Common Challenges in Traditional Foresight Work

When we were planning to start Futures Platform, there were several challenges we thought a good platform should address. We saw an increasing need for strategic foresight in a more volatile, uncertain, complex and ambiguous world, and we saw that the available solutions did not quite satisfy the need of most individuals, teams, and companies who needed to stay on top of these changes. In this article, we go through three of several challenges we sought to resolve, and we provide examples of how our tool addresses them.

1. Excessive Focus on “Growing” Trends, Missing Out on Weakening Trends

The first challenge in traditional foresight knowledge production is its heavy focus on “growing” or “strengthening” trends or phenomena, and its slight dismissal of “weakening” or “decreasing” trends or phenomena. Yet, to understand change and conduct proper foresight, the knowledge and understanding of those things which are prominent today but will disappear in the future is equally important.

One way to deal with this is by creating a system whereby different trends or phenomena are assigned “strengthening” or “weakening” markers. For instance, in our own platform, we use the green colour to represent strengthening phenomena, while blue represents weakening phenomena. Oftentimes, these can be two sides of the same coin. For instance, with the emergence of the “platform economy,” several new business models and other trends have come about; yet, other parts of the economy are also decreasing and, who knows, even disappearing—take, for instance, traditional forms of journalism.

Hence, one can avoid focusing too much on strengthening trends and improve the outcome of their foresight work by making a conscious effort to balance out their foresight and see the “two sides of the coin”. There are not only growing trends, but also weakening, “too-early-to-tell” and “wild cards.” It is important, therefore to keep this in mind, and give different weights to different trends.

2. Doing Away with Bias in Foresight Work

The second challenge can be thought of as a bias challenge.

Firstly, there is often an uneven emphasis on subjective, randomly-discovered and intuitively-assessed weak signals. More importantly, this is too often done without an attempt to link those loose observations with one another, which results in a non-comprehensive view of changes and trends. Hence, single observations, weak signals and case examples should be treated as real evidence of change only if there are enough other insights supporting that same conclusion.

Furthermore, another pitfall is the reliance on value rationality, which can also be called ideological or proactive visioning. This is when a person, be it a leader or a professional, would want to see a world without, say, industrial food processing, or genetically modified organisms, or nuclear energy. Given his ideology, he might proactively attempt to convince his audience, and himself, of the probability, or even necessity, of such a future, falling prey to selection bias. This is, of course, something that all fields of research, or professions, should attempt to minimize; yet, it is a real challenge, especially in a field where the outcome of the research is not a given, like in foresight.

These challenges, regarding the objectivity of the research, are harder to tackle, as there is always some amount of unconscious subjectivity in all we select to present or write. Nevertheless, there are some techniques that work to improve this. For instance, with regards to overvaluing weak signals, in our own platform we are careful to cluster together different phenomena or trends, linking them to megatrends, bigger themes, and different sectors (such as technology or economy), thus making sense of weak signals, seeing supporting and contradictory evidence, and understanding them as part of a larger phenomenon.

As for the objectivity of the content, including third parties is key. For example, in developing content for Futures Platform, our team goes through an internal peer-review process to make sure our content is up to the standards we have committed to. We also have an international content advisory board, which provides useful feedback on the material and presentations, and lends its expertise when needed. Finally, as the users should have the final say, we allow them to modify our content and supplement it with their own material, should they feel it does not accurately represent their vision of the future.

3. Avoiding a Static Vision of the Future and Trends

A third challenge occurs when we are asked to analyse how a trend changes in time. Too often, it is hard to assess this analytically, step by step, as trends or phenomenon are thought of as static things “to come at some point” or “growing, but without changing.” We know, for instance, that transportation is moving towards automation—but how do we assess this?

The proper way would be to look at how the overall transportation industry (and the traffic system along with it) are changing in the short-term, mid-term, and long-term. This is much better than saying, “in 25 years all transit will be automated.” Maybe that’s true, but it brings little value when compared to a broken-down analysis of the trend, its different stages and, with them, the different opportunities and threats observable.

This challenge can be tackled by utilizing a “theme” structure. For instance, at Futures Platform we have recently established 30 large themes, such as “Virtual Reality/Augmented Reality,” “Energy,” and “Sustainability,” which are broad topics and cluster together different phenomena or trends. For example, the theme “Sustainability” covers in its description some more specific trends like “Clean Tech” and “Circular Economy”. Every single one of these themes is divided into short-term, mid-term, and long-term. So, our “Transportation” theme, for instance, would have different time perspectives to it, affected by different phenomena or trends, such as the growth in ride-sharing apps in the near future or the sole use of clean energy to power them in the more distant.

Short-term macro-phenomena or trends describe how a certain theme is evolving prior to, say, 2020. This is done with both a description of the theme and with linkages to several trends and phenomena that correspond to that theme. A mid-term macro-phenomena, therefore, would describe the theme at a later date (for example, 2025), linked with how trends might have evolved up to that stage.

For us and our users, this allows for radar to be built around these themes and macro-phenomena, providing a great overview of how the future is developing. If users want to stay at a higher operational level, they can focus just on macro-phenomena. This way, it’s possible to prioritise, comment, assess and operate with larger clusters of phenomena and trends instead of individual micro-phenomena. But of course, users can also delve deeper into the network of micro-phenomena under each macro, and use the micro-trends or micro-phenomena exclusively if preferred.

Using a “theme” structure, where themes are larger clusters of micro-trends and micro-phenomena that are divided into short-term, mid-term, and long-term macro-phenomena, users can not only make sense of a moving and ever-changing future, but also resolve some of the other challenges, as the connections and links between phenomena must provide evidence for any single trend, thus reinforcing objectivity.

So there they are. They are focusing on both strengthening and weakening trends, removing some common biases, and understanding how trends and phenomena change with time and in relation to each other, all help in dealing with some of the common challenges in foresight work.

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