Proof Points: The Four Foresight Skills 

We’re now ready to consider a few success examples, or “proof points” for strategic foresight. Our examples will span the Personal, Organizational, and Global-Societal (POG) domains. We’ll consider strategic foresight successes through the Four Foresight Skills of Learning, Anticipation, Innovation, and Strategy (LAIS). Later, and particularly in Chapter 5 (The Do Loop) we will offer proof points in coupling strategic foresight to the Four Action Skills (EIRR), to create adaptive foresight.

Remember that while LAIS is our best model for how we create strategic foresight, we need all of the Eight Skills (LAIS+EIRR) to be successful as managers and leaders. We must use all Eight Skills well to practice adaptive foresight, foresight that results in successful action. Good strategic foresight only has the capacity to change our plans and actions. It won’t actually do so without the Four Action Skills as well.

Each of the examples in this section required effective action (Execution, Influence, Relating, and Reviewing) to achieve success. We don’t always cover those skills as well as the Four Foresight Skills, as this is a foresight, not an action guide, but we must not forget them. Also, each example below is typically offered with respect to just one, or a few, of the twenty specialties. Yet when we look deeper, we see that just as every foresight success requires all Eight Skills, each example given here also required an effective use of several of the twenty specialties. All of them are important for leaders to understand and use, when appropriate.

With these caveats, let’s look at a few success examples, to see what can be accomplished with good foresight culture and process.

1. Learning Skill – Intelligence & Knowledge Management Specialty Focus. Examples: CompStat, Palantir, Intellipedia, Evidence-Based Mgmt, etc.

Learning is aided by three foresight specialty practice pairs in our model, accounting & intangibles, intelligence & knowledge management (KM), and learning & development (training). Let’s look now at a few generally recognized foresight successes in the organizational domain, focused mostly on the learning specialty of intelligence & KM. We’ll explore the other learning specialties further in Chapter 5 (The Do Loop) and elsewhere in the Guide.

One notable 20th century learning proof point in intelligence & KM which also, secondarily, an anticipatory (data science, forecasting, security) proof point (Skill 2) can be found in CompStat, a platform originating in 1994 in New York City’s Transit Police Unit for sharing crime statistics, resource management, tactical options, and feedback among law enforcement professionals across the unit. CompStat was originally called Charts of the Future, as it continuously tracked historical crime data and also extrapolated future trends, initially via pins stuck on maps.

Under Chief William Bratton, CompStat was soon professionalized, and adopted in 77 precincts and 12 transit districts across New York City. It introduced statistical and predictive policing, crime mapping, weekly crime reports, and accountability of unit commanders for crime outcomes. It is also notable for incorporating broken windows theory, an evidence- and psychology-based criminology theory that focuses security professionals first on the most visible and easily fixable examples of crime, disorder, and anti-social behavior. It is well known in social science that simply living in a more littered, vandalized, and broken down environments makes citizens more likely to engage in anti-social and misdemeanor criminal activities themselves. By addressing such easy fixes first, security professionals could address the more obvious factors eroding public trust and collective security, creating a positive security trajectory, and conditions where more ambitious crime reduction, social services, security improvement projects can be publicly proposed, funded, and launched. An excellent (though also self-congratulatory) account of CompStat’s value in foresight, and some of the politics of its establishment, can be found in William Bratton’s Turnaround (1998).

Another powerful 21C intelligence & KM platform, also primarily a learning and secondarily an anticipatory platform, is Palantir, founded in 2003. Palantir has developed a very powerful learning and foresight platform that they offer to military, intelligence, law enforcement, and civilian clients. It is a descendent of CompStat, though Palantir apparently does not yet have the crime-reduction philosophy and management accountability features of its predecessor. Palantir built its first system predicting and managing insurgencies in Iraq, and is widely considered a strong success in that application. The company next applied their platform to law enforcement. It has not yet had the same level of success there, primarily due to ethical lapses. In that new arena Palantir’s leaders have exhibited insufficient transparency, including not fully publicly disclosing and getting public consent for its operation in New Orleans (2012-2018). The greater risk of civil liberties infringement for civil law enforcement applications of such learning-predictive systems, make it clear they need increased transparency and democratic oversight to be successful.

Another 21C proof point for intelligence and KM foresight, which is presently much less anticipatory than the others just mentioned, is Intellipedia, the collaborative learning platform (wiki) established in late 2005 by Chris Rasmussen and other young analysts in the US intelligence community. Intellipedia was the first online effort to get 16 different and compartmentalized intelligence agencies within the US to share their relevant classified knowledge with each other, on an ever growing number of topics. It was established at three different classification levels, Sensitive, Secret, and Top Secret (the lowest and largest rung of Top Secret). When first established, most senior officers expected it to die, and several actively resisted it. Senior offers often prevented junior analysts from using it as a source, the way some teachers discourage the use of Wikipedia as a source, even today. But within three years, Intellipedia had become a vital tool in intelligence knowledge management. Today every analyst, whether junior or senior, typically has a shortcut to it in their classified browser. It has vastly improved analyst knowledge of relevant history and current status of many of the most challenging topics in US intelligence. It has also become a “water cooler” for the many silos within the US intelligence community, as it offers a new way for professionals to share useful information and chat across the community, regardless of rank.

The ROI on launching a project like Intellipedia is clearly off the charts, were we to assess it based on community use, satisfaction, and learning and development (another learning specialty). We might would hope that its success would spur investment in the kind of collaborative brief production and prediction platform that was also originally envisioned as a feature of Intellipedia by Rasmussen, but never funded. In reality, the politics and bureaucracy of the defense establishment have caused it to move into anticipation functions slower than it should. Nevertheless, one can predict such a platform will eventually emerge, and be of great value in enhancing US security.

These learning proof points are focused on government, and specifically the intelligence and security industries, as these are vital but often-overlooked topics in civilian foresight work. But there are many great examples of learning-driven corporate processes and cultures as well. Evidence-based management is one of the terms scholars use to describe learning-driven organizations. Marr and Davenport’s The Intelligent Company (2010) offers a great overview of management tools (performance dashboards, balanced scorecards, KPIs, KPQs, intangibles surveys) that help leaders use learning and facts to guide their decisions. It gives examples of over a dozen companies, large and small, that use these tools well. 

Google offers a particularly strong example of a learning-oriented, evidence-based corporate culture. They are constantly collecting data, and seeking to use the most evidence-based practices throughout the organization, in HR, R&D, engineering, sales, and just about every other department. Lazlo Bock’s Work Rules! (2015) is a good overview of the evidence-driven people management practices Google uses to hire, manage, and retain talent. 

IBM was once a strongly evidence-driven and R&D-based company, but it has increasingly lost its way in recent decades, as Robert Cringely documents in The Decline and Fall of IBM (2014). IBM’s decline is not yet recognized by the media or stock market at its proper level, but it is following a path that HP, GE and other industrial giants followed as they lost their R&D-, evidence-, and learning-first cultures. Microsoft, by contrast, is more successfully reorganizing itself as a learning organization, and recognizing that it must put customer and employee satisfaction at the center of its processes. Read Microsoft CEO Satya Nadella’s Hit Refresh (2017) for an excellent (and predictably company-congratulatory) account of their learning-driven turnaround in recent years.

2. Anticipation Skill – Forecasting & Prediction Specialty Focus. Examples: Paris, Profiles, Clarke’s Law, The First Law of Foresight,etc.

Anticipation, or probability thinking, is aided by five specialty practices in our model, data science & machine learning, forecasting & prediction, investing & finance, law & security, and risk management & insurance. Let’s now scan a few 19C, 20C and 21C foresight successes, primarily focusing on the practice specialty of forecasting & prediction, beginning in the personal foresight domain, and focusing on a class of easily found examples–individual authors of foresight texts. Corporate forecasting and prediction success is not as easy to find, as their work is not as public, and successes are not celebrated. We’ll discuss anticipation-oriented organizations in each of the above specialties at the end of this section. 

One 19th century example of commendable anticipation, Paris in the Twentieth Century (1963), not published at the time because it was deemed too far-fetched by the publisher, was written by then-young futurist, Jules Verne. It was recently rediscovered in a family safe by Verne’s great-grandson. Paris accurately portrayed large metropolitan cities, automobile culture, elevators, fax machines, and homelessness, among other insights. One of the first great 20th century examples, Anticipations: Of the Reaction of Mechanical and Scientific Progress Upon Human Life and Thought (1901) was written by the then-young futurist H.G. Wells. The latter was published and widely read at the time, and like Paris, it also offers an impressive mix of many highly accurate and a small set of culturally biased and flawed predictions in technology and society. Another great piece of multidisciplinary anticipation is The Next Hundred Years: The Unfinished Business of Science (1936), by chemical engineer Clifford Furnas. While this work also predictably falls down on social foresight, due to its unrecognized cultural bias, historical foresight analysis shows that it offers a largely brilliant look ahead in a particularly challenging area of foresight: basic science. 

Another commendable set of anticipations is found in Arthur C. Clarke’s Profiles of the Future (1962). Among many other things, it predicts an electronic “global library” (the web) in 2005. Clarke made the same prediction in a video interview in 1964, and by then he’d shortened the arrival window to 2000. In that video he also described telesurgery, a practice we’ve experimented with since the early 2000s, and which can make commercial sense only once we have much better medical robotics. Clarke as an individual was an amazingly humble, self-critical, open, and curious prognosticator. He was also deeply scientifically informed. There have been few to match him in our modern era. Among many other successes, he famously predicted the geosynchrononous satellite an detailed technical article in Wireless World magazine in 1945.

In this 3 minute excerpt of that 1964 video, Clarke notes that scientific and technological predictions of the future, when successfully done, typically fall between “two stools”. If the predictions sound reasonable, they will typically take place sooner rather than later, and the predictor will later be considered too conservative. If, on the other hand, the predictions are both long-term and accurate, taking into account the accelerating nature of science and technology, many will appear “absolutely unbelievable” and “so absurd, so far-fetched,” that their audience will usually laugh, disbelieve, and scorn the predictor. Jim Dator, founder of the U. Hawaii PhD in Alternative Futures, later summed up part of Clarke’s insight as “Dator’s Law: “Any useful statement about the future should appear to be ridiculous.” I have a lot of respect for Dator, but find this formulation too extreme. Let’s reword this for greater accuracy, and call it:

Clarke’s Law of Science & Technology Anticipation:

Useful statements about our probable S&T future will always straddle two extremes: conservative and near-term, and ridiculous and long-term.

Standing on the broad shoulders of Clarke’s Law, we can offer a corollary that may be even more useful. This one is not restricted to S&T but applies to all STEEPS predictions, and to all forms of foresight. We can call it:

The First Law of Foresight:

The most useful statements about the future offer as much RODA (Risk, Opportunity, Disruption, Adaptation) as people can believe, while avoiding ridicule.

The First Law reminds us to continually gauge our audience, and know just how far out we can go to get an adaptive response. If your client thinks your forecasts and predictions (or possible and preferable futures) are ridiculous, you need to dial them back until they are willing to believe there are real risks, opportunities, and disruptions ahead, which will motivate them to adapt. You can’t go to long-term S&T prediction with your client, the way we do in Chapters 7 (Acceleration) and 11 (Evo Devo Foresight) of this Guide until they have enough context to see the trends and causal factors that seem to be driving accelerating change. You can only go there with people who are ready. Every good predictor should be aware of the consequences of long-term S&T prediction, and develop a thick enough skin that they can handle the personal attacks they will inevitably receive, often from well-meaning individuals who just want the world to stay the way it is today, or was in the past.

Notice that Clarke’s Law does not include EEPS futures. Most of our long-term Economic, Environmental, Political, and Social futures are actually very predictable today, not unreasonable at all, once we adjust for our current cultural biases. Those preferences have been baked into our genes, over millions of years, and behavioral science and psychology are unearthing them now. They won’t change until our genes change, which will happen long after accelerating S&T continue to disrupt our societal futures. The vast majority of us want a more ethical and empathic world, with greater individual empowerment, fairer laws, better social safety nets, and greener and more sustainable environments. I think it’s also obvious we’re going to increasingly be able to get those outcomes in the future, as technology and AI empower us all. But on the way there, as usual, we’ll also have to deal with a lot of near-term risk and disruption from a subset of self-centered and opportunistic individuals, and their politics and business models, that don’t put the well-being of life and humanity at their center.

Another famous 20th century anticipation proof point is found in Herman Kahn and Anthony Weiner’s The Year 2000: A Framework for Speculation on the Next Thirty-Three Years (1967) a report on a long-term foresight project of Kahn’s think tank, the Hudson Institute. Kahn was a foresight practitioner who recognized the value of starting with the probable future, then developing possible and preferable futures within the confines of the probable. A retrospective review of this book by Richard Albright, What Can Past Technology Forecasts Tell Us About the Future?, Tech Forecasting & Social Change (Jan 2002) found that 50% of the one hundred very likely technical innovations listed in the back of The Year 2000 had been “good and timely” forecasts by 2000, and that forecasts in the subfield of computers and communication s had the highest success, at 80% correct.

The Year 2000 is impressively insightful in both science and technology forecasting and in many economic and social forecasting areas as well. Where it misses the probable S&T future, as in assuming the continued centralization of computing rather than its distribution (PCs, mobile) and its recent recentralization (cloud, platforms) it does so because it doesn’t understand certain features of complex systems, such as the way they are in continual tension between bottom-up and top-down control, due to their evo devo nature. In short, it doesn’t miss much. Even then, Kahn and Weiner offered us an exponential perspective, citing the “bewildering speed of technological doubling” (Moore’s law was then just three years old) in the language on the book’s jacket. In many ways, The Year 2000 anticipated Alvin Toffler’s much deeper discussion of socio-technical acceleration in Future Shock (1970), and it is a great proof point for how to do foresight work well, and mostly right. We’ll discuss both of these great works of successful anticipation a bit more later in the Guide.

For good 21C examples of anticipation at the individual level, let me recommend Kurzweil’s The Singularity is Near (2005), Diamandis and Kotler’s Abundance (2014), McAfee and Brynjolffson’s Machine | Platform | Crowd (2017), Pinker’s The Better Angels of Our Nature (2012), the Rosling’s Factfulness (2018), or any of the other books in our list of Top Foresight Books in Appendix 3. None of these authors has perfect foresight. We are all human, and individually flawed, each in our own ways. With Kurzweil’s writing, for example, I would advise ignoring the majority of his overambitious biological and medical predictions, as it is a domain he doesn’t understand anywhere as well as IT. But he is also to be greatly commended for making it clear, as far back as his brilliant 1990 edited book, The Age of Intelligent Machines, just how powerful and disruptive AI is now and will continue to be in coming decades, and why. We’ll explore this acceleration-aware, exponential thinking a lot further in Chapter 7 (Acceleration).

Companies, institutions, and governments also successfully anticipate the probable future, the one that will likely arrive whether we want it or not. Probable foresight in the organization is done in at least five specialty practices, as we have described. Let’s look at those now. Some companies develop strong teams and process in data science & machine learning. In many cases they are seeing the near-term probable future, as with predictive analytics, but some are seeing the long term well. For example, Google and Nvidia are both AI leaders at present, and their long-term visions, market positions, and investments make them look like AI leaders for the foreseeable future, in my view. Others are great at forecasting & prediction. Read Gilliland’s The Business Forecasting Deal (2010) for some great examples of Intel, AstraZeneca, and Cisco using forecasting well. Every forecasting textbook offers a few success examples. Paul Saffo offers excellent advice for forecasting teams in Six Rules for Effective Forecasting, HBR (2007). Read Tetlock’s Superforecasting (2015) for examples of group process that will greatly improve a team’s ability to predict. Forecasting and prediction will often be wrong, but the more we do them, the better we get at uncovering risk, opportunity, and disruption ahead.

Let me offer just two examples of successful corporate prediction. There is good evidence that Apple’s product development teams predicted (did not just guess) the consumer need for the iPod, smartphone, and iPad, in their simplest and choice-curated forms, and used those insights to revolutionize those product categories. These were all obvious products that had to eventually exist, as they are electronic versions of the handheld 3″ x 5″ index cards (iPod, smartphone) and clipboards (iPad) we’ve been using since the invention of paper and pens. Each of these were long anticipated in science fiction. The iPad (tablet), for example, appears in Stanley Kubrick and Arthur C. Clarke’s transcendent film, 2001 (1968). But someone in product, strategy, and marketing had to recognize they could deliver those inevitably successful products. No prediction is perfect, and the timing of an innovation is often more fungible than we realize. Apple could have made a consumer tablet a success as far back as 1993, instead of 2010, as I argue in a counterfactual in Chapter 9, 1993: Tablets and eBooks at the Birth of the Web. But their anticipation was still far enough ahead of the competition for them to gain a major competitive advantage.

Google’s Artificial Intelligence group offers another good prediction example. They released their core AI tools and libraries for public use, under an open source license, in late 2015. Google realized that the 15 million (and rapidly growing!) coders on GitHub offer a far larger development community than their 30,000 software engineers. Getting the global community to use their tools first would offer Google a key new hiring pool and market, as those coders take jobs in companies. Whoever took an open strategy first, with the easiest to use tools, would clearly be able to marshal the most AI talent. Google realized this, and executed, more than a year before Amazon, Facebook, Microsoft, and others did the same with their AI tools and libraries, with predictably much less impressive results. As we’ve said, this kind of prediction isn’t celebrated as much as that of individual authors, or as easy to find and explain, but it happens all the time.

Let’s finish our brief tour of the other anticipation specialties now. Some companies build strong anticipation in investing & finance. This ranges from internal investing teams that appropriately manage a firm’s investable assets, all the way to creative financing and high-frequency and algorithmic trading. For a disturbing account of the still-poorly-regulated world of machine trading, read Patterson’s The Quants (2011) and his even better follow-up, Dark Pools (2013). Some companies develop superior anticipation in law & security. The positive side of this is seen in corporate social responsibility initiatives and triple bottom line accounting, and the negative in new forms of tax sheltering and income and governance hiding, and all those exotic new derivatives that helped create our 2008 global financial crisis. For corporate leaders in risk management & insurance, think of all the large reinsurers like SwissRe, who have the most detailed risk models available in the world today, or think of InsurTech startups like Root, whose leaders realized that an app on your phone that tracks your driving, and allows the insurer to give you custom rates based on both how often and how poorly you drive, would be even cheaper than requiring a $100 tracking dongle for the car. The latter is made by companies like Octo, and used by InsurTech startups like Metromile. Both Root and Metromile are gaining from predicting an obvious future feature of car insurance (usage-based and driving-based rate determination), and both will gain success against the incumbents. But Root’s strategy is the simplest and cheapest, so it has the potential to grow the fastest among the two, all else equal.

Such insights are not experiments (innovations) or preferred futures (strategies). They are anticipations, probable futures that we realize are likely going to work, as soon as someone brings them into the world. Our modern freedom-oriented culture currently biases us against thinking too much about probable futures. We don’t like believing that the world works this way, that we are constrained as well as enabled by the future, but the more we use LAIS skills, in a diverse and critical group, the more probability we can see.

3. Innovation Skill – Alternatives and Scenarios Specialty Focus. Examples: Shell, Mont Fleur Scenarios, Scenario Learning, Wargaming, etc.

Innovation, or possibility thinking, is aided by five specialty practices in our model, alternatives & scenarios, entrepreneurship & intrapreneurship, facilitation & gaming, ideation & design, and innovation & R&D. Let’s now introduce a few 20C and 21C foresight successes, primarily focusing on the practice specialty of alternatives & scenarios, with a few examples in the both the organizational foresight and global-societal foresight (and specifically, governmental foresight) domains. We’ll also mention a few innovation-oriented organizations in each of these specialties at the end of this section. 

One of the best-known 20th century proof points for organizational foresight, in the practice specialty of alternatives & scenarios, is Royal Dutch/Shell’s scenario planning process. It was described by Peter Schwartz in The Art of the Long View (1991), the first widely-read publication on scenario planning. Schwartz was head of Shell’s London scenario planning department from 1982-86. Scenario planning was initiated at Shell in the early 1970s, under Pierre Wack. As Art Kleiner says in The Man Who Saw the Future, in Strategy+Business (Spring 2003), Wack’s team, and his scenario planning successors at Shell, have been credited with helping the company anticipate, among other major change, “the 1973 energy crisis, the oil price shock of 1979, the collapse of the oil market in 1986, the (1990) fall of the Soviet Union, the (2000’s) rise of Muslim radicalism, and the increasing pressure on companies to address environmental and social problems.” If even part of this is true, it’s a stunning example of the value of “what-ifing” the future on a regular basis.

Unfortunately, getting the true details on these claims is much harder, at present. Some of these may be myth, others are at least inflated. Angela Wilkinson, former member of the Shell scenario team, and former faculty in the Oxford Scenarios Programme takes a more critical tone. She says, in a 2013 HBR article co-written with Roland Kupers, that “We have no solid examples of Shell’s having anticipated future developments better than other companies”. But we must remember that unlike the scenario work of futurists like Herman Kahn, which focused on probable futures, or of normative futurists like Gaston Berger and Bertrand de Jouvenel, which focused on preferable futures, Shell focused their scenarios on possible (and at the same time, plausible) futures. Thus it makes sense, given the exploratory aim of Shell’s scenarios, that the Wilkinson and Kupers find little predictive value in them.

In their article, Wilkinson and Kupers also assume that scenarios are used to describe possible futures, which may be preferable or dystopian. They are not intended to explore probable futures in any way. This assumption about scenario work has grown widespread in foresight practice today, versus the early years of our field, that we will restrict the use of scenario to this modern definition as well. In this Guide, a scenario is a story of a possible future. Stories of probable futures we’ll call things like forecasts, predictions, and weeble stories (well-critiqued probable stories), among other names. Stories of preferable futures we’ll call visions, aspirations, strategies, and progress stories (widely-held preferable future visions), again among other names.

Wilkinson and Kuper argie that scenarios make leaders comfortable with an ambiguous, open future, can counter hubris, expose assumptions, create shared and systemic sensemaking, and foster quicker adaptation in times of crisis. This last benefit, a potentially quicker and smarter response to previously anticipated change, may be the most obvious benefit of possibility-focused scenario work. Again, anticipation isn’t the goal of scenarios, but if you do enough of them, you’ll prethink a future that is going to come, and can respond to it ahead of your competitors. The authors quote historian Keetie Sluyterman, who characterizes Shell as “perhaps faster than other companies in catching on to changes in market or culture, by virtue of its sensitivity to emerging topics such as climate change, the rise of China, and the controversial boom in the development of extensive unconventional gas resources in the United States.” Clearly, regular scenario work should make any strategy team more aware of possible futures, more able to identify early indicators and trends that might suggest alternative futures they’ve already considered, and more able to put potential experiments, strategies, and plans on the shelf, in case they are needed. All of this may allow a company to profit faster and better than competitors if such change emerges.

In 1970, when they began their scenario work, Shell was already number two in the world in oil and gas company revenues, at $10.8B, but it still a good way behind Exxon, which had 60% greater revenues (16.6B) at the time. By 2004, Shell had overtaken Exxon, which it has been well established did not have a similar commitment to foresight. If they had, they might have avoided the tragic and negligent Exxon Valdez oil spill in 1989. This 60% growth in relative revenues (to the previous market leader) over 34 years may be partly a testament to Shell’s better use of foresight process and culture, which was started and maintained by senior leadership. Shell was arguably then the leading oil and gas company in the world. BP was barely ahead of Shell in revenues ($285B vs $265B) in 2004, but it achieved that not by organic growth but by a supermerger between British Petroleum and Amoco in 1998. At the same time, rising to the top at the end of the 20th century was no guarantee of success in the 21st. By 2017, Shell had fallen to sixth place, behind newly global Saudi and Chinese oil companies (and now again barely behind Exxon), with BP falling to eighth place after its Deepwater Horizon disaster (another lack of foresight and oversight) in 2010.

One can only imagine how much better Shell’s foresight work might have been, all these years, if they had also attempted to find weeble stories and progress stories as much as they did possibility-oriented scenario work. Wack’s team did start down the anticipation road, at least. The began building a list of apparently inevitable global trends that they called TINA Trends (TINA stands for for There Is No Alternative), including liberalization and globalization, which they argued would continue to advance, on average, across the world. Unfortunately, they didn’t apparently try to quantify and formally forecast those trends, and that type of anticipation thinking doesn’t seem to have extended past Wack’s tenure. We’ll offer our own list of TINA trends in Chapter 7. 

For more on Shell, see the Shell Global Scenarios to 2025 (2005) a compendium of their global scenario work. See also Kupers and Wilkinson’s The Essence of Scenarios: Learning from the Shell Experience (2014) for an in-depth analysis of the value of scenario work to Shell and other organizations for corporate innovation, strategy, and planning. Shell’s scenarios are possibilities, but they also have many nuggets of anticipation within them as well. This lovely book includes a dematerialization curve (we’ll discuss dematerialization as a global trend in Chapter 7), showing energy intensity saturation, the predictably declining use of energy per citizen, the richer any country becomes. This is a curve I’ve never seen in any environmentalist’s reports about our global future. Many people still don’t want to admit that humans are self-correcting, and the richer and smarter we get, the faster we move into realms of technological efficiency and sustainability. We’d rather imagine disasters ahead that are never going to arrive. The data, however, tell us a different story. 

Let’s turn now to a commendable government example of alternatives & scenario use. In 1991, the Mont Fleur scenarios for South Africa, were developed by stakeholders (also an example of facilitation & gaming), by Adam Kahane’s group at Generon Consulting. These scenarios were used to stimulate expert and public debate about what South Africa might be like in 2012, twenty years after its transition from an all-white government. See this PDF summary of the four scenarios the developers foresaw, which the public came to widely understand.

As Kahane describes these, Ostrich (a non-representative government) pointed out the risk and futility of the white government trying to prevent or avoid a negotiated settlement with the black majority. Lame Duck (incapacitated government and economy) imagined a long transition of power, with a constitutionally weak government, and low international investment due to prolonged uncertainty. Icarus (fly now, crash later) imagined a black government could come to power on a wave of public support, and rapidly crash the economy by embarking on large, unsustainable public spending programs. Flight (inclusive democracy and economic growth) imagined a rapid political settlement, with adoption of sound social and economic policies, inclusive democratic policies, and slower economic growth than Icarus at first, but more prosperity in the long term, as there would be no crash.

The scenario team for Mont Fleur included twenty-two diverse stakeholders who met three times, for a three day workshop each time, to generate scenarios in small and large group activities, and the Generon facilitators consolidated them into these particularly instructive four. The scenarios were intended to provoke public debate and generate positive change. They were widely discussed, and they appear to have helped the country’s politicians and the public avoid the obvious pitfalls of the transition. F. W. de Klerk, the last white prime minister of South Africa, was recorded saying “I am not an ostrich” at a press conference before the transition.

As of 2001, Generon had run what they call civic scenarios for seven other countries, and could point to the existence of at least six other projects globally, inspired by their work, run by other foresight teams. For methods, see Kahane’s Civic Scenarios as a Tool for Making History (2001). Kahane argues that civic scenarios offer four important outcomes. 1. Reframed mental models, 2. Shared commitment to change, developed through dialog, 3. Regenerated energy and optimism, and 4. Renewed action and momentum. These are surely reasonable claims, and it would be excellent to see more of the developed world’s leaders employ civic scenarios to engage their citizens in public debate. In our next major swing back from plutocracy toward democracy, I think we can be hopeful that this will occur. 

For a good intro to scenario planning, see Tom Chermack’s Scenario Planning in Organizations (2011). My favorite advanced scenario planning book at present is Fahey and Randall’s Learning from the Future: Competitive Foresight Scenarios (1997). Now over twenty years old, it is as valuable as when it was written. In this edited volume, twenty-five highly experienced scenario developers share their insights regarding using scenarios, in a range of corporate, nonprofit, and government environments, to explore the possible, generate visions, create tensions between completing views of the future, and to resolve them with better strategy. It offers the term scenario learning, as a better way to understand how scenarios fit into the organization. They note that foresight leaders don’t primarily use scenarios for better planning, they use them as a learning tool, with the ultimate aim of improving organizational decisionmaking. So scenario learning is a more general term than scenario planning. It also reminds us that the best scenario production process must integrate well with LAIS, the Four Foresight Skills. 

Companies, institutions, and governments continually imagine or create (mentally or physically innovate) many possible futures. Only a subset of these are obviously at first, or later turn out to be, preferable futures. It is the imaginative, creative, experimental act that primarily drives the innovator. Possibility foresight in the organization is done in at least five specialty practices, as we have described. We’ve covered a few success stories alternatives & scenarios so far. Let’s look briefly now at the other four specialties.

Some companies are well-known leaders in entrepreneurship & intrapreneurship. While entrepreneurship on its face might seem like it is the creation of a preferable future, in reality, it is only the strategy, analysis, and planning that goes before the venture that deserves to be called preference foresight. The creators of startups often have strong preferences and visions, to be sure, but the venture itself is always an essentially creative, experimental act. The vast majority of ventures fail. Entrepreneurship is, in its essence, an experimental, exploratory activity, truly different from strategy. Leaders who understand this will develop incubators that keep their new ventures outside the firm, so they aren’t held back by the larger and older organization’s politics and culture. They develop the tolerance for failure of a good venture capitalist, expecting their entrepreneurs to fail fast, fail lightly, and fail often, and to learn from failure. Owen and Fernandez’s The Lean Enterprise: How Corporations Can Innovate Like Startups (2014) and Mui and Carroll’s The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups (2013) both offer advice and examples of large firms doing internal and external venturing. Chris William’s Venturing in International Firms (2018) explores eight corporate examples in a multinational context.

My favorite intrapreneurship success story from these three books comes from The New Killer Apps and is a bit counterintuitive. It involves Xerox and their famous R&D lab, Xerox PARC. Most students of business know that Xerox famously failed to capitalize on the desktop graphical computer, which they developed as both hardware and software in the 1970s at PARC. They lost this market, without even licencing this incredible innovation, to Apple, IBM, Microsoft, and many others. But as Mui and Carroll point out, Xerox spent just $43 million in today’s dollars on PARC up to the 1980s, and their internal entrepreneurs were able to use that R&D to successfully launch the laser printer, which has generated $100B of revenues for Xerox to date. That alone is a fantastic rate of return. This is a great lesson in intrapreneurship and R&D. It only takes one big commercial win to justify an R&D commitment. You don’t have to get all the fruits of an R&D commitment, only enough to get a superior rate of return. Xerox got big wins in both printing and copying as a result of their long-term R&D focus with PARC. As a result, the company enjoyed an exponential growth phase that lasted over two decades, to the mid-2000’s. Sadly, on the way to that peak they increasingly scaled back both their basic research and intrapreneuring to a shadow of its former level, just as did HP, GE and other giants. But they are still a great historical success story in innovation practice.

Other foresight success stories can be found in facilitation & gaming. Wargaming has long been used in military environments to expose weaknesses in strategy and tactics. Herman et al.’s Wargaming for Leaders (2009) describes Booz Allen Hamilton’s (BAH’s) work in industry simulation games for corporate clients. These games are typically done over three days, simulating the next three years, and require the submission of three strategic moves and plans by a company’s product and marketing teams and competitor teams, and responses by regulator teams, customer teams, and a public team, all coordinated by a control team played by BAH facilitators. A good simulation game, which may only lightly use computers (for financials and numeric dashboards), will explore how an organization’s competitive position and operating environment may change over the next few years, based on moves the company’s strategy teams employ. Herman et. al. explore how Florida Power & Light used these games to reject a risky expansion idea, and change their approach to safety and public outreach. Caterpillar used the results of their game to evaluate merger scenarios, and change their R&D plans. The industry modeling required to develop a good game, and the creation of internal teams that are forced to think like competitors (or other threats), can be just as valuable to the organization, long term, as the experiences managers gain by simulating the possibilities in their competitive environment.

There are many well-known success stories in ideation & design. Think again of Apple’s products over the last forty years. Design thinking is a growing set of practices, championed by design firms like IDEO, for imagining and prototyping great new products and services, and reimagining and improving existing products and services, by starting from the customer’s perspective and looking for unexpected solutions. Tim Brown’s Change By Design (2009) is the best-known primer on the topic. Brown is the CEO of IDEO. See The Accidental Design Thinker’s “40 Design Thinking Success Stories” (2017) for some great proof points for this practice. A famous one is AirBnB, early in their startup phase. They were not getting website traction, and as one of their founders, Joe Gebbia, had design thinking experience, they decided to take a fresh look at their website from a creative, customer-centric perspective. They came to the intuition that bookings were poor because the photos of the residences were too amateurish. Their creative solution was to rent a camera, go to their client’s homes, and take better photos themselves. This wasn’t a scalable strategy, but this “experiment” soon showed double the bookings at these homes, made it clear that great photos would be central to their service, and may have even saved their company at its most vulnerable stage. Design thinking is critical to startups, which are constantly pivoting to find their business models, but everyone can use it. 

Some companies and organizations, from small startups to giants like AT&T’s Bell Labs, Boeing and DARPA, excel in innovation & R&D. DARPAs work on the internet, drones, self-driving cars, and explainable AI (the latter not yet widely recognized for the brilliant innovation that it is) are all great examples of innovation success. A great example of an innovation opportunity lost is Kodak, who invented digital photography in 1975, but whose leadership refused to see how disruptive it would become to their core business, despite their commissioning many good predictive foresight studies, from both internal strategy teams and external consultants. Digital photos were grainy and expensive in those early years, but Kodak could have spun out a team to use keep refining it from the beginning, and it could have found eager early clients with the intelligence, law enforcement, and defense communities. Those customers, and perhaps others, would have been happy with lower quality at first, for the many benefits that such transmissible digital photographs offered. Law enforcement could have used digital cameras integrated with Kodak fax machines, for mug shots and search requests via the National Crime Information Center (NCIC) which was created in 1967, almost twenty years before the web. Commercial applications could have inevitably come in the 1980s. This, and the PARC example of the personal computer, are two great lessons in the challenges of innovation, which is defined as a new product or service that becomes a commercial success. In digital photography, Kodak had a great invention (design, prototype) but wasn’t able to turn it into an innovation. While they had the foresight to see that it would get continually better, they didn’t have the culture or entrepreneurial processes to allow cannibalization of their core business. Clay Christiansen famously describes this problem in The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (1997/2013). We’ll revisit the innovator’s dilemma at several points in the Guide. We need all Eight Skills of adaptive foresight to get a firm past good strategic foresight (which Kodak had) into adaptive foresight, or successful action in the environment.

For a few practical tips for innovation success, see Kelly and Littman’s The Ten Faces of Innovation (2006), also by IDEO authors. This book explores the human and process resources that help leaders overcome internal resistance to creative solutions. They profile particular types of people, including the Anthropologist (always presenting data on consumer behavior and interests), the Cross-Pollinator (combining ideas and processes across silos), and the Hurdler (looking for creative ways around current blocks and constraints) who are vital to successful internal innovation teams. They also explore successful innovation examples from Kraft, Procter and Gamble, Safeway, and the Mayo Clinic. Neal Thornberry’s Innovation Judo (2014) also addresses the challenges of internal innovation. It explores the many ways internal actors may try to shoot down experiments that could threaten their own power or process, and the various “judo skills” internal innovators need to succeed. The team at Chrysler that created the Jeep Wrangler Rubicon in 2003 is a great case study of innovation success tactics in this book. 

4. Strategy Skill – Strategy & Planning Specialty Focus. Examples: Amazon, Apple, Google, Rick Rescorla, etc.

Strategy, or preference thinking, is aided primarily by two specialty practices in our model, analysis & decision support and strategy & planning.  In other words, strategy development requires analysis, prioritizing, decision-making, visioning, and planning. Strategic foresight occurs when a team, or a leader, motivates us to achieve a preferred future. To qualify as strategy, this future has to be something that would otherwise not happen in the world, without the leader or strategy team’s intent. Otherwise, it’s a result of a probable process (which we can try to anticipate) or of a possible process (a random outcome, a creative, unexpected result). These three futures, the Three Ps, really are the only fundamental types. Great strategic foresight always builds on Learning (understanding relevant past and present), Anticipation (uncovering probable futures), and Innovation (imagining possible futures), to craft a preferred future, and to give us some guidelines (plans, tactics) for how to get there.

Improving any strategy also requires repeated application of all of the other Eight Skills, in a continual Do loop. It is only when a strategy is Executed, and leaders attempt to use it to Influence their teams and stakeholders, Relate to them in the process, and Review its impact (the Four Action Skills), that we get to test any strategy against the world. Nineteenth century Prussian military commander Helmuth von Moltke famously said (in paraphrase) “No plan survives first contact with the enemy”. We can generalize this to “No strategic plan survives first contact with reality.” Continual iteration of strategy and action, via the Do loop, is the only way we adapt. With these caveats, let’s survey a few obviously excellent strategies (in hindsight) by some of the well-known business leaders in recent decades.

Consider Amazon’s foray into Web Services in 2006. With revenues of $17B a year, it created a platform to rival Google for cloud computing, and a perfect platform for its AI products and services. This move made perfect strategic sense for Amazon, yet wasn’t inevitable in any way. Consider Apple’s revitalization under Steve Jobs, with the iMacs, the GMacs, the iPod, and most recently, their foray into the iPhone in 2010. Note that the last two were executed as a fast follower (aka, second-mover), not a first mover in those product categories. Second mover successes often show the value of superior strategy and design. Wikipedia notes that Amazon was a second mover in online bookstores. Book Stacks Unlimited was started three years earlier, in 1992, and grew to 500K titles and 35 employees. But Amazon had a superior execution and marketing (influence) strategies, and rapidly overtook Book Stacks, which was sold to Barnes & Noble in the late 1990s.

Think also of Google’s decision to open up and give away the mobile operating system Android, a perfect counterstrategy against Apple’s iOS. As Gerald Nanninga notes, this also saved Google’s primary search engine business as global web traffic moved from desktops to phones. As Vivek Singh notes on Quora, think of Dell’s Assembled to Order strategy, which allowed them to beat the majors in personal computing at the time (IBM, Compact, HP) without carrying finished inventory. Or as an anonymous blogger notes at Cascade, a strategic planning software provider, think of Toyota’s strategy and culture of continuous improvement or kaizen, which allowed them to beat the US car majors in the 1970s, and then, after local tariffs were introduced, to bring that strategy and culture to Toyota manufacturing plants in the US beginning in 1986. Toyota’s Kentucky plant is their largest manufacturing facility in the world. See Jeff Liker’s The Toyota Way (2004) for a great overview of kaizen. See Scott Galloway’s excellent book, The Four: The Hidden DNA of Amazon, Apple, Facebook, and Google (2017) for examples of smart strategic management by four different 21C titans. See McAfee and Brynjolfsson’s excellent Machine, Platform, Crowd (2017) for a great overview of why all three of these should be central to your own business strategy. They are major new developmental trends (anticipations) that you can use well or poorly, depending on your strategy.

For our last strategy proof point in this section, consider the story of Rick Rescorla. It offers a heroic and riveting example of a leader successfully integrating all Four Foresight Skills, and devising a powerful strategy that was successfully implemented at the organizational level, in the process saving thousands of lives. It also offers us a lesson in how we can each see and prepare for both the probability and possibility of catastrophes, sometimes large and sometimes small, for ourselves, our teams, our organizations and our societies.

Rescorla was a decorated officer in the UK, Rhodesian, and US military, the latter as a distinguished Colonel in Vietnam. As Wikipedia notes, after returning to the US from Vietnam, he wrote and published a textbook on criminal justice, then left teaching for the higher pay and responsibility of corporate security. He started as a member of the security team at Dean Witter Reynolds at their World Trade Center offices in New York City in 1985. After the 1988 terrorist bombing of Pan Am Flight 103 over Lockerbie, he worried about the possibility of a terrorist attack on the World Trade Center. He asked a colleague from Rhodesia, Daniel Hill, who was trained in counterterrorism, to assess the WTC’s security for weak points. Hill noted the easy accessibility of load bearing columns in the WTC basement and said “I’d drive a truck full of explosives in here, walk out, and light it off.” Around this time, Rescorla and Hill wrote a letter to the Port Authority of NY and New Jersey, insisting on the need for more security in the WTC, including the basement. Unfortunately, Rescorla and Hill’s recommendations were ignored, and no security changes occurred.

But following the 1993 WTC truck bombing, in the basement of the building as he had anticipated, Rescorla gained new authority as head of security at Dean Witter. He then hired Hill and another expert, Fred McBee, as a security consultant, and built a team of diverse security professionals and stakeholders, informally dubbed “Team Rescorla” to advise the firm on security matters. Hill predicted that the first WTC attack was likely planned by a radical imam at a mosque in NY or New Jersey. Later that year, followers of imam Sheik Omar Abdel Rahman of Brooklyn were arrested for the bombing. As James Stewart outlines in a 2002 New Yorker article,  Rescorla, based on a flight simulator exercise done by Fred McBee, sketched a chilling scenario for Dean Witter management of what the next WTC attack might look like: an air-cargo plane loaded with explosives, chemical, radiological or biological weapons might be used by terrorists to crash into the towers. They said “the ground is secure, but the next attack may come from the air”.

When Dean Witter merged with Morgan Stanley in 1997, Rescola recommended to his superiors that Morgan Stanley leave the towers for New Jersey, to lower their security risk. But their lease ran to 2006, and a move was financially out of the question, so Rescola secured authority to devise an emergency evacuation plan for all the firm’s employees who occupied twenty-two floors in the South Tower, and four floors in a neighboring building as well. He also got new smoke extractors and lights in the stairwells. Finally, he instituted a series of fire drills to train all the firm’s employees in rapid evacuation procedures. As employees recall, these drills were implemented frequently enough, every three to six months, to be a significant annoyance to top management.

On the morning of September 11, 2001, Rescola heard the explosion in the North Tower of the WTC, and began immediately ordering all Morgan Stanley employees to head down the stairwells in pairs per the plan. He also chose to ignore the Port Authority’s announcement in the South Tower, over the P.A. system, urging people to stay at their desks, so as to not disturb the North Tower evacuation. He is credited with successfully evacuating most of the firm’s 2,687 employees from the towers and neighboring buildings prior to the collapse. He was last seen on the 10th floor of the South Tower, heading upward with his bullhorn, walkie-talkie, and cellphone. He was committed to stay in the tower until the last person under his care was safely out of the building, as he told his wife on his cellphone shortly before its collapse. Watch the History Channel’s The Man Who Predicted 9/11 (2005) (YouTube, 45 mins) for a brief documentary on this amazing story, and the skills, habits, and attitudes a good leader needs to generate strategic foresight.

Rescola’s story offers us a shining example of successfully integrating learning (bringing in diverse experts to deepen understanding of the threats), anticipation (successful prediction of major risks, more than once), innovation (taking personal responsibility to devise a creative solution, rather than relying on others) and strategy (devising, drilling, and executing effective security responses, within the strategic limits set by his superiors). Rescorla also demonstrates the exemplary ethics and empathy of a great leader as well. He offers us an astonishing example of the power of integrated foresight and action, and is a hero we will remember for life.

There is also a government-level security counterfactual we could add to this story as well. Imagine what might have happened if one of Rescorla associates, or someone in US domestic counterterrorism, had been privy to Team Rescorla’s plane scenario, and had recognized that a large plane full of jet fuel would be an even simpler and more likely weapon than one laden with explosives. Imagine also that someone had recognized that this threat could be substantially reduced by greater security on large commercial planes, and greater oversight of US commercial flight schools. El Al, the Israeli airline, has long been recognized by security professionals as a physical and process security leader, having had only one actual hijacking since its inception in 1948, despite countless attempts. At that time, US airline security did not incorporate many of El Al’s innovations, including unbreachable cockpit doors. Finally, imagine that someone had sufficiently raised the public heat regarding this security threat, by publishing this scenario, with recommended solutions, in open journals, magazines, or newspapers, or including the plot in a dramatic film.

There’s a great saying about change, attributed to 40th US President Ronald Reagan: “When you can’t make them see the light, make them feel the heat.” This can be paraphrased as: “Some folks change when they see the light, but most folks change when they feel the heat.” Some of us are convinced by what Dan Kahneman in Thinking, Fast and Slow (2013) calls System 1 thinking (logic, rational argument, evidence) but most of us need System 2 appeals (intuition and emotion, including fear) to motivate us to change. Sometimes it takes a public recognition of a threat, in an easily digestible medium, such as a widely seen article or even a film, to create the heat that will cause a large institution, like the US government, to execute the strategy solutions we think will lead us to better futures.

As a proof point for this last claim, recall the public pressure to continue progress in nuclear disarmament talks that accompanied the ABC television debut of the chilling nuclear holocaust film, The Day After (1983). This film garnered 100 million viewers on its first showing, and was credited by policymakers, including then-President Reagan, with generating pressure on the US administration to execute the Intermediate-Range Nuclear Forces Treaty of 1987, one of the great steps forward in nuclear disarmament during the Cold War. The film was also shown on Soviet television in 1987, where it was intended to have a similar public effect. This outcome seems particularly impressive, given Reagan’s extensive efforts to bankrupt the Soviet economy, beginning in 1981, by greatly ramping up US military and intelligence investments and activities, sanctioning anticommunist insurgencies, and greatly increasing anti-Soviet rhetoric. As Downing’s 1983 (2018) describes, Reagan’s policies led us to a second peak of Cold War tensions, as dangerous a time as the Cuban Missile Crisis of 1962, though the events are far less widely known. The events of that year, and the public reaction to this film, both made Reagan into a nuclear disarmament champion. Read Reagan’s Secret War (2009) for a declassified account of his deep desire to eliminate nuclear weapons as both a threat and an investment, while also ending the Soviet Union, and how he managed the conflict between those two goals.

This counterfactual demonstrates that there are many levels at which we can each execute strategic foresight and leadership. We control our own foresight activities the most, but we also have some responsibility for the foresight of our teams, our organizations, and our society. No one can do or foresee everything, but we can each do something to advance our collective foresight abilities.