Which Strategic Technology Trends Will Be More Than Just Trends in 2017?

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A few months ago, Kasey Panetta of Gartner published a nice article entitled Gartner’s Top 10 Strategic Technology Trends for 2017 in which she discussed technology trends that will have a strategic impact on companies in 2017.

With this article in mind, I recently led a one-on-one chat with Scott Manuel, Vice President, Head of Product Management at Thomson Reuters, during our Thought Ensemble company trip in Phoenix. We used the topics raised by Gartner as a framework for our discussion: intelligent systems, digital technology, and mesh computing. The discussion itself was well received, and I’d like to (again!) thank Scott for his excellent contributions, as well as for taking the time to sit down with us.

Since our chat, I’ve had a little more time to ruminate and wanted to contribute some of my own thoughts by creating two buckets from the Gartner predictions: (1) those technologies that every business is going to be talking about in 2017 and (2) those technologies that companies will actually experiment with and try to use strategically in 2017.

Bucket 1 (things we will only talk about in 2017):

  • Intelligent things (robots, drones, and vehicles) – yes, we will all drive (or ride in) cars that have some intelligence in them, and some companies are already using intelligent robots for manufacturing. But, for the clear majority of technologists looking to innovate, they will see little or no actual use for intelligent things in the near term to help their business, as physical machines are not appropriate for many industries and there is still far more research needed around safety, liability, etc.
  • Virtual reality and augment reality – again, with exceptions, this is still an area of high R&D with few practical uses for most businesses. While products like Microsoft HoloLens show promise, even those are only available for developers at $5,000 each. The costs will need to come down, and the business applicability will need to come up. And, of course, we will need to address cultural norms around wearables.
  • Digital twins – the idea of using computer simulations for real-world phenomenon has been around for a very long time, but this is also an area of major R&D and would require huge investments to make real for most companies in 2017.
  • Blockchain – I know people will make you sound foolish if you admit that you don’t really know what it is or why it is so important, but the reality is that blockchain is an extremely niche concept that has limited practical use for now. This area reminds me a lot of “mashups” that were all the rage in 2006. Yes, every web-site now integrates Google maps into its interface, but the other use cases didn’t materialize. I suspect blockchain will be similar.

Bucket 2 (things that will start to become reality in 2017):

  • Conversational systems – Natural Language Processing (NLP) is going to start to come at us quickly, and I can’t wait. While this may only be an area of R&D investment for many companies, starting to find ways to use NLP for searching of corporate information, and investing in intelligent IVR systems should absolutely be on your near-term strategic list.
  • Machine learning – this is an area that has been ripe for many organizations for a long time, and the availability of systems from Google, IBM, and other companies makes this extremely easy for R&D work. The costs are low. The opportunity is big. Why aren’t you already figuring out how to use this?
  • AI strategies – I agree with the sentiment that says if you don’t have an artificial intelligence strategy, you need one. How is AI going to help you better understand your business? How is AI going to help reduce costs for commodity tasks? How is AI going to help improve your customer service? If you can’t answer those questions, 2017 is the year when you should start to get answers.

I’m excited about this year because it has been 20 years since I received my master’s degree in artificial intelligence. Back then, we were hoping for a time when it would be possible to exploit the research we were doing. Now is that time.

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