Gartner has a tool known as the \u201chype cycle.\u201d It shows where a technology stands on a curve that represents the lifecycle of that technology \u2014 including the peak of its \u201chype,\u201d or inflated expectations, followed by the low point of missed expectations, or the \u201ctrough of disillusionment.\u201d The lifecycle finally ends as the technology achieves what they refer to as the \u201cplateau of productivity.\u201d\u00a0\nAI 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\u2019m sometimes asked is, \u201cWhy now?\u201d Aren\u2019t we headed toward that \u201ctrough of disillusionment\u201d where we realize that Siri won\u2019t cook dinner and Alexa won\u2019t take out the trash next week like we\u2019d hoped?\u00a0\nTen 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.\u00a0\nThis is where things have changed. The rate of technological change is rapidly accelerating. This is sometimes demonstrated by the speed of technology\u2019s adoption. For example, the landline telephone \u2014 a phenomenal invention \u2014 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: \u201cFirst we build the tools, then they build us.\u201d 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.)\u00a0\nThere are many different views on when Skynet (the main antagonist of the \u201cTerminator\u201d 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\u2019ve already written a blog about why AI is back; this blog is more about how quickly it\u2019s moving.\u00a0\nIf you don\u2019t 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\u2019s reality for innovative first-mover technology teams. The good news is that it doesn\u2019t 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 \u2014 and not getting \u2014 a customer at all! Five years was all it took for an amazing \u201cnovelty\u201d 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\u2019t wait for your next innovative idea to become a minimal requirement for doing business. Be the first mover \u2014 and release it while it\u2019s a \u201cminimal viable product\u201d instead.