Our 12 month innovation and disruption scorecard

Influential technology trends

It’s coming towards the end of the year and it is time to reexamine many of the assumptions about emerging or disruptive technologies we made twelve months ago.

The areas of interest we highlighted back then:

  • Mobile technologies
  • Cloud services
  • Artificial Intelligence/Machine Learning
  • Internet Of Things
  • Autonomous vehicles
  • Blockchain
  • Virtual Reality

Of these, the first two, mobile devices and Cloud services, are now so mainstream that they are not even considered to be emerging, they just ‘are’.  Of the others, a couple of innovations, autonomous vehicles and Blockchain, are now part of common discourse and commentary, while the rest still languish at the margins of mainstream adoption.

So why are some developments more successful than others? I suspect it’s their relevance to regular day-to-day activity and their potential to either help people save time or money. This is nothing new, as innovations through history were usually driven by the same, or similar, objectives. But it’s not just this, as almost all of the innovations mentioned above could match the same qualifications. I suspect it also depends on a convergence of other circumstances that enhance the proposition into something really compelling. This convergence of interest may also include the emergence of an appropriate environment that either enhances the attraction of the proposition or provides a fertile landscape where it can flourish.

Take the example of autonomous vehicles. The technology already exists and manufacturers such as Tesla have introduced it into mainstream vehicle production. Legislative authorities are starting to appreciate the virtues of driverless vehicles in some circumstances, in that the technology can potentially reduce accidents and ease congestion. Finally, the slow evolution away from fossil fuel based drivetrains and towards electric powertrains requires advanced electronic platforms to operate. The combination of electric motors and intelligent systems to ensure efficient operation are essential ingredients enabling the car to operate autonomously.

Every mainstream vehicle manufacturer is now testing autonomous operation, but they are only doing so because they know that it will soon be generally accepted by society. They also recognise that they have been proven to work in a variety of (but not all) circumstances, and manufacturers need to have an alternative when oil burning combustion is no longer allowed. But the present limitations do still require some technologies to evolve further. For example, GPS needs to become much more accurate, down to inches and centimetres from the present feet or metres that the present generation of commercial satellites allow. However, it is fair to say that this is one development that is fulfilling it’s initial promise and will continue to develop.

The adoption of the technology behind Blockchain is also accelerating out of the pure technology realm. Blockchain is essentially an independent digital ledger that we described in a recent TI brief.

A number of startups are now exploring ways in which the Blockchain can be applied to commercial problems across a number of industry sectors – particularly supply chain management. Until recently, most people associated Blockchain technology with the digital Cryptocurrency Bitcoin. But as the technology defining Blockchain has become more understood, people have realised its power and utility. Hence, we now see numerous problems being examined with a view to a solution being delivered by blockchain-based platforms.

A critical and essential byproduct of both of these developments is that as their adoption increases, they generate more and more data. Data scientists and analysts can exploit this to gain a greater understanding of how the solutions can be improved. Unlike many earlier innovations, anything involving technology that is ‘connected’, can provide instant information flows about its condition, the environment in which it operates and most importantly, what was going on when it either failed or encountered something unexpected. By allowing these huge data sets to be examined by a combination of human analysts and focussed machine learning algorithms, rapid evolution can take place.

It would seem amazing to most people that just a decade ago the vast amounts of computing resource required to do this could only be found inside giant corporations or the government agencies of major economies. Today, such has been the evolution of technology, the required analysis can be done through most mobile phones or tablets exploiting the virtually unlimited processing power of the cloud services available from Amazon, Google or Microsoft.

Disruptive startups no longer need scale to enter markets such as these. Just as Apple or Dyson can decide to enter the automotive market, so can almost any other group of talented engineers. This is healthy and provides immense opportunities for innovative thinkers and those bold enough to try something new.

The other technologies we picked as having the potential to disrupt, may also break out into the mainstream in the months ahead. If they don’t, it may be because they do not enjoy the convergence of interest and circumstance that autonomous vehicles and Blockchain solution are exploiting. It may also be that they need more time or that their time will never come to be adopted by the mainstream and they will be bypassed by the next new thing.

Source: Ti

Author: Ken Lyon

GSCi

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