Back to the future – What we can learn from predictions that didn't come true.

The British economist John Maynard Keynes once declared the only reliable long-term prediction is that “in the long run, we are all dead.” Casting a critical eye over quotes and predictions from well-known industry specialists and experts, he seems to be right. Even mid-term predictions in the world of IT can never be really certain.

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E-mail is a totally unsaleable product.

 Ian Sharp, Sharp Associates, 1979


I think there is a world market for maybe five computers.

Thomas Watson, CEO IBM, 1943


640K ought to be enough for anybody.

Attributed to Bill Gates, 1981


It would appear that we have reached the limits of what it is possible to achieve with computer technology.

Jan von Neumann, Kybernetiker, 1949


There is no reason anyone would want a computer in their home.

Ken Olson, President of Digital Equipment Corp., 1979


DOS will be with us forever. We’ve learned how passionate people are about DOS.

Brad Silverberg, Microsoft Manager, 1991


In 1958, Disney released a short film called “Magic Highway USA”, showing a fantastical vision of the future with the American highway system as the main method of transport. In this highly entertaining film we see self-driving cars, nuclear-powered tunnel drilling machines, weather-influencing systems, highly-automated multi-storey car parks and much more—some of which have become more or less reality, some not. Today, the film is a central element of the paleo-futurism research discipline which examines past predictions of the future. Essentially, the key question is: what can we learn from previous predictions and their (lack of) accuracy to improve the quality of today’s forecasts?oking for new planets to conquer.

Paleo-futurism offers three key insights.


Technological advancements are usually overvalued, yet social developments underestimated. In the Disney film, we see the way that cars drive, are steered and are maintained plus the structure of transport routes and even urban development transformed. But the family structure stays the same: the man is the sole earner while the women looks after the children. She goes shopping, he goes to work. Environmental pollution, social problems? Not a mention. No consideration is made for how the structure and values of society inevitably change over decades—technology evolves and becomes more modern, whereas the people remain the same. We know, of course, that this isn't the case, but it is clearly difficult to predict these changes, or include them in technology forecasts. But it’s exactly this that has the most significance, because in the mid-term especially, it’s sociological perspectives that form the foundation for accurate predictions of future needs—and therefore markets and opportunities.



The location determines the angle. Predictions should always be viewed from the perspective of the time they were made in. Not just from a technological perspective, but in terms of attitudes.

The period of the economic miracle was characterised by the belief that anything was possible, the catastrophic war with all of its terrible consequences was over, people began to become more affluent, new technologies like space travel, nuclear power, and even computers were developed. One thing seemed clear: in the future, things could only get better, technology would know no bounds, and tomorrow would be pleasant and peaceful. This can be seen in the science fiction films from this period. Referred to as “white sci-fi”, films such as 2001: A Space Odyssey and Star Trek show that the world has been freed from hunger, poverty, and war and how the happy people are now looking for new planets to conquer.



Franz Kühmayer is a trend researcher and strategy consultant. He works at the Zukunftsinstitut as a business management consultant, lectures at several universities, and is a regularly published author.

The oil shock and economic crisis of the 1970s, however, put a dampener on this euphoric spirit and suddenly the forecast didn’t look so rosy any more. On the contrary, the world would perish from overpopulation or the dying out of the forests. The boundaries of growth had nearly been reached, the Club of Rome prophesied. Science fiction became sombre, dystopian films like Blade Runner showed a future of overwhelming crime and environmental destruction.


And today? Current predictions are being made against a backdrop of scepticism and wariness in a crisis-ridden Europe that looks fearfully to emergent nations like China and is no longer so sure of its own future. The conclusion: to be able to make long-term predictions, we have to free ourselves from as many of the perception filters of the present day while understanding the long-term development process and observing those that differ from the virtually seasonal ups and downs of the market.



Success is a poor teacher. False prognoses from those supposedly in the know have shown us time and again how, off the back of years of success, it is hard to imagine alternative market developments or radically different customer expectations. The belief of still being on the road to success has become so entrenched—especially within companies. Successful companies are less likely to disrupt the market with breakthrough innovation, as they are too focused on gradual optimisation. The prevalent belief in these organisations is that, while there is always room for improvement, their product is here to stay. With this approach and perspective, linear trajectories can be well managed, as opposed to a product or market segment suddenly breaking away on its own. On the contrary, even in the face of change, they continue to invest in what already exists, until the bitter end. The IT industry is full of examples of this type of escalating commitment in failing strategies. “Having lost sight of our goals, we redouble our efforts.” This was the mindset that led to seemingly indestructible companies such as DEC, Wang, Kodak, Commodore, and many others being resigned to the history books in their industries. New companies are now at risk of falling into the same trap—just the other way around. While established companies tend to overestimate the sustainability of existing system technologies, newcomers tend to have too optimistic a view of their new technologies. Blinded by shining examples of radical innovation, sluggish markets, customer familiarisation effects, and also network effects are not examined critically enough. The result being outstanding niche products, that remain just that: niche products.


Eyes on the future.

Given these perspectives, we can come to the conclusion that the future cannot be predicted and that any undertaking of this type is bound to fail and is better left alone. But in the face of startlingly awful forecasts, we continue to do it every day—try to predict the future. Without imagining how tomorrow will be, it’s impossible to act today. Our decisions, both on a personal level or in enterprise terms, are based on a conscious or subconscious image of the future. Even spontaneous and unconscious actions are embedded in a network of assumptions of continuity or change. We don’t even set a foot out of our homes without having some idea of what’s going to happen next. We start families, begin courses of study, take out a loan etc. We live with certain continual assumptions of the future that influence our actions. In other words each one of us is constantly predicting the future—whether we are aware of it or not. And management, who—as the name suggests—manage everything, need to make assumptions about the future in order to determine how to act. Because in the end, this is what legitimises their role as managers. The key here is that we can never predict the whole future with confidence, and we can never foresee every detail. We have to get used to mistakes—if we assume that it’s not all one big plan designed by fate or a god. As soon as complex systems such as economies, markets, or social systems come into the picture, their future  does not develop deterministically. Markets can blossom or collapse, societies can develop or break down, economies can stagnate or establish new value creation structures. All of this happens under specific conditions and in specific contexts that are governed not only by chance. We can make statements about the developments that will underpin the future. A basic source of errors in forecasts lies in the false assumption that we can create trends. Of course, with considerable marketing efforts, it is possible to push certain products, but as a rule this only leads to short term success. Actual socio-cultural changes can’t be manufactured; they arise from the depths of the social transformation and have deep roots and causes. While creating trends will remain a pipe dream for many companies, what is attainable is recognising and interpreting trends.


The question is therefore not whether the future can be precisely predicted, but how can we accurately observe, explain, and understand the preconditions that give rise to certain future possibilities in an economically justifiable way, that lead to meaningful conclusions being made at enterprise level.



Megatrends – Avalanches in slow motion.

 The answer to this lies in fundamentally recognising that the world is dynamic. Its basic structures are constantly changing, giving way to new ones all the time. These changes can be superficial phenomena such as new products, or deep, sustainable shifts in, for example, demographic developments. If we choose the right perspective, the structures of the transformation are recognisable and at least partially understandable. The direction and speed of changes, their complexity and failure propensity to external influences can be modelled and predicted within certain bandwidths.


A valuable basis for this are what are known as megatrends. Their starting point is long-term changes in the economy and society as the foundation for a deeply rooted understanding of change.

Researching megatrends is essentially about observing the transformation of living environments—a culture science in the empirical sense. In its deepest form it is influenced by numerous scientific branches, including evolutionary biology, behavioural economics, and cultural anthropology. They recognise a transformation as a very slowly progressing change in systems, settings, and values. Under the megatrend of individualisation, we can observe the creeping dissolution of hierarchies and trust in authority giving way to a more pronounced egocentric storyline. The effects can be felt on several levels—in a political context in the loss of faith in superior institutions, in a societal context with lives that deviate ever more from the "norm", in a company context with the increasing expectations of individual employees to be granted corresponding levels of freedom.


This development has been visible and recognisable for decades and its course is therefore easier to predict accurately. Other, equally stable megatrends include Silver Society—the demographic development towards a population that is on average older, but that appears to remain young, and Female Shift—the changing role of women in society, visible in alternative family models, multi-layered corporate cultures and academic achievement. The rationale here is that change happens at different paces—in some areas quickly, radically, in several directions, and in others more slowly, sustainably, and with a clear direction. Product trends have a character that’s virtually impossible to predict, existing from season to season, either in reality or as a marketing statement. Making prognoses on this level is therefore hardly productive and has a high error rate. Megatrends, on the other hand, have an epochal character, changing not only specific segments for the long term, but also many aspect of social life and the economy. These are not just superficial movements—the wave breaking—but deep currents in the ocean. Carefully analysing the data available actually makes predicting megatrends relatively easy. Even more so, since integrated prognostics are getting better and better at modelling the complexity of highly dynamic systems as an interdisciplinary approach and processing them with analytical methods. Today, this has been made possible by using massive data systems such as Big Data in addition to new, computer-aided simulation techniques. Enhanced and augmented by verbal argumentative processes, predictions can be accurately made for a period of five to ten years.


In essence, it’s less about predicting individual events or end results than about understanding the underlying systems and behaviour. The future cannot be precisely predicted, but by addressing it, the scope for shaping it rises. Considering how tomorrow could be helps us understand today better. We can make decisions and act with more certainty—we learn how to deal with obstacles. The challenge of using megatrend perspectives however, is not only in recognising the diversity and intertwining of systems, but also using them to form our own thoughts. The complexity researchers Ross Ashby and John Casti have shown that the control centre of a complex system has to be as complex as the system itself in order for it to endure. “When a system gets more complex, the mental diversity in its centre also needs to increase,” is the plea to colourful thinking and diverse management.

A little flopology.

When reflecting on which visions of the future tend to fail, there are several different categories of mistakes. Rare, making them all the more serious, are “megaflops”—based on fundamentally incorrect thought or data models with the result that the entire technology is useless and redundant. One example is underwater cities, which drastically underestimated future environmental conditions and population growth.


More interesting, from an economic perspective, is the category “right question, wrong answer”. Here, the initial requirement has been correctly evaluated, but something has gone wrong on a technological level on the way to a concrete project. You could also say the specific technology proves to be the wrong solution for the specific problem. Well-known examples include hovercrafts and magnetic monorail trains. The IT branch can look back proudly on a long list of such misguided developments—WAP, for example, that no one really used, or the multitude of data storage media that today reside in technology museums—who still remembers ZIP drives or MicroMV cassettes?


And the third type of flop, the running gag, has a shining IT example. This category is home to all products that already exist in prototype form and next year are sure to have their big breakthrough—since the year dot. Remember the intelligent fridge that orders whatever you need on the internet? No consumer electronics fair is complete without such a fridge that resides in a futuristic kitchen of tomorrow. It has been technologically possible for a long time, but this object of fascination falls down due to a lack of trust on the customer’s part that a kitchen appliance can actually predict what they would like to eat over the coming days. Looking back, we don’t only chuckle at pie-in-the-sky predictions of experts, but also products that completely missed the mark. Of course, hindsight is a wonderful thing. Or, to put it in business terms, looking back, we can easily see a strategy. And flops teach us two important lessons: how to avoid them and why we should make them.



Why flops are still important.

Poor data availability, insufficient models or simply a too centralised approach are all triggers for flops. Planning and implementation are carried out by departments far away from each other. The strategy department sits high in their ivory tower with a wealth of Excel sheets and hasn’t seen a customer in a long, long time. The thought and planning processes happen in an artificial bubble that will inevitably burst—at the very latest at market entry—as a rule with painful consequences for the financial prosperity of the company. The linearity of development cycles in which a strategic or R&D department toil in secret on a product of the future at the start of the process that is then refined through the Innovation Funnel is a thing of the past. This approach has a hefty disadvantage—the customer only comes into play at the very end. In this way, far too much time and energy in the company is spent planning something that has a very uncertain chance of success. Future-oriented innovation methods rely on transparency and openness and involve the customer from very early on, using rapid prototyping techniques to create testable draft versions with little effort and discuss them with customers, accessing a wide range of customer, partner, and prospect opinions via crowd sourcing.

A London furniture store offers its customers the opportunity to have their say on every piece of furniture that is to appear in future collections, and then orders the pieces that have good ratings in terms of design and price.

Would methods like these lead to fewer mistakes? No, maybe even the opposite—and that’s not a bad thing. Because innovation needs experimentation and mistakes have to be made—as many as possible in the fastest developing industries—in order to learn from them as quickly and as well as possible. The difference is that these methods cannot be used over several years to work towards a goal that will turn out to be a flop, but valuable feedback will be gained and processed far earlier on in the process. More mistakes are made, but fewer crucial ones.


It’s not easy—on neither a methodical nor a cultural level. Because in the technological industries it means a drastic undermining of expertocracy while turning to openness, a willingness to make mistakes and a new learning culture. What if the employee of the month is the one who makes the most mistakes? Very often, consistent pursuit of such an innovation strategy leads to a fundamental shifting in corporate culture and philosophy.

A basic quality of companies that successfully use such methods is a fundamental curiosity. The feelers have been put out and the channels of communication are unhindered and fully-functional between the different departments.

In this way, signals can be quickly and reliably detected. While companies are generally well set up structurally when it comes to detecting quantitative swings on scales such as unusual growth or cost explosions, it’s the qualitative signals that can make all the difference. Approaches and opinions change, public discussions acquire a new tone and transform previous advantages into disadvantages and the image of whole product categories change. 15 years ago, Apple products were for graphic designers and individualists. Today it’s products represent an entire generation. And in ten years? The answer to this question cannot so easily be answered by market share analyses or appraisals of technological innovations, but rather through careful perception of values and consumer attitudes. Whether the next iPhone will one day be a technological revolution is perhaps less decisive than the opinions that form up until that point around the brand and the product category, because innovation is seldom a new technological development, but more a new practice. It’s how new things are used and applied, how we use them in daily life that singles them out as innovations and distinguishes them from pure inventions.


To fully experience this difference in reality, we need to keep an unobstructed view of socio-economic undercurrents in mind. Curiosity and reacting to signals are in no way a deviation from long-term perspectives, but a step towards recognising these underlying changes early on and so being able to predict them more reliably. A way to do this is to use cross-innovation—watching and learning about other industries, always considering what can be taken from those and used in our own. To understand the value changes in societies, it is wise to consider which status symbols can be seen currently and which properties they have that can be transferred to their owner. The fact that high-power cars are selling considerably less well than before is linked to rising fuel costs, but mainly with how friends and family react when someone arrives in a huge car. Are they proud and even envious of someone who has managed to afford such a vehicle? Or do they wrinkle they noses at the pretentiousness and disregard for the environment and sustainability? The change in demand has very little to do with the technological capabilities of the car. In the IT industry, recognising this can be used in key ways.



Don't predict the future. Shape it.

The reality of economic trade spans mid and long-term forecasts and the dynamics of the current market, and poses management the challenge of achieving the right mix and deriving strategies and innovations from these, that can be effective quickly and have long-term staying power. A compass for this uncertain course can be obtained by expanding and deepening our perspective used to consider or even plan ahead for customers' wishes and markets and subsequently rethink innovation behaviour and its parameters of influence. Because we can take the future into our own hands rather than predicting it from the sidelines.

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Bechtle update editorial team



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Published on Oct 18, 2019.