Prologue

“The world is full of obvious things which nobody by any chance ever observes.”

Sherlock Holmes (Conan Doyle 2019, 34)

In 2008, Philip Morris, one of the world’s leading tobacco companies, started their research in tobacco heating systems, replacing cigarettes with smoke-free products. Although the new technology would not reduce addiction to nicotine, it promised to eliminate the majority of diseases caused by burning tobacco. In 2016 Philip Morris announced its new purpose to end cigarette sales within 10 to 15 years in many countries. Since then, the company has fully aligned its employees with this purpose and shifted its organizational focus and resources to smoke-free alternatives. What has caused such an unprecedented disruption of a commercially successful company with a global workforce of 70,000? Was it pressure from civil society, prohibitions from regulatory authorities, declining sales due to increased competition or directives from a new CEO? Was Philip Morris forced to change by strong impact from the outside world, or were they lead by “weak signals” originating from reflections about the future of their ecosystem? We will never know, as Philip Morris’ beliefs and decision-making processes are not accessible to the outside world due to the autopoietic nature of the company – a concept to be discussed later the book. Nevertheless, the direction and the speed of the change process leads to the conclusion that weak signals have certainly played a decisive role.

What are the characteristics of “weak signals” in a leadership context? The term was first introduced in the context of strategic management by Igor Ansoff (1975), but the underlying concept barely received the attention it deserved in the scientific community and in managerial practice. Therefore, in view of the lack of a generally accepted definition and body of knowledge, it proves to be appropriate to start the investigation by conveying concrete examples from Philip Morris. At the turn of the century, the company environment (its “ecosystem”) started to change fundamentally, and still existing trends were dramatically reinforced. Philip Morris had to shift its modus operandi from running the business to changing the business, provoked by

• a degenerating image of smoking in society

• smoking bans in hotels, sport arenas, and public transportation

• an increasing speed of legislation on smoking

• rising health consciousness of the population

• a growing aversion towards Philip Morris as large and harmful company

• a progressively poor image of Philip Morris as employer

• new technologies for inhaling purposes

• new marketing approaches (“Apple stores”)

• new opportunities for diversification

At the time, the emergence of these developments and their sequence of entry was not immediately apparent. The first indications of change – or “weak signals” – were small, subtle shifts in areas such as Philip Morris’ hiring process, which suggested a decline in its reputation as an employer, as well as changes in the focus of scientific research towards new inhalation technologies, and new marketing approaches aimed at replacing traditional advertising methods. In order to become a pioneer in the tobacco industry, Philip Morris needed to interpret the underlying patterns behind these changes early on. Despite the uncertainties, the company had to develop new strategies based on “small data” as there was no time to wait for more evidence.

At Philip Morris, just as in most other companies, a primary task of executives is to detect fundamental change at an early stage and to cope with it proactively. In times of complexity and uncertainty, three basic concepts could lead the way to meet the challenge: Serendipity, Big Data and Weak Signals. Serendipity is the art of gaining crucial insights by chance while searching for other topics. Even though significant discoveries are often made this way, leadership cannot rely on this approach, as it is too arbitrary to initiate proactive moves. Big Data on the other hand is emerging as a prominent managerial concept. Executives have always trusted in numbers and data: the more comprehensive and accurate they are, the better. It is therefore not surprising that Big Data is highly welcome as a promising way to cope with the complexity of managerial practice. By sophisticated computation of enormous data sets, Big Data claims to identify patterns of human behavior that have a major influence on the corporate growth path. This pledge opens completely new horizons, but caution is still advised when applying the “magic of advanced statistics.” Big Data provides excellent results when exploiting business processes or improving their efficiency – it is perfectly suited for “running the business.” The current state of technological progress in the operational domain is astonishing, there is no doubt that new algorithms will become more sophisticated to enhance these processes.

In the realm of strategic management and innovation operations, a greater focus on Small Data – or more accurately, Weak Signals – is becoming increasingly necessary. When organizations dismiss such data as “not big enough,” they risk missing out on potential business opportunities or innovative ideas, as well as overlooking potential dangers. While Deep Learning Networks and Large Language Models are making significant progress, the concept of Leading by Weak Signals remains important. Identifying weak signals and their underlying patterns is not solely dependent on human ingenuity or machine intelligence, but on the ability to distinguish them from noise and take proactive measures to stay ahead of the competition.

The purpose of this book is to provide leaders with a framework to master the emerging shift to small data. Based on a novel understanding of corporate intelligence, Leading by Weak Signals strives for a holistic interpretation of “what is going on here” – the only meaningful question in the face of radical uncertainty (Kay and King 2020). As shown in Figure P..1, it amplifies weak signals to become strong insights which lead the way to proactive measures. The resulting intervention aims at maximum impact with minimal invasion, and it develops solutions to tackle future surprises proactively.

Figure 1: Leading by Weak Signals – a closed loop.

This book presents a framework and a step-by-step methodology to unleash the power of weak signals for leadership in times of complexity and uncertainty:

• In the perception phase, weak signals are the tiniest and earliest indications of change, when there is no evidence yet about its origin and its background pattern. They can be data or stories that are just surprising or inconsistent with the present state of knowledge, whereas after the fact the context often seems obvious (Smith 2020, 71) During the intervention phase, leadership activities should be guided by the principle of weak signals, focusing on exploiting the self-organizational forces of the system rather than direct intervention.

• Leadership has many connotations, in academic circles as well as in managerial practice. For the authors, the purpose of leadership is to ensure the viability of enterprises, by co-evolving sustainably with the demands of their stakeholders and ecosystems. Leadership is interpretated in an inclusive way, as a dynamic constellation between people, but it can also be attributed to individual executives.

• Leading by Weak Signals aims to discover and interpret change at the earliest stage, proactively and inclusively managing its complexity with the goal of ensuring and evolving an enterprise’s viability.

The methodology of Leading by Weak Signals comprises eight steps, each characterized by a keyword and dedicated to answering a question of fundamental importance for managerial practice:

1. “System” How to identify an enterprise in its overarching ecosystem?

2. “Edges” How to look for weak signals at the system’s periphery?

3. “Patterns” How to detect regularities and their generating forces

   (“power laws”) behind the weak signals?

1. “Evolution” How to develop scenarios of pattern dynamics to explore

   potential paths into the future?

1. “Indicators” How to develop performance measures and supporting narratives to enhance and monitor the evolutionary path?

2. “Framing” How to create self-organizing viable systems to support

   proactive intervention?

   ...