It is comforting to believe that every problem has a clear, contained solution. For decades, this belief has underwritten how decisions and policies are designed. By treating problems as discrete, solvable and predictable, this traditional problem-solving model provided reliable frameworks to break down complex issues  into smaller parts, collect data, and think of optimal solutions. This model thrived in environments where cause and effect were relatively stable and predictable. Its success is evident in areas such as public health management, where vaccination drives or sanitation reforms delivered measurable improvements.  Success has also been seen in regulatory decisions like the legalisation of ride-sharing or assisted dying laws, where outcomes could be evaluated at scale.  In these cases, problems were sufficiently bounded, and the systems they operated in behaved in ways that made intervention both effective and efficient. 

The trouble however begins when problems stop being contained. Linear approaches, such as the root-cause analysis , assume that addressing one component improves the whole. But in more complex systems, interventions can displace pressures or generate unintended consequences elsewhere. They also tend to optimise a solution  within existing systems rather than question whether those systems are fit for purpose. For instance, efforts to improve urban transport have often focussed on reducing congestion through better traffic management or road expansion. However, research from the OECD shows that such measures can unintentionally encourage more car use over time, ultimately recreating or even exacerbating the very congestion they aimed to solve. The issue here isn’t just inefficiency. It is also that the underlying system dynamics are being overlooked.

But the conditions that made this model reliable are eroding. We are increasingly seeing how individual shocks cascade across domains: climate extremes disrupt harvests which spike global food prices, and drive displacement. Pandemics spread through global networks, rapidly crippling supply chains, labor markets, and entire economies. These challenges are not unfamiliar, but they are unfolding differently. They are deeply interconnected, faster-moving and far-reaching in their effects.  These challenges are also mutually reinforcing. They are not isolated disruptions but are part of a larger, entangled reality  often described as a polycrisis,  where multiple crises interact and amplify one another. Despite this shift, much of the problem-solving toolkit today still reflects the linear logic of the past - one that assumes that challenges could be broken down into manageable parts and addressed in isolation. 

Climate change offers the clearest illustration of this limitation. It does not behave like a problem that can be addressed in a predictable and mechanical way. Rather, climate systems are characterised by non-linear feedback loops and tipping points, where small events can trigger abrupt, irreversible, and disproportionately large events. Small changes, like a slight rise in temperature, can trigger cascading effects across agriculture, water systems,  and livelihoods. Actions in one domain can undermine progress in another: efforts to secure energy supply may conflict with environmental goals, while biodiversity protection can introduce economic trade-offs. Research shows that frameworks that attempt to find an ‘optimal’ policy have systematically underestimated damage by excluding categories like migration and biodiversity loss, as well as long-term environmental impacts.

What makes this especially challenging is not just the science, but the context in which decisions are made. Even when evidence is strong, responses are shaped by competing economic interests, political trade-offs, and institutional constraints. As a result, action tends to lag behind what is needed - not because solutions don’t exist, but because the existing decision-making systems struggle to address problems that do not fit within their established frameworks. 

This is precisely where a different approach becomes necessary - one that moves beyond reaction and instead anticipates how interconnected challenges may evolve. In other words, a case for foresight. 

Thinking in Futures 

If traditional problem-solving focuses on fixing what’s broken, foresight expands the emphasis upstream. It also asks: what might break next, and how can we prepare for it? 

Foresight can be understood as a structured and systematic method of inquiry that explores possible futures in order to inform present-day decisions. Rather than attempting to predict a single outcome or control what lies ahead, it examines a range of plausible scenarios and considers how current choices may unfold across them. In this sense, it is less about forecasting and more about shaping the conditions under which the future emerges. 

The idea isn’t new. Even though traditional public policy often succumbed to the temptations of immediacy, the instinct to think ahead had surfaced. After World War II, as the world grappled with uncertainty on an unprecedented scale, researchers began developing structured ways, such as systems approach, to think about the future. Early efforts, particularly in the U.S., think tanks used tools like scenario planning and probabilistic modeling to anticipate geopolitical risks and the role of science and technology in development of a country’s capabilities. The goal was not to predict a single outcome, but to understand a range of possibilities and to be prepared.

By the 1960s and 70s, this approach expanded beyond military strategy. Scholars and practitioners began applying foresight to global challenges such as hunger, inequality, environmental degradation. The founding of the Club of Rome in 1968 expanded the focus of foresight to understand the “global problematique”: a cluster of interrelated world problems that concern the survival of humanity and could only be addressed holistically. The future was no longer just a strategic concern, but  it became a shared human question.

This was followed by a shift. By the 1980s and 90s, it became clear that the world was not just uncertain but complex in ways that defied control. Insights from systems thinking, ecology, and the humanities reshaped foresight. The ambition changed. The emphasis moved away from controlling outcomes toward understanding interdependencies, identifying blind spots, and expanding the range of possible responses. 

Foresight also introduced a normative dimension by asking deeper questions about not just what could happen, but what should happen. It asks Whose future are we imagining? and who gets to decide? This expands it beyond a technical tool into a broader framework that integrates analysis with imagination and evidence with values. 

What Foresight Makes Possible

What, then, is the practical value of foresight, both as a way of thinking and as an applied method?

Foresight enhances our capacity to navigate uncertainty by preparing institutions for a range of plausible futures. At the epistemic level, foresight reshapes how institutions relate to uncertainty. Rather than chasing a single ‘correct’ future, it prepares organisations for a range of plausible ones. This reduces exposure to unexpected shocks and improves strategic flexibility. In volatile environments, adaptability is often more valuable than precise prediction. By planning for a range of possibilities, policies become more resilient and responsive. When uncertainty is high, the priority shifts from waiting for clarity to building the capacity to adapt.

Foresight also integrates and encourages systems thinking into decision-making. Foresight encourages actors to examine how economic, social, technological, environmental domains interact with one another. This helps identify second-order effects and unintended consequences that are often overlooked in linear planning models. Policies, in this sense, become less about isolated interventions and more about navigating interconnected systems.

At a political and institutional level, foresight supports anticipatory governance. By identifying emerging trends, weak signals, and potential disruptions, it enables policymakers to act before issues escalate into crises. 

Foresight also makes values explicit. Every decision about the future involves trade-offs between different variables whether they are  growth and sustainability, efficiency and equity, innovation and regulation. For instance, when organisations scan for future risks, they often uncover more than just economic threats. Issues like digital rights, gender inclusion, and access to natural resources come into view. These are not just technical risks but are a reflection of what societies care about. Foresight surfaces these trade-offs and invites deliberate reflection on them. It reframes risk not just as a technical probability, but as a question of what societies choose to prioritise and protect.

Foresight also opens the door to participation.  It moves beyond expert-driven models to include diverse perspectives - citizens, communities, and stakeholders. Through participatory methods such as scenario workshops, storytelling, and visioning exercises, it democratises the process of imagining the future. This not only leads to more inclusive outcomes but also strengthens collective ownership of long-term decisions. The Wales “Nature and Us” initiative offers a compelling example. By engaging citizens in envisioning the environmental future of their country, it transformed abstract policy goals into shared societal aspirations. Participants were not just consulted - they were co-creators of the future vision.

Finally, foresight is not a single method but a mode of inquiry. It draws from a range of disciplines including science, social science, and the humanities, and resists reduction to any one discipline or approach. It adapts to different cultural and economic contexts. Whether deployed through quantitative analysis, storytelling, scenario workshops, or speculative design, it takes the shape of what a given moment, community, or challenge requires.  This malleability allows foresight to travel across sectors and cultures and showcase its utility there. 

From Reaction to Readiness

At its core, foresight offers a simple but powerful shift  from reacting to anticipating. In a world defined by polycrises, this shift is more essential than optional. 

Foresight does not promise perfect answers. What it offers is something more pragmatic. It helps identify emerging patterns, question assumptions more deeply, and prepare more thoughtfully.  By equipping institutions to navigate a range of plausible futures rather than a single predicted one, it builds the strategic flexibility that volatile environments demand. Furthermore, the very shift towards the usage of the word ‘foresight’ instead of futures or future studies is driven by the desire for practical application and action in the present itself. 

However, this shift from reaction to readiness is neither automatic nor costless. Foresight, for all its value, carries real limitations in practice. Effective foresight depends on institutional capacity to absorb and act on it. These capacity limitations manifest in several ways. At the cultural level, short-term organisational pressures and delivery cycles systematically marginalise long-term thinking in the absence of meaningful ownership over foresight outputs. At the level of process, quantitative studies may be prioritised or trusted by organizations that view ‘weak’ evidence like qualitative and narrative studies, and institutional fragmentation may prevent the cross-sectoral collaboration the work demands. Structurally, foresight functions often remain isolated and dependent on single individuals or short funding cycles, making policy continuity especially difficult. Lastly,  foresight requires specialised skills, futures literacy, and multidisciplinary teams that are either absent or unevenly distributed across institutions. 

Addressing these limitations is a pre-condition to effective foresight. These limitations are not arguments against foresight but arguments for taking its implementation seriously. Ultimately, the future is not something that simply happens to institutions; it is something they can actively shape, provided they are structured and committed to doing so.