Competitive advantage goes to those who best deduce the forces acting on their industry, and who best adapt to profit from them.

The earlier and the better managers anticipate future circumstances, the better they can position themselves to benefit from them, or influence them, and so increase their chances of survival and success. The better their view of the future, the better their decisions will turn out to be.

While “leadership” is often associated with motivating staff and streamlining organisational effectiveness, in a changing world it involves far more. Leadership success means adeptly riding changing market, technology, and regulatory conditions to renew the fit between technology, markets, products and customers. Anticipating and competitively interpreting new opportunities and setting appropriate direction is the key competitive skill business leaders bring to their position.

Responding to uncertainty
All effort spent preparing for a future that will not emerge is a waste of personal or organisational resources. So decision makers seek to narrow down what they must adapt to and plan for. Therefore they seek the analyses of those who peer ahead. They look to industry and technical experts who forecast change. In short, they acquire, buy, read, listen to and assimilate forecasts for their industry and the world at large.

This is compounded by the rapid growth and specialisation of knowledge. The days of the panoptic amateur intellect are over. There’s just too much to know in too many specialised fields. Being forced to build our picture of the world on the expertise of others, we are, whether we like it or not, retail consumers of others’ forecast perceptions. This appetite for prediction is willingly fulfilled many times over by an industry of prognosticators, forecasters, futurists, economists, sociologists, journalists, financial advisers, market researchers, defence and security planners, technology gurus, and every conceivable form of consultant and analyst… all falling over themselves to predict technologies, innovations, and legislative, social, and market change.

So predictive statements are all around us: in the newspapers, on TV, at conference presentations, in industry reports, consulting documents, think tank studies, and so on. Sometimes this is called industry foresight or future studies. Mostly it goes under more conservative labels such as economic analysis, market research, technology assessment, or competitive intelligence. Management consulting firms, including the “Big 6,” trade on their reading of the future, as do fund managers and investment advisers. The world of the average manager is full of people telling him or her what’s coming next.

A poor track record
Unfortunately, our competitive need to anticipate the future is matched only by our lamentable inability to forecast it. Beyond the immediate short term, where variables are more or less locked in, the track record of all predictive methodologies is poor – the list of wrong forecasts and laughable errors is a mile long.

There are many reasons for this. In my book, Future Savvy (Amacom Press, 2009), I recount the famous parable about a conman who promised a local nobleman he could teach a donkey to talk – for a large fee – but it would take 20 years. Of course, in 20 years the conman, the nobleman, or the donkey would be dead.

Predicting is safe for the same spurious reason. By the time outcomes emerge, there is almost never anybody around to say, “Hey, that never happened!” Not only is there no recourse, but putting predictions out into the world is ridiculously easy to do. There are no accepted conceptual frameworks, accepted methods, agreed professional standards, or guidelines for application to policy or business decision making. There is no oversight board or council or licensing mechanism, no organisation to which one must belong, no minimum qualifications, no agreed or standard curriculum in teaching forecasting.

The sobering reality is that even the best “neutral” foresight work in the best institutions also often turns out quite wrong. Even where future analysts work competently and diligently, with balanced intention, the extreme complexity of human and natural systems makes mediumand long-range views of the future extremely hazardous.

The problem, therefore, is a subtle one. While foresight is a crucial decision-success resource for managers, most forecasts are unreliable. It is not that predictions are always poor, but the field as a whole is always uneven. Some forecasts will be useful. Some will be junk.

Some will be actively trying to influence future outcomes in their own interest. We cannot rely on them, but it is fatal to ignore them.

A new management skill
So something new is necessary: a new skill in the manager’s armoury. Forecasts become valuable only alongside a clear way to separate the wheat from the chaff, recognising and discounting exaggerations, conflicts of interest, failed assumptions, technological over-enthusiasm, spin and salesmanship, and mechanistic modelling and a host of other problems that forecasts are subject to.

With this skill, decision makers of all types could critically interact with the barrage of forecasts that compete for their attention and resources and discriminate between worthy and unworthy ones.

They would be able to ask of every future-oriented claim: which parts of it are worth integrating into my mental framework? Which parts should be part of my organisation’s preparation and planning and which can be discounted and safely ignored? Can I use this knowledge to further the goals of my institution? Can I base a decision on this with confidence?

There are, in fact, a number of clear quality filters, questions, and hurdles through which one can put a forecast. The purpose of Future Savvy was to put them all in one place. The following is a guide to the key considerations in doing this:

10 FORECAST PROBES TO KEEP HANDY

1. Purpose

Ask: What is the purpose of the forecast? What can be gleaned about why it exists, who put it out, or what was the intention of the forecaster? All forecasting is done for benefit. By recognising the interests at work behind a forecast, assessing what benefit or benefits are sought by the forecaster or whoever commissioned it, or what effect or concerns it is trying to arouse, one can make a better judgment as to potential strengths and weaknesses.

2. Specificity

Ask: Is there too much certainty? In short-term situations, or closed systems with few variables, the attempt to pinpoint outcomes is reasonable. But the forecast consumer should consider claims to medium- and long-term accuracy with acute scepticism.

Ask: Is there enough certainty? Whether predictive or speculative, good forecasts are compelling in their detail. They give the reader a concrete sense of how a new reality would look and feel and specify a full and reasoned path to the future. The speculative forecast may allow for a number of alternative outcomes, but will detail each one.

Ask: Is the forecast clear about the pace of change? Does it specify timelines or does it leave the question hazy? Tackling the problem of timing forces forecasters to think very carefully through the drivers and blockers of change, and particularly to clarify their argument about why and how blockers will be overcome.

3. Information quality

Ask: How extensive and how good is the base data? Data is never as solid as it seems. The difficulties in getting numbers that are an accurate reflection of the world – which bedevils studies of the present as well as the future – are immense. Among these are problems in validity of definitions, validity of sampling, how research is skewed by the form of the research or particular questions asked, or by how resulting information is judged and collated.

Particular questions to ask of data in forecasts are:

• Is the data up to date?
• Does the forecast use secondary data? It is very common that “big-picture” future views use data created by others, often bringing together facts and numbers from many sources into one study. This risks disconnection with context and statistical caveats that frame the primary research.
• Is the data real or a projection? Sometimes data given is not real recorded figures but “future” data points that have been projected from past data, which raises obvious questions about how this projection has been done.

4. Interpretation and bias

There is no such thing as a perfectly neutral or objective view of the future. We should expect natural bias, and distinguish it from intentional bias, where forecasters are misrepresenting likely outcomes.

Ask: Are bias-prone contexts at hand? Particularly:

• Is the forecast sponsored? Who paid for it and why?
• Is self-interest prominent? If it is, bias is likely. (Self-interest is often subtly at work when the forecasting organisation stands to benefit from funds allocated to address a future problem that it itself forecasts.)
• Are ideology and idealism prominent? Does the forecast focus on a “single issue” future? Where a forecast prioritises a single issue as dominating the future — bioterror, renewable energy, or slowing human aging, for example — chances are the forecaster is compromising the truth or reasonable analysis to propose this extreme view.
• Is editorial oversight bypassed? The editorial process generally limits or verifies extreme views before they are pushed out into the world. Where editorial oversight is lacking, for example, in the many self-publishing options on the internet, the forecast escapes peer review.

5. Methods and models

Ask: Does the forecast specify its methods? A forecast takes us from present conditions to future outcomes: in every case there is a method for getting from the present to the future, even if that “method” is pure intuition. A good forecast will state its primary methods, including its limitations and biases. The author will show his or her working, revealing a train of logic that one can follow and agree with, or not. Poor forecasters will be unaware of their primary method or unable or unwilling to state it.

This does not imply that highly methodological forecasts are better. Often formal methods give the illusion of process when it is not there. A forecast can be overloaded with method and short on basic insight and common sense.

6. Quantitative limits

Ask: Is the use of quantitative methods appropriate? When presented with a
forecast based on quantitative processes, the forecast consumer should ask, “Is this a valid domain for quantitative analysis?” Quantitative forecasting works well in low-uncertainty, relatively closed, short-term forecast situations, where it is reasonable to say that key assumptions will hold during the forecast period. For other forecasts it is inappropriate. No matter how pretty the stats or how powerful the computer, one mistaken assumption will still send the analysis barrelling down the wrong path.

7. Managing complexity

Ask: Does the forecast oversimplify the world? Most situations in the world have many if not infinite input variables. Poor forecasters determine the simple progression of one issue while assuming the rest of the world stands still. Good forecasts distil this complexity and narrow the uncertainty, but don’t oversimplify the irreducible uncertainty of multifactorial situations.

Ask: Does the forecast anticipate things that could speed up the future, or push it off track? Does it account for triggers and tipping points? A good forecast will not assume a steady evolution from the present or a constant rate of change. It will see the effect of critical mass and be ready for reinforcing or balancing thresholds crossed, leading to rapid acceleration or deceleration of change or a sharp change the direction of an established trend.

Ask: Does the forecast expect exponential change? Rapid change is all around us, but change is exponential only under very specific, unique circumstances, and never dependably so.

8. Assumptions and paradigm paralysis

Every forecaster makes assumptions, for example about society, technology, human nature, legislative developments, etc. Assumptions are the basis on which present conditions are turned into a view of the future.

Ask: Is the forecaster clearly aware of his or her own assumptions or willing to entertain alternative assumptions? Better work will realise, acknowledge, and specify its assumptions, and argue for their pertinence. It will be clear about how its assumptions lead to its forecasts, and how other assumptions may lead to alternative outcomes. If no assumptions are identified, it is likely that the forecaster is assuming that existing trends will evolve at their current rate, which is a poor assumption.

Ask: Do the forecaster’s assumptions appear valid and reasonable? A forecast is only as good as its assumptions. If the assumptions fail, the forecast will fail, no matter what model or technique is used.

9. Zeitgeist and groupthink

A poor forecast will, consciously or unconsciously, assume that currently dominant perceptions, needs, wants, concerns, and aspirations will be still dominant in the future – it will be held hostage by the current zeitgeist. History shows that the issues framing the present and recent past will inexorably evaporate, to be replaced by others, and a good forecast will anticipate these shifts even if it can’t see them.

Ask: Is the forecast jumping on the bandwagon? Consensus-based forecasts are particularly vulnerable to a bandwagon effect, or “groupthink”. A good forecast will not easily be sucked into the prevailing wisdom and will question general consensus before buying into it.

10. Drivers and blockers

Whether future change occurs depends on the outcome of the “power struggle” between the forces in favour of change – drivers and enablers, on one side and friction and blockers on the other.

Ask: Are change drivers and enablers identified or are trends simply projected? A poor forecast will take a trend at face value, assuming its continued evolution. A better forecast will determine what forces are driving the trend, and how durable or vulnerable these are.

Ask: Are blocking forces identified and fully accounted for? Is friction factored in? A good forecast will assess the strength of resistance to change and anticipate specifically if and how this resistance will be overcome, if indeed it will be, and account for the resources required to achieve this.

Ask: Have utility questions been asked and adequately answered? No matter how stunning an innovation or how unprecedented a technology; it will only emerge into the world at large if it favourably improves consumers’ cost-benefit equation (including the cost of overcoming legacy systems and of new necessary complements).

Ask: Does the forecast challenge social, cultural, or moral norms? A change will proceed slowly, if at all, if it goes against prevailing cultural norms and values.

Ask: Whose side is the law on? The law is almost always, by design, a drag on the future. Sometimes a law or a legal principle is a direct blocker of change, for example where an innovation is possible but may be blocked by patent protection or the fear of liability suits.

Ask: Is the forecaster in love with the technology? Technology is being developed all the time. It very rarely gets to market, let alone broad adoption and commercial success. Technophiles have a history of seeing big change when none is likely and rapid change when slow is likely.