Anticipating the Future: A Strategic Approach


“Foresight marks the ability to see through the apparent confusion, to spot developments before they become trends, to see patterns before they fully emerge, and to grasp the relevant features of social currents that are likely to shape the direction of future events.”

(Rohrbeck et al., 2015)


Of any superpower to possess, the power to see into the future is the most valuable of all. It’s the sine qua non of disruptors for individuals, companies and even gods. In Greek mythology, the gods blinded the seer Tiresias in an effort to stop him from prognosticating and revealing their secrets.  For companies, the stakes are even greater. Indeed, much of a company’s very activity is directed to predicting, planning for, and attempting to shape a particular future. In this way, the creation of winning products and services becomes not only an exercise in understanding today’s customer, but the necessity of peering into the mind of tomorrow’s. Success means going back to the future, again and again.

For centuries, charlatans and, depending on whom you ask, those with true “powers” of prognostication from Tiresias forward have become rich, famous, and even worshiped for their purported gifts. And why not? If one truly has the power to foretell what is to come, strategy determines itself. Ignoring the philosophical issue of chicken and egg time loops, one with such a power would merely need to align their pieces to properly react to the moves that others, whether rival generals or customers, would be making in the future. The prisoner’s dilemma would merely become the “just pick whichever choice screws you least based on what you already know about the move of the other prisoner” dilemma. In other words, knowing the future, breaks the game.

Tiresias notwithstanding, there is little evidence that anyone actually has, or has ever had, such a power in history. Many have and continue to claim to be able to do so, however. The most famous of current prognosticators, Ray Kurzweil claims the ability to predict the future with an astonishing 86% accuracy, and without any mystical or otherwise superhuman powers. However, without independent verification, it’s a claim that we can only take Kurzweil at his word for. Google’s success, where he works as their resident futurist, does bolster the idea that disciplined strategies or models of thought may improve one’s ability to accurately understand likely developments in the future.  Indeed, there are a number of techniques that organizations and individuals can employ today to help improve their chance of predicting the future, even without a resident seer on staff. 

All predictive techniques have strengths and limitations that impact their overall effectiveness.  Taken together, however, along with a culture that fosters innovation, predictive techniques can help organizations to better understand and position themselves for the future. But they’re not enough. Most critical is adopting the right mindset. That is, adopting a critical mindset to continually assess outcomes in order to refine strategy, whereby the risks and rewards inherent in making such decisions becomes clearer and the future less opaque. Such a mindset shares much in common with those whose livelihoods depend on anticipating future outcomes like professional gamblers. Instead of soothsayers, CEO’s should look to this mindset as they peer into the abyss of the future. This paper details what a future mindset entails, how to adopt one and how to best leverage it in a foresight practice. But before delving into the mind of a professional gambler, one needs to understand the limitations of current futurecasting methodologies.



Emerging technologies and disruptive startups continue to gain a foothold across industries, driving innovation across categories at an accelerating rate.  Even as the pace of change increases, we see the need to bring new ideas to market is increasingly tightening, bringing an explosion in interest around methodologies that purport to anticipate the future. And while no single approach can provide a completely accurate picture of the future, many provide situational advantages for a particular organization, market, or predictive task.  By understanding these techniques as well as their strengths and weaknesses, enables organizations to deploy them in a targeted manner more likely to yield successful results. 



The most straightforward – and easiest -- technique is to simply ask those who have relevant domain expertise in the current area one wishes to anticipate. The connection between prediction and expertise are well documented, having been the subject of serious social science research for some time. Foremost among this research is Phillip Tetlock’s and his seminal book from 2006, Expert Political Judgment. Tetlock, a University of Pennsylvania psychologist, utilized data collected over 20 years from a varied group of domain experts to make a total of around 28,000 predictions concerning politics, war, economics, and other topics. After scoring the accuracy of the predictions, the data indicated, somewhat depressingly, that experts overall performed little better than “dart throwing chimps.”

Of course, “dart throwing chimps” was simply the average accuracy of experts. Some performed worse and some performed better. And, according to Tetlock, there are indeed some attributes that can enable one to outperform the prediction average in a non-random, statistically significant way. Some of the attributes that Tetlock outlines, in fact, overlap quite a bit with attributes that make for successful poker players. 

First, practice matters. Those who submitted more predictions to Tetlock’s study over the 20-year period got more accurate over time. What this suggests is that high-practicing participants were able to modify how they approached particular problems based on the feedback they received from previous predictions.  By being open-minded and willing to accept and adopt evidence contrary to their initial predictions, their accuracy significantly improved. Those not locked into a particular way of thinking or a prescriptive worldview fared even better: the shades of gray outperformed black and white. Additionally, those who worked in teams typically did better (In Tetlock’s study, researchers were divided randomly). Integrating various viewpoints and perspectives helps improve predictive accuracy, provided the team is able to effectively bridge divides and work together cohesively. A wide variety of inputs increases the known variables in a situation, and thus significantly improves outcomes.

Ultimately, Tetlock found that the most successful forecasters are those who understand and leverage the power of probability.  As he would later argue in the follow up Superforecasters, the best prognosticators calculate specific probabilities into their forecasts, taking the time to understand specific likelihoods, rather than reducing terms to simpler understandings such as right and wrong, true or false, and yes or no.  In other words, probability enables forecasters to explore the likely future with greater relevancy.  In practice this works by evaluating equally the disparate bits of relevant information in order to generate specific abstractions of chance.  As new information becomes available, the best forecasters continually subject their claims to further evaluation, amending probabilities up or down as appropriate, rather than remaining entrenched in a past prediction.



Trendcasting is the art of utilizing today’s trends to forecast tomorrow’s business context. Adherents believe that by identifying important trends, then reassembling them in a novel way reveals opportunities for innovation opportunities.  Transportation + Automation, or Coffee + Subscription Services are simple examples.  This approach yields positive results in some instances, but it remains most effective for short-term strategies.  Using only today’s trends, even when combined in a new manner, fails to account for other variables that impact the accuracy of a prediction to the point where outcomes become unstable or unreliable.  Trendcasting brings its own butterfly effect.

While this technique is generally best applied to the near future, it can be used to good effect when done properly. Quantitative data showing the technique’s effectiveness is limited, however there is an abundance of qualitative evidence. Take, for instance, the popular dating application Tinder. The application is, essentially, a fairly natural confluence of trends that had long been occurring and could be observed and seized upon with a keen enough eye. First, the Internet had long been enabling new ways to foster connection between people. Social networking sites then shifted people’s beliefs around the linkage between their digital and analog selves. And finally, smartphones heightened people’s expectations for instant gratification. As if inevitable, the instant gratification, social network, human connection platform was born.

Successful application of trendcasting requires not only an understanding of where people are going, but the underlying reasons behind their movement.  It is not enough to know what but to know why.  It also requires a clear understanding of the elements that actually make up a trend. The first, is an external event that enables new forms of life. The genesis of the Internet is an example of such an event. Second, is a novel business or idea that takes advantage of the event. For example, Napster utilizing the internet to create a new music sharing platform. Finally, only when underlying needs have been identified and successfully marketed to can trendcasting yield its full advantage.  Spotify would ultimately fulfill the promise of Napster by building a service based around the underlying consumer desire for near infinite choice and immediate access to music, while also providing a mechanism to compensate artists for their work.

Being first to identify or even act upon a trend does not necessarily guarantee success, as the examples of Spotify and Napster demonstrate.  The key is in leveraging needs into sustainable business models. Take Webvan for example.  Without a doubt ahead of their time, Webvan pioneered online grocery delivery in 1996. While the time-saving needs that Webvan identified were (and are) real customer needs, Webvan’s failure in attempting to scale their business too quickly grew ultimately from the false belief that being first was the key to success.



Perhaps the most cliched quote in all of innovation is Henry Ford’s oft quoted and likely apocryphal “If I had asked people what they wanted, they would have said faster horses.” There is scant evidence that Ford actually said this (Vlaskovits, 2011), but the underlying point that people are only truly capable of seeing the future through their own frame of reference is valid. The practical implication being that innovators should not be afraid to simply make the future.  In time, the public will follow along.

This is, essentially, the concept of a self-fulfilling prophecy as applied to innovation. While self-fulfilling prophecies might sound like a pop-psychology term used to admonish anxious people to not obsess over negativity, it is actually a well-grounded theoretical concept coined by the late Robert K. Merton, an American sociologist whose work has had a major influence on our current thinking about society.  Merton defined a self-fulfilling prophecy as a false definition of a particular situation that shifts one’s expectations of the future, leading to new behaviors that ultimately cause this “false” conception of the future to become true. In other words, a self-fulfilling prophecy is simply an idea about how the future will be that ultimately affects a person or people’s behavior in such a way that it actually makes that idea reality.

Merton, in addition to being a sociologist, was also an amateur magician. It is perhaps no coincidence, then, that magic tricks and self-fulfilling prophecies have much in common. Like a self-fulfilling prophecy, a magic trick uses an audience’s expectations in such a way so that the future as revealed - the ta-da moment - is both validated by and built upon their expectations. Like the Wizard of Oz, the role of innovator as a magician (or manager, sometimes even manipulator) of people’s expectations is itself a way of predicting the future.  Look no further than Elon Musk.

Possessing of both a clear and inspired vision, Musk, has not only convinced the public, press, and competitors to accept aspects of his vision, but through this acceptance, he has effectively galvanized action around it, ultimately cementing elements of it as reality.  The strength of such an approach is its ability to utilize such feedback loops - one of the strongest aspects of a self-fulfilling prophecy (Barnett, 2014) - in order to build momentum and buy in for the vision, ultimately making it less dependent on a visionary leader.

With Musk we can see that by having a strong point of view on the future, one that is publicly and loudly expressed, a profound effect on future business contexts can be understood. But using self-fulfilling prophecies suffers from similar weaknesses as any other magic trick.  If the reveal does not meet expectations, such as Elizabeth Holmes’ Theranos, or if the public is distracted by someone else’s competing vision, the slight of hand becomes apparent and the magic fizzles out.



Using big data to make predictions is essentially the philosophical concept of determinism made real. According to determinism, our universe is causal, meaning all effects are determined by distinctive causes within a defined system.  If everything about a system is known, then outcomes of that system ought to be reliably, even simply, calculated. There’s one major problem with this. Despite progress made in predictive analytics, artificial intelligence, and raw processing power, the world remains too messy to reliably count on big data to show us everything that’s ahead - just ask the weatherperson.

A great example of the promise and peril of big data predictions is Google Flu trends. In 2008 Google claimed that by utilizing the massive amounts of data on what their users search for daily, they were able to reliably predict flu outbreaks in the United States five days before they actually occurred. Eventually, however, the wheels began to fall off of their predictions. As Wired magazine put it: “And then, GFT failed—and failed spectacularly—missing at the peak of the 2013 flu season by 140 percent. When Google quietly euthanized the program, called Google Flu Trends (GFT), it turned the poster child of big data into the poster child of the foibles of big data. But GFT’s failure doesn’t erase the value of big data. What it does do is highlight a number of problematic practices in its use—a sort of ‘big data hubris’” (Lazer & Kennedy).

The biggest problem with big data predictions, as in the prior example, is the tendency to overfit the data.  Overfitting occurs when a model is created that, while explaining the data within a set, fails to identify and take into account non-causal noise that may get wrapped up in a data set.  In an overfit model, this noise ultimately distorts predictions enough that the reliability and accuracy of the model itself blows up.  And while outside of the quantum world, the world may still appear deterministic, we have yet to get the models right.  No doubt big data will continue to improve and its ability to make certain kinds of calculations are clearly unrivaled.  For now though, the sheer complexity of the real world makes future casting beyond the reach of even the best AI’s.



Using the crowd, whether by the thousands or millions, employs much of the same reasoning as  big data: sheer size and volume make predictions more likely to be true. While any one person may have a myopic view of the causes currently forming into particular effects, taken at an aggregate level, the members of a large mass of people should each have a window into the workings of many causes, both big and small, that will eventually result in a particular future.

Similar to the theory underlying Adam Smith’s Invisible Hand, the crowd-as-predictor has been championed by libertarian philosophers for decades in the form of Prediction Markets.  In a Prediction Market, outcomes of events are traded like stocks, with prices determined by crowd consensus on the probability of an event. 

A popular example of a prediction market is, run by the non-profit research foundation On the site, users can buy and sell the predictions on future pop-culture related events. Fameresearch is ultimately seeking to utilize the data collected on the site to better understand exactly how well the crowd really can predict the future.

An interesting and potentially very powerful future method of aggregating crowd data into future predictions, is the utilization of blockchain. Blockchain is by its very nature a decentralized information source. A natural next step in the application could very well be a prediction on what millions of people expect to happen next in the world. But as anyone who follows cryptocurrencies knows, the value fluctuates considerably, defying those who seek to predict its value. Even when everyone thinks the exact same thing, it doesn’t mean that they are necessarily correct. On the eve of November 7, 2016, how many reliable sources predicted that the next day the American people would elect Donald Trump to the President of the United States?



Of course, if one has simply given up on trying to predict the future there is always the Silicon Valley mantra of fail fast, fail often. The idea being that failures are inevitable; therefore, one should embrace this fact and simply develop a portfolio of different approaches and pivot as necessary. And indeed, this has worked well for many in Silicon Valley, on the whole, as it embraces mathematical thinking.

While any single startup or innovation has a low chance of success, when multiplied by thousands, the odds that some entity will become the one unicorn increases substantially. The problem for many organizations, is that this mindset primarily benefits the innovation community as a whole, rather than a single company or even industry. By itself, this strategy does not tell anyone where to concentrate and, moreover, it means that a large corporation is always at a disadvantage to startups. The startup community has a mandate to try and fail; established companies have a lawfully mandated responsibility to return profits to shareholders. Well-run companies, even those with a high success rate for new innovations and ventures often fail to take full advantage of the highest value opportunities simply because they are unequipped to assess and take bigger risks.



All of these techniques when properly applied can be used to an organization’s advantage, and should be among the considered set of a robust innovation toolbox.  But while these approaches can help identify potential scenarios, they do little to actually assess the risk and reward of those scenarios.  When we combine innovative thinking with the right mindset, we are better able to understand the competing outcomes before us.  For insight on what such an approach might look like, we can look at the mental models employed by professional who actively engage in anticipating future outcomes. Top tier gamblers, including world class poker players and sports bookmakers are able to regularly move future outcomes in their preferred direction through skill, technique, and most importantly through the adoption of a disciplined and focused mindset.

In a nutshell, professional gamblers do not think in terms of right, wrong, true and false, as most think of when considering the “correct” path to take to arrive at some preferred future outcome. Rather they think in terms of odds, statistics, slight percentage differences, edges, and expected values. In other words, gamblers employ a method of thinking where decisions are never judged by their outcome, because no outcome is ever guaranteed, but instead judged by the chance that that decision will provide the player an edge over the house. It is a mindset with infinite shades of gray, some slightly darker than others.   

Contrast the professional gambler with the decision making of an average corporation, or rather the decision making of the people that make up a corporation. The constituents of such organizations tend to be risk averse in their decision making - not because of an inherent aversion, but because internal incentives such as promotion and bonuses typically reward only good outcomes. Unfortunately, even the best of predictions can sometimes result in bad outcomes for a myriad of reasons beyond the control of the decision maker. The result is that decision makers often implement the strategies that yield the highest chance of a successful outcome even if the value delivered is lower than what should be achievable. In other words, Decision A might have a 95% chance of success, but at best yield a meager 2% growth.  Decision B, on the other hand might carry only a 50.1% chance of success but the potential for 10% growth.  Utilizing a statistical approach decision B would result in a higher value to the corporation on aggregate, however in most circumstances decision A is almost certain to be chosen.  The near guarantee of some success, particularly in a culture that rewards decisions makers for predictable mediocrity rather than encourages calculated and thoughtful risk taking invariably results in poorer results in innovation for such corporations over the long term.

Breakthrough innovation inherently has a low chance of success.  But, because corporate decision makers focus only on outcomes, rather than chance and statistics, they rarely take the juiciest of bets like a professional gambler would. Professional gamblers do not care how many times they lose. They are willing to lose ninety-nine out of a hundred times, so long as that one win’s expected value overtakes all of the other losses. Unfortunately for corporations, there are literally thousands of hungry startup founders just waiting to take the bets that corporations leave on the table. Most of these founder’s bets will lose, but multiplied over thousands of chances with huge winnings possible, eventually there will be a new jackpot winner to contend with.

This problem compounds because only rarely does one decision maker hold the “yes” or “no” decision on a particular bet. Instead, corporations almost always have numerous stage gates with their own decision makers, each who wants to look like they made the “right” decision at the end of the year. Dozens of gatekeepers see the bad odds, thinking in terms of outcomes, and kill what could be a jackpot winner. No one wants to be the one responsible for the “wrong” decision, even if it might be the best one.

In this way, large institutions are almost inherently incapable of breakthrough innovation through traditional channels. To successfully innovate, employees and particularly those working on anything novel cannot be judged by how many hands they win. Instead they must be judged by how much value they create over the long term. Moreover, just as a professional poker player spends significant time and effort to understand the precise odds of success of various actions, given particular scenarios and signals about the future (i.e. the cards in one’s hand is the present scenario and the signals about the future are the likely cards left in the deck and in others’ hands), a company should be carrying out similar work. Every company should know, at least in an approximate sense, what the odds of success of courses of action based on all of the signals they are getting about the external world.

By leveraging one, several, or all of the techniques discussed above, future scenarios can be identified and explored, giving an organization a myriad of potential futures to assess, and an essential edge over others attempting to do the same.  Taking a lesson from gambling, one can then apply odds to the likelihood of such future scenarios as well as realistic values for both favorable and unfavorable outcomes.  These estimates, both in terms of the odds and tangible results are not set in stone but should be continually revised as new information becomes available.

Using this data, a portfolio of potential futures can be imagined.  And, like a stock portfolio, it should be balanced with some moon shots and sure bets, ultimately balancing an acceptable amount of risk in order to deliver a positive value over the long term.  These futures are the bets.  Some will be winners and some will be losers, but as new signals come in, including the results of one’s own wins and losses, the portfolio is updated with new values that inform better decision making in the future. 

In placing tangible values that that can be understood and debated, a company can develop a much sharper sense for which strategic choices and innovation opportunities to take leading to a particular future. And just as master poker players build and adapt their strategies around what each card reveals – the best innovators continually revise their understanding, adjusting strategies in order to deliver high-value results.



There is an increasing need for business, particularly those in markets undergoing (or yet to undergo) transformation to reassess the relevance of their business model.  For every Netflix, there’s a Blockbuster, every Facebook a MySpace.  But there are also stunning examples of turnaround, companies that adapt and thrive by being able to visualize the future with some measure of accuracy.  And it does not take a prophet – either of the Rasputin or Steve Jobs variety – to improve an organizations ability to assess the future.

Despite several high profile blunders (Juicero), changing regulatory climates (Uber, Airbnb, Google), and shifts in the public perception around tech and tech companies (Facebook), the pace of innovation shows little sign of slowing.  For every disruptive technology that the public now takes for granted, there are almost certainly new ones under development with the potential to upend today’s norms.  But the future viewed through only the lens of technology is inherently myopic.  As global order shifts, spheres of cultural influence contract and expand accordingly.  We are in the midst of unprecedented global change, economic shifts, climate change, and mass migration. With such changes, there is an increasing need for organizations to institute methodologies that allow them to more accurately predict the future landscape and the consequences of their actions or inaction.  The need to introduce models that make such assessments possible, as well as fostering a culture that encourages calculated experimentation is more important now than ever.   


Barnett, W. (2014). Winning as a Self-Fulfilling Prophecy. Retrieved from Harvard Business Review:

Lazer, D., & Kennedy, R. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Retrieved from Science Magazine:

Rohrbeck, R., Battistella, C., & Huizingh, E. (2015). Corporate Foresight: An Emerging Field with a Rich Tradition. Retrieved from

Tetlock, P. (2006). Expert Political Judgment. Princeton University Press.

Tetlock, P. (2015). Superforcasting. Crown.

Vlaskovitz, P. (2011). Henry Ford, Innovation, and That “Faster Horse” Quote. Retrieved from Harvard Business Review:    

Greg VanderPol