Change, Hyper-turbulence and technology

Change, Hyper-turbulence and technology

by The Future Of - Foresignt Accessible

Zygmunt Bauman’s idea of fluid modernity is a good metaphor for the type of change we are experiencing: while the components of our reality flow on each other, the landscape is less stable and predictable, and in certain cases, it can be even chaotic and turbulent. This change is first and foremost social, as we live in a society where more and more dichotomies are emerging: between the digital natives and the digital migrants, between the ageing populations in developed economies, and the younger and younger African ones; between the wealthy and the poor. But this change is also economic: the powerhouses of the past are losing ground to emerging new companies, and new industries. General Electric was dropped from the Dow Jones index, after being listed on it for more than a century, while at the same time, two tech companies have soared for the first time above the 1 trillion USD market capitalization: Apple and Amazon.

As one could expect, a big component of this change is due to the digitalization of our lives, with technology becoming more and more embedded in every aspect of our work and leisure. Nevertheless what drives the hyper-turbulence in the new landscape, is not just technology, it’s the rate of adoption of it: traditionally technology adoption was characterized by an S-shaped curve, known as the learning curve, featuring a slow start, a tipping point of exponential growth, and finally an inflexion point where growth becomes very slow.

Speed of Tech Adoption


The Tech Adoption curve is changing shape: in graphical terms is shifting to the left (which increases the speed of adoption) and in a form where there seems not to be any tipping point nor inflexions.

In these chaotic environments, driven by the combination of social, economic and technological change, we have observed the emergence of design thinking, as a discipline to cope with those trends. On one end, its empathic design approach ensures that companies keep an eye on their customers, guaranteeing more relevant products, services and business models. In addition to that, it combines an emergent approach to strategy, with a creative logic, that allows for more experimentation, in lieu of the traditional “strategy-as-a-chess-game” model of “analysis before the action”. Design Thinking is still dealing with wicked problems, but elevates the customer at the centre, rather than the product; it leverages foresight instead of long-term forecasting; it disconnects consumer insights from categories boundaries, by allowing shifts from “consumer needing a car” to “consumers needing a mobility solution”; from “consumers needing a hotel” to “consumers needing a bed away from home”.


But which technologies?


When we hear in the media about new technologies, we often hear it in connection with the fourth wave of industrial revolution. This is the stage of up-and-coming cyber-physical systems, which are autonomous: in other words, we are referring to robots, which have swarm-like behaviour, complementing (not necessarily substituting) the work of humans. These robots - for operational purposes - rely on end-to-end communications between previously disconnected entities: we often talk of M2H – machine to human - and M2M – machine to machine – protocols, which allow integration in both vertical and horizontal sense, between, and beyond, the traditional boundaries of a firm.  These robots also rely on sensors and smart devices – also knows as Internet of Things – which provide relevant information to all players involved: when a truck leaves the premises of the supplier’s warehouse, en route to its customer, generates tons of data, useful to multiple players: it can be used by toll companies – in aggregate form with data from all other trucks and cars – to adjust dynamically the pricing of motorways based on congestion. Insurance companies can use it, for theft protection, roadside assistance and insurance policies based on driver’s behaviour. The fleet owners can leverage those data to control costs and route optimization. The receiving customer can utilize the expected arrival times for the dynamic planning of the downstream supply chain. And finally, the first-aid and emergencies healthcare operators can use it in case of accidents or health problems.

Moreover, in the next 2-to-5 years, technologies like Augmented Reality and Additive Manufacturing are expected to cross the chasm, by becoming mainstream. The first has the benefit of being more inclusive, by even bringing back into the blue-collar jobs pool, many people whose tech competencies were not sufficient with the increasing digitalization of work. The second one, which is often referred to as 3D printing – has the benefit of challenging the traditional model of economies of scale, giving back a landscape of opportunities to small shops and digitally-savvy craftsmen. 

But beyond AR and 3D printing, the combo of big data and Artificial intelligence is the one, which has the biggest potential. First and foremost, we talk about big data, to refer to large – often unstructured - and complex datasets, whose dimension and logic make obsolete the traditional database management approach. The big data challenge is not in the management of the data though, is more on understanding those data, and making good use of them: this, we refer to, like analytics.  On the other end, Artificial Intelligence is the key tool to analyze the large datasets and provide input to/ guide machines like flying drones and autonomous cars. AI is a set of algorithms mimicking human intelligence, to compute large datasets: with AI, of notable importance is Machine Learning, which uses statistical techniques to ensure machines improve at tasks with experience. A subset of Machine Learning is Deep Learning, in which the software trains itself by exposing multi-layer neural networks to big data. So in this sense, IBM Deep Blue Chess Program, who defeated then chess champion Kasparov, is a traditional AI algorithm, whereas the Google Search Engine or our email SPAM filter, are machine learning programs. Finally, Natural Language Understanding and Processing, visual recognition and autonomous driving and flying machines are examples of deep learning algorithms.


In conclusion


In the context where reality is fluid, landscapes are chaotic - if not hyper-turbulent -  and technology becomes more and more pervasive, two interconnected paradigm shifts are occurring: agility is emerging as a model to make the most of the current modernity, especially where long-term planning is becoming obsolete. In addition to that, more and more non-adjacent industries are converging together: while the smartification of watches is bringing closer together watch manufacturers – even the luxury ones – and consumer electronics, the emergence of wearables is at the intersection of fashion, consumer electronics, healthcare and insurance: in a near future your t-shirt could be communicating to your car that you are having a heart attack, so that the car can take control of driving and park in a safe place, while calling for an ambulance at the closest available hospital. In the credit card world, social media and online shopping are becoming training grounds for Machine Learning algorithms making decisions on users creditworthiness, by bringing together the financial industry and the likes of Facebook and Twitter. 

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