the difference between getting — and not getting — a customer at all! Five years was all it took for an amazing “novelty” to turn into a mandatory feature. You need to be agile enough to release the minimally viable products and adapt as your world changes. Don’t wait for your next innovative idea to become a minimal requirement for doing business. Be the first mover — and release it while it’s a “minimal viable product” instead.

This article was written by Greg Simpson from CIO and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to legal@newscred.com.

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