Six Points On The Map Of Emerging Tech

Triangulating on a model for Emerging Technologies research

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Not too long ago, I wrote a post here called The Map Is Not The Territory, But Is Useful Anyway, in which I laid out the rationale for creating a model — a map — of the rapidly changing world of emerging technologies. Now, we are beginning to sketch the outlines of that map.

As a quick recap, from my first post:

I’ve been embroiled in discussions with the folks at Traction Technology Partners about how to triangulate on all the swirling and chaotic goings-on in emerging technologies. My plan is to look at how others are trying to model the world of emerging technologies and their current and foreseeable impacts, as well as asking our readers for their perceptions about these technologies and models, too.

Importance and Rate of Adoption

So, we asked members of the Traction Network (mostly CIOs, CTOs, and senior IT execs in the enterprise) to answer a few questions about a collection of technologies we think are worth tracking. In particular, we asked how important various technologies might be, ranging from very to not at all. Note that we didn’t define these terms, relying on the individual’s personal understanding of what the terms mean. And here’s what dropped out of that, ordered by very to not at all:

chart 1 — emerging technologies, ranked by importance

Note the steep drop off between The Internet of Things bar and the bottom of the list. It’s a natural cleavage point, and so let’s zoom in on the top of the chart for the purpose of this post, since those at the bottom failed to garner more than 50% ‘very important’ in the survey¹.

chart 2 — The 6 most important emerging technologies in the enterprise

Defensive and Offensive Technologies

Perhaps it’s no surprise that cybersecurity — a primarily defensive technology near and dear to the hearts of IT-focused people — scored the highest on ‘very important’. Especially in the wake of the Wannacry mess.

As I said, I consider Cybersecurity defensive: if there weren’t malefactors out there trying to steal secrets, subvert devices for their own ends, or attempting to extort by taking over servers, files, or other computing resources, well, we wouldn’t spend a nickel on it. It’s purely a cost, like insurance, and it’s not an investment in the company’s competitive orientation. While cybersecurity can be innovative, it’s not about increasing the company’s level or rate of innovation. So it’s the offensive technologies that follow cybersecurity that are more interesting.

The next five are AI and Machine Learning, Cloud Computing, Data Analytics, Big Data, and Internet of Things, which are all areas of enormous investment and perhaps even greater promise².

The second question was about the rate of adoption of these technologies, ranging from zero (for no adoption) to 10 (for being the fastest adoption possible). Here’s what we found, for the top emerging technologies:

chart 3 — adoption rate

Note that the six technologies deemed most important are also those with the fastest adoption rates, but interestingly the adoption rates are in a different order. AI, for example is deemed second “most important,” but only fifth in the rate of adoption.

If we plot the two dimensions against each other — Rate of Adoption (as shown in chart 3, above) versus Importance (the value is the percentage that say the technology is ‘very important’)— here’s what we see:

chart 4 — putting the factors together

Tentative Conclusions

We are just beginning to noodle about creating a map of emerging technologies that we can use more generally. One of our goals is:

To create a relatively small, 2-D map that a reader can grasp in 15 minutes or so, and which accomplishes the paradoxical and essential job of pulling all the emerging technologies into one map while dividing them into different and more-or-less well-defined neighborhoods.

We’ve started to organize the list of emerging technologies in various ways: defensive versus offensive technologies, the “very” versus “less important” technologies, and “quick” versus “slow” to be adopted technologies. But we haven’t yet defined the “neighborhoods,” or their relationship to one another.

I have to confess, I disagree with the crowd in some significant areas. For example, I believe that augmented reality will be very important in business, and elsewhere. Perhaps that means we should separate AR and VR (just as we might want to combine big data and data analytics). But we’ll have to work out those differences in the future.

Also, my sense is that we will have to explore the degree to which a technology is a foundational, deep platform for other technologies to build on or incorporate, as well as showing up in direct interactions with users. So, for example, perhaps AI is judged as being more important than others because of its perception as a platform as well as directly experienced by end users: AI will underlie advances in other technology areas, such as Big Data, Robotics, Drones, and Chatbots, while the opposite may not be the case.

Consider that AI is reaching down deep into the computing stack: Apple, Google, and Nvidia are making chips that will allow for much faster AI-intensive computing, both in the cloud and on end-point devices, like PCs and mobile. The same can’t be said for Chatbots or 3-D printing.

I hope to dig into these issues in later installments in this series.

And for those of you who haven’t yet participated in the survey, you can still do so here.

A Few Related Links:

AI and Machine Learning — Recent news stories about Google’s AlphaGo beating the world champion Go master might have played a role, but the torrent of news of the form ‘<some industry> + AI’ is probably the reason that AI/ML rates so highly.

My bet is that the real tipping point will be the emergence of driverless transport in major cities, at which point perceptions of the rate of adoption will swing above 9 in the chart above. Likewise, as speech-driven AI interfaces (Alexa, Siri, etc.) become more powerful and more prevalent expectations of faster adoption will likely rise.

Cloud Computing — Mary Meeker’s 2017 State of the Internet report revealed some fascinating data about cloud computing adoption, which now accounts for 37% of IT infrastructure spending:

Natalie Gagliordi | Mary Meeker’s 2017 internet trends report: 5 takeaways
Businesses are spending almost as much on private and public clouds as they are on traditional data centers. Spending on public and private clouds last year rose to $36 billion, up 37 percent since 2014. That accounts for 37 percent of IT infrastructure spending last year, compared to 63 percent spent on traditional data centers. In terms of cloud concerns, the report notes a shift in focus from data security and cost uncertainty to vendor lock-in and compliance.

Internet of Things — Doing some research for a client, I came across a piece by security maven Bruce Schneier, called We Need to Save the Internet from the Internet of Things, in which he makes the case for a more resilient internet to deal with the mess also known as IoT, following the Krebs attack on CCTV cameras running Linux. He writes,

What was new about the Krebs attack was both the massive scale and the particular devices the attackers recruited. Instead of using traditional computers for their botnet, they used CCTV cameras, digital video recorders, home routers, and other embedded computers attached to the internet as part of the Internet of Things.
Much has been written about how the IoT is wildly insecure. In fact, the software used to attack Krebs was simple and amateurish. What this attack demonstrates is that the economics of the IoT mean that it will remain insecure unless government steps in to fix the problem. This is a market failure that can’t get fixed on its own.
The IoT will remain insecure unless government steps in and fixes the problem.

He points out that a number of majors in the computer and phone worlds employ large staffs dedicated to countering security problems, which is why they are as secure as they are. This level of investment is not the case in the sprawling frontiers of IoT, which is why government regulation is needed.

But we are in a difficult time for such regulation to arise, alas. So IoT will continue as a wild frontier for the foreseeable future.


  1. I’m especially surprised by Augmented Reality/Virtual Reality scoring below 25% ‘very important’, but I guess it’s too early to even see the promise of AR.
  2. I am leaning toward consolidating Big Data and Data Analytics, going forward. Note that doing so would not have changed relative positioning in the Importance chart, or, I believe, in the Adoption chart.