Economic intelligence: making decisions and planning ahead in times of uncertainty
Given that uncertainty is now systemic in our societies and shapes the very framework of decision-making, we believe it is crucial for decision-makers to develop their ability to make decisions and think ahead. This policy paper examines the role of economic intelligence, which involves gathering, analysing, protecting and exploiting strategic information to enhance an organisation’s competitiveness, security and influence. However, given this new environment marked by uncertainty, the conventional approach to economic intelligence is proving restricted. This policy paper thus aims to highlight the limitations of the conventional economic intelligence model and the need to adapt it to contemporary issues. It involves clarifying the shift from economic intelligence based on information-gathering to economic intelligence focused on strategic decision-making in times of uncertainty.
What is economic intelligence?
The role of economic intelligence is to collect, analyse, protect and utilise strategic information to enhance an organisation’s competitiveness, security and capacity for influence. Economic intelligence is a holistic approach that supports decision-making, engagement and the execution of an organisation’s activities in a competitive environment. The traditional model of economic intelligenceis based on monitoring, information protection and influence. Strategic monitoring consists of active and continuous surveillance of the business environment, encompassing its technological, competitive and regulatory dimensions. It draws on a variety of sources to detect weak signals and anticipate market developments. Information protection aims to safeguard the company’s strategic assets against internal and external threats. It covers a range of issues such as cybersecurity, the protection of intellectual property and the prevention of industrial espionage. Influence strategies involve shaping the company’s environment by actively participating in legislative, regulatory and public decision-making processes.
Why is the traditional model of economic intelligence reaching its limits in a changing world?
Today we are witnessing a transformation of the international order that emerged in the aftermath of the Second World War, which was founded on multilateralism and the rule of law to govern relations between states. Indeed, the 21st century began with the emergence of several crises (the 2008 economic crisis, terrorism, the wars in Ukraine and Iran, etc.), the effects of which have left a lasting mark on Western societies. Added to these crises is the political upheaval brought about by Donald Trump’s rise to power in 2016, which accelerated America’s withdrawal from Europe in order to focus on domestic issues and counter China. Alongside these geopolitical tensions, environmental, technological and societal changes are intensifying and disrupting the established balance. The global Covid-19 pandemic has also put societies to the test with new challenges relating to health, psychology and the organisation of work. Furthermore, the early 2020s have been marked by the advent of Artificial Intelligence (AI), which is transforming relationships in the workplace and in the realm of knowledge. Competition for natural resources, the worsening of inequalities due to rising raw material costs, and the decline in trust in institutions are further factors illustrating the transformation of our era. Thus, all these upheavals reveal a new reality: the omnipresence of uncertainty.
Consequently, economic intelligence needs to be rethought in the digital and AI era. Faced with accelerating technological change, the advent of AI and geopolitical instability, the traditional approach to economic intelligence is showing its limitations. This policy paper aims to highlight these limitations of the traditional economic intelligence model and the need to adapt it to contemporary challenges. The aim is to clarify the shift from economic intelligence focused on information gathering to economic intelligence focused on strategic steering in a context of uncertainty.
From information management to decision-making under uncertainty
In an environment characterised by uncertainty that has become structural, the challenge for organisations is no longer simply to gather more information, but to generate actionable insights that can inform decision-making. Consequently, strategic intelligence systems must evolve towards approaches capable of combining the detection of weak signals, the contextualisation of data and strategic foresight.
AI as a driver of enhanced economic intelligence
The advent of AI, by enabling organisations to harness the vast amount of available information, broadens and deepens the analytical capabilities needed to understand, anticipate and make decisions in a VUCA world (volatile, uncertain, complex and ambiguous). By automating data collection, pattern detection and trend forecasting, AI enables faster and more informed decision-making. Companies that use it effectively do not merely react more quickly; they also anticipate developments more accurately.
Indisciplinarity as a response to uncertainty
Indisciplinarity could represent a complementary approach to rethinking an economic intelligence capable of navigating periods of uncertainty. Unlike interdisciplinarity, which combines the findings of several disciplines, indisciplinarity examines a subject from the perspective of several disciplines used simultaneously, by cross-referencing their viewpoints. It stands out for its ability to challenge dogmas, as well as its refusal to confine the study of a phenomenon to a single discipline or methodology that would fail to capture its complexity. Indisciplinarity rejects intellectual compartmentalisation at a time when issues transcend disciplinary boundaries. Thus, it aims to break free from the constraints that a discipline might impose on creativity and, ultimately, the production of knowledge. This approach appears relevant in the face of contemporary challenges which, in globalised societies, are by their very nature transdisciplinary and transnational.
The 6 recommendations from the policy paper for decision-makers
1. Rethinking economic intelligence as a capacity for strategic foresight
Economic intelligence should no longer be limited to monitoring the environment. It should develop a forward-looking capability that makes it possible to detect weak signals, structure available information and inform decision-making in uncertain environments. This evolution requires moving beyond conventional monitoring towards a dynamic understanding of the strategic environment.
2. Developing economic intelligence augmented by artificial intelligence
Organisations should use AI to analyse diverse big data, identify complex correlations and detect potential disruptions at an earlier stage. The issue is not to delegate decision-making to the machine, but to use AI as a tool to aid interpretation and direct human attention towards the most relevant information.
3. Building strategic scenarios and turning them into options for action
In the face of uncertainty, economic intelligence should enable us to explore several possible futures rather than seeking to predict a single outcome. These scenarios should be accompanied by concrete courses of action that can be implemented swiftly in a crisis, technological upheaval, geopolitical shock or disruption to value chains.
4. Establishing short decision-making loops between monitoring, analysis and action
The effectiveness of economic intelligence depends on its ability to quickly turn information into decisions. Organisations could thus shorten the lines of communication between EI teams, strategic departments and operational functions in order to test hypotheses, refine decisions and adapt actions on an ongoing basis.
5. Establishing cross-functional and multidisciplinary governance for economic intelligence
Economic intelligence should be organised as a cross-functional role, bringing together experts, decision-makers, operational staff and professionals from a wide range of disciplines. This governance would help to minimise the silo effect, bring different perspectives together and interpret weak signals more effectively. It could draw on a network of EI representatives to ensure that forward planning is closely aligned with realities on the ground.
6. Boosting organisations’ resilience and agility in the face of disruption
Organisations could incorporate the lessons learned from economic intelligence into their operational adaptability. This would involve identifying critical dependencies, diversifying the available options and introducing mechanisms for rapid repositioning. Resilience should no longer be seen as a mere reaction to crises, but as a proactive ability to adapt in the face of uncertainty.
