Throughout human history, productivity has always been associated with the number of people available for a specific activity. Whether we are talking about manufacturing, administrative processes or project execution, the production capacity of a team has always been related to the number of hands available to do the job.
This principle has been successfully applied throughout the ages, whether it was for manual processes in its time or later, during the 20th century, for industrial processes. It is a very useful linear model to forecast both production times and the resources required to carry out a given activity. An increase in resource results within a proportional reduction in execution times.
The validity of this model has endured beyond the industrial revolution, resisting the progressive automation of processes which has modified the relationship between the labour required and the volume of product obtained. This surprising longevity has been possible because all the changes in the production systems of the last century were carried out at a pace which allowed this equation to be adapted to the new scenario. Consequently, any future forecast drawn up on this basis contemplated with acceptable precision the evolution of technology towards a new reality. This is why, even in a period of intense process automation, a linear mental framework for forecasting the future continued to be the norm.
The linear model was functional until a very specific date: on 19 April 1965, Gordon Moore, co-founder of Intel, expressed his particular insight into the evolution of computing. According to Moore, the transistor integration capacity of a microprocessor would double every two years. This insight became the golden rule of the electronics industry to the extent that, even today, sixty years after its formulation, Moore’s law is still valid.
Moore’s exponential evolution was a dramatic paradigm shift with universal implications far beyond the scope of influence of the silicon world. To begin with, this insight invalidated any application of the continuum and linear reasoning which had prevailed to date. From this point on, the future could no longer be treated as a linear extension of the past. Until then, there was no evidence of the point at which a given technology would evolve exponentially, but from Moore onwards, the possibility of disruption should always be on the table. All models of forecasting and planning for the future collapsed. As a result, over the last decades, some planning activities and methodologies which were of undisputed value under the linear management paradigm have shown their shortcomings in a new environment in which more and more sectors, directly or indirectly, are evolving exponentially.
Start to innovate!
Our “Innovation” canvas will help you to question everything you take for granted even though you have no data to back it up. Assumptions and beliefs are, depending on how they are treated, a source of errors and a source of innovation.
The first to suffer the consequences of this paradigm shift in project forecasting or new product development were strategic consultancies with extensive experience in the application of linear models. As an example, the prestigious consulting firm McKinsey&Co advised AT&T during the 1980s not to enter the mobile telephony sector, arguing that it would hardly exceed one million users by the year 2000. The application of linear logic led McKinsey to a forecast error of 99%, when nowadays we know that the mobile business entered the new millennium with more than 100 million units in the market.
Exponential innovation
Raymond Kurzweil, Director of Engineering at Google and founder of Singularity University, has been studying this phenomenon for more than thirty years. From his studies in different sectors, he concludes that this law of exponential evolution can be applied to an increasing range of technologies. Within this range, some novel fields such as artificial intelligence and robotics have evolved, but so have some traditional sectors such as energy.
Today, we already have enough examples to state with certainty that any activity or project based on any of these exponentially evolving technologies will also be subject to this same evolution. In these cases, the main difficulty the project team has to deal with is planning. 1% error in the planning of a process following a linear evolution is equivalent to a deviation in the result of 1%. On the other hand, in a process that follows an exponential evolution, this same planning error can represent a 50% deviation in the expected result. This is why, currently, any attempt to foresee the future is illusory.
Start innovating!
Our “Innovation” canvas will help you to question everything you take for granted even though you have no data to back it up. Assumptions and beliefs are, depending on how they are dealt with, both a source of error and a innovation.
We are immersed in a world in which we have become accustomed to seeing frequently the disappearance of companies which, a priori, seemed to have perfect health. In less than a century, the life expectancy of large corporations has fallen by more than 50 years, from an average of 67 years at the beginning of the 20th century to a mere amount of 18 years today. The vast majority of analysts expect this trend to continue in the future. Some studies even go a step further, claiming that 75% of the organisations currently in the S&P500 will have disappeared in less than a decade.
By contrast, in this same unpredictable world, a small startup like YouTube can evolve in less than 18 months from being financed by its founder’s credit cards to being bought by Google for $1.4 billion.
Faced with this reality, the only alternative is to set up agile working systems, oriented towards monitoring and feedback of results, which allow us to react effectively to both small-scale changes and major technological disruptions.
If you want to incorporate innovation into your daily business, we can help you design the systems you need. We will be happy to guide you through this transformation.
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