4iR and New Economics


Emma Parkin

What should we expect from the economic models born out of the newest industrial revolution?

4iR and New Economics

Every industrial revolution has transformed global economic models. The first industrial revolution changed a local subsistence and craft economy into a factory economy sparking the birth of management science. The second industrial revolution led to the development of mass production and efficient manufacturing systems, spawning Keynesian Economics, which in turn gave rise to big business and management theory. The third industrial revolution arose from the development of semiconductor technologies, leading to the widespread adoption of computers, advanced digital communications, and ultimately the emergence of the internet and associated new business models such as online shopping. The resulting globalisation underpinned the development of vast multinational corporations operating across borders based on software systems rather than physical products.

Logic dictates that The Fourth Industrial Revolution (4iR), characterised by the emergence of intelligent technologies and systems including artificial intelligence, machine learning, data analytics, and robotics, will engender the same market response. With the benefit of hindsight, we should also be in a position to spot where it might lead macroeconomics next and identify how this might affect organisations’ ability to capitalise on that change. But we need to start by understanding why industrial revolution sparks economic transformation in the first place, and why 4iR is likely to follow suit.

During any industrial revolution the rate of technological change is significant, creating new opportunities for global society to develop, grow, and generate value. The conversion of those opportunities into reality requires broader societal change. So the make-up of the workforce, social demographics, the political construct, and everything in between shifts to enable the revolution to achieve its potential. The way macroeconomics develops is a direct result of this process.

In the current period of change it therefore follows that the economic models on which we have come to rely will adjust to remain relevant as 4iR takes hold. That change is already taking place, but are we cognisant of how it is manifesting itself?

To a certain extent we can already begin to see the direction in which macroeconomics is heading in response to these emergent technologies:

  • People and technology - We know that this revolution is all about the changing relationship between people, technology and organisations. Breakthroughs in autonomy, extreme connectivity, analytics, simulation, and rapid feedback loops are all rewriting this triumvirate and driving up the interplay to create a systems approach to most business environments. Current economic models are predominantly linear so they are unprepared for the mesh of interdependences that are starting to characterise this relationship, and which are starting to underpin the way modern commercial relationships work
  • Data-driven thinking – Existing models don’t take into account the sheer volume of data now available to inform decisions and the speed at which it can be accessed. Data not only changes the nature of the way we work but it also gives us a clearer picture of the world around us, increasing the ability of global economic models to accurately reflect the reality of today’s commercial relationships. It also makes a difference to the way we perceive value, wealth and assets. The advent of data as a wealth creator is stimulating fundamental changes to industries and trade.
  • The impact of automation - The emergence of adaptive intelligent systems also throws existing models off course by suggesting a level of automation for many current processes, stimulating a change in the nature of work. There may be a strong bias towards jobs that require creative and relationship skills as routine work is increasingly assigned to intelligent systems, creating fundamental changes to national, regional and indeed personal economics.

To chart the future direction of travel for macroeconomics we also need to look at recent trends in the adoption of new business models. Whilst global economic paradigms change slowly, business models develop rapidly in response to market change so companies can take advantage of emerging opportunities as they arise. Today’s business models have developed radically as a result of the advent of 4iR technology and organisations that have quickly embraced these changes are already reaping the rewards. The major adjustment that indicates a direction of travel is the changing definition of value and the reduced perception of physical assets in particular as a key criterion for business growth. In the past, for a business to sell a product it needed a factory, a distribution centre, and a significant workforce. Today it’s possible to reach a global market without any of these. Whatsapp has 55 employees serving 420 million customers. AirBnB is the world’s largest accommodation provider without owning a single piece of property. What makes this possible is the increasing value of data and the improved performance of analytics – two of the 4iR’s core foundations.

It’s not just our ability to capture a greater volume of data, but our ability to crunch it quicker, and to draw more accurate intelligence from the integration of disparate data sources more effectively. The capture of data has been possible for some time but advances in processing speed mean we now can do it far faster than ever before, and from a far wider range of sources, delivering more powerful data-driven intelligence. That intelligence allows organisations to personalise their products, services, and marketing to reach their target audiences in a way that maximises commercial impact. Data itself has now moved from being a commodity to a prized asset. When Caesars Entertainment Operating Co. filed for bankruptcy in 2015, the single largest asset it owned was its loyalty programme, with data on 45 million customers, valued at $1bn. Uber has now announced it will start to charge what it thinks you will pay and not a flat rate, using its data bank and charging customers more if they are leaving from a more affluent area. Data has become so crucial for underpinning our concept of value that The Economist recently hailed it as the “oil of the 20th Century”. But if data is greasing the moving parts, the engine itself is experiencing a bigger change, morphing into what we can now call the ’subscription economy’, a concept identified as an underpinning principle of the next phase of global economics models.

The subscription economy sees a business’s focus shift from the product to the customer themselves through the application of intelligence from data. As a result, organisations can target their efforts towards nurturing relationships and bank on a long tail of committed revenue. More businesses are now exploiting the concept of the subscription economy rather than more traditional consumer economic models, using data and analytics to build relationships that appeal to a customer base with far more choice than ever before. As products and services become similar and in some cases ubiquitous, the presence of a true USP is starting to fade in many markets, so personalisation is fast becoming the key differentiator. 78% of adults subscribe to a service today and a quarter of believe they will subscribe to more in the future. What makes this so appealing to both user and provider is that subscription services represent cyclical value chains. They not only use data to enable a better quality of engagement/product/service but they also generate a rich stream of useful data themselves, allowing businesses to add yet more relevance to their customer relationships. Subscription models provide businesses with steady and consistent revenue streams whilst customers have a more enhanced experience.

This new economic shift is being driven, in part, by the advent of the 4iR – from machine learning and artificial intelligence, to analytics, cryptocurrencies, and cyber security developments such as blockchain, these technologies are swiftly changing the way businesses think about how they grow. We are still some way from understanding the characteristics of the next phase of global economic models but we can certainly draw some initial conclusions from what we are seeing today:

  1. Data and analytics will drive the direction of individual business models. The capability to make the most of data analytics will be the key to growth and success as the 4iR takes hold.
  2. The definition of assets will change and be reflected in the way we attribute value to organisations, individuals, and sectors.
  3. Macroeconomic models will shift away from a prioritisation of traditional manufacturing, and towards a focus on the productivity of relationships, measured by depth of engagement and recurring revenue stream.

We know that current models aren’t fit for purpose because they were developed when resources were unencumbered, access to high-performing intelligent technology was limited, and globalisation was in its infancy, all of which are no longer the case. For organisations to take advantage of emerging models they need to act now and begin the process of adapting to the changes around them. Some are primed for the shift. Others haven’t worked out they need to adjust at all. What is clear is that they need to understand the spread of 4iR technologies and how the individual characteristics of each technology interplay to improve opportunities for operational advantage across the board. At the very least, they need to place data capture, analytics and implementation against deep data-driven intelligence at the heart of what they do. If they can do this, they are likely to align well with the natural swing of global economics that has already begun.