Don’t believe the hype (cycle) – part 1

 

The 2017 Gartner hype cycle is out and, while it should always be taken with a pinch of salt, it’s fun to decide for yourself whether or not you agree with their assessment. Before we get stuck in, it’s worth having a quick read of this interesting analysis of past hype cycles where the author assessed how accurate these predictions had been previously (spoiler: not very). But still; I think we can all agree that technology is moving at an ever increasing pace, which not only makes predictions harder to make with certainty, but also presents increasing opportunities to outflank competitors – or be outflanked.

So with that in mind, let’s take a look at this year’s hype cycle:

Emerging-Technology-Hype-Cycle-for-2017_Infographic_R6A-1024x866.jpg

Interestingly, for the first time Gartner has decided to sub-categorise most of these technologies into three buckets; AI everywhere, Transparently Immersive Experiences, and Digital Platforms. We’re going to tackle each of these over a series of three blog posts, starting today with AI everywhere.

The hype – or, dare we say, hysteria – behind Artificial Intelligence reminds us of the Big Data excitement about a decade ago. Everyone’s talking about it, very few can show results, it’s rarely well defined, and it’s being heralded as the answer to whatever problem you care to mention. This enthusiasm contrasts with a sense of the shine coming off, such as the AI poster child IBM Watson being uncovered as overhyped, and the lack of self-driving cars on the road several years since they were first announced with excitement. Such activity is highly indicative of the trough of disillusionment.

Maybe it’s because we’re early adopters by nature, but our feeling is that quite a few of the AI technologies are even further along the hype cycle than the trough of disillusionment. When we look at virtual assistants, the connected home and machine learning, we can see a positive pattern of widespread experimentation and learning, with the occasional commercial success and the broader pragmatism you’d expect to see on the slope of enlightenment. In any event, take note of the key used in the diagram: “Plateau will be reached in:”. What represents a plateau when it comes to e.g. machine learning? We expect it will fragment significantly for many years to come. Some basic aspects will become mainstream soon, while others will continue to build on these breakthroughs over the next few decades. In any event we feel we’re entitled to a more optimistic outlook than the graph indicates.

The aforementioned analysis of past hype cycles refers to the Gartner hype cycle as “mostly a reflection of industry consensus”, which is fair, but which also masks wildly varying opinion. The typical Creative Director might be quite comfortable inflating expectations around AI as part of a creative idea, but they’re not always around when such an idea needs to be built. In contrast, practitioners such as those at Bilue are already on the slope of enlightenment due to their first hand experience and their ability to better interpret the various success stories, such as with AlphaGo. We mix proven technology (especially e.g. Tensorflow and CoreML) with strategic intent to find utility with a decent prospect of success, rather than encouraging non-technical creatives or business leaders to go wild with ideas that may not be viable.

Our recommendation would be for companies to invest in multiple, small experiments to figure out which of these technologies can generate a return. Typically these might be in a narrow context, such as reducing call centre costs by a modest margin, or helping position your brand as being more innovative, but putting a strategic lens over the opportunity space can improve your strike rate return a stronger ROI.

Phil wrote an introduction to Artificial Intelligence a little over a year ago. The closing paragraph still stands up:

“In the short term…we can start looking for opportunities to exploit AI technologies as they mature and generate new forms of value. It’s important to get an early, solid understanding of how the opportunity can be exploited and, as with the technology waves that came before, a few well-chosen bets may pay off handsomely. She who dares wins, but let the buyer beware.”

Next up: Part 2 – Transparently Immersive Experiences

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