Why Foresight is Essential for Risk and Resilience in a Disrupted World

One of the key goals of Strategic Foresight and Futures Thinking is to help us anticipate and prepare for disruptions before they occur. In contrast to reacting after the fact, foresight empowers decision-makers to explore uncertainty, identify emerging risks, and design strategies that are resilient—or even transformative—in the face of change.

To do this, foresight uses a variety of tools:

• Horizon scanning to detect signals of change, including weak signals and wildcard events.

• Trend and megatrend analysis through frameworks like STEEP or PESTEL-V, covering everything from socio-cultural shifts to technological breakthroughs and environmental tipping points.

• Futures Wheels and systems thinking to map ripple effects and second-order consequences.

• Scenario planning to stress-test strategies and policies against multiple plausible futures.

  
  

All of these techniques push us to think systemically, long-term, and beyond the limits of the known.

But how does this differ from more traditional risk management?

Comparing Traditional Risk Management and Foresight

Traditional risk management, especially in corporate and public-sector contexts, tends to be linear, data-driven, and probability-based. It relies heavily on historical data to assess known risks—estimating their likelihood and potential impact in order to design control measures. Think of practices like:

• Risk registers

• Heat maps

• SWOT analyses

• Business continuity planning

• Insurance modeling

These tools are invaluable for managing known and measurable risks, such as operational failures, regulatory non-compliance, or market volatility. But they often fall short when it comes to deep uncertainty—the kind triggered by systemic disruption, geopolitical shocks, AI-driven transformations, or climate tipping points.

In other words, traditional risk management asks:

“What is the probability that X will happen, and how can we mitigate it?”

Whereas foresight asks:

“What could change the game entirely—and how do we prepare for a range of plausible futures?”

(In this context, refer to Foresight vs Forecasting as well)

Rather than being opposites, foresight and traditional risk management are highly complementary. When integrated, foresight can broaden the scope of risk identification, enrich strategic imagination, and ensure that risk frameworks don’t just focus on what’s probable, but also on what’s possible and impactful—however unlikely it may seem.

As we move deeper into a world marked by nonlinear change, volatility, and polycrisis dynamics, it’s increasingly clear that traditional models are no longer enough on their own.

This shift is underscored by FERMA’s recent publication, “Next: New Exposure Trends”, which I covered in a previous blogpost. It highlights how Europe’s risk managers are grappling with emerging threats that defy conventional metrics and planning cycles.

And it’s further reinforced by a compelling new paper by Roger Spitz and Olivier Desbiey in the Journal of Operational Risk (2025):

“The Future of Risk and Insurability in the Era of Systemic Disruption, Unpredictability and Artificial Intelligence.”

In the next section, I’ll walk you through what that means—and why it matters for future-ready risk and strategy.

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In a world increasingly defined by systemic disruption, deep uncertainty, and AI-driven transformation, the classical risk models based on probability and predictability are no longer adequate. This paper argues for a radical reframing of how we conceptualize, manage, and insure against risks, proposing the AAA Framework – Antifragile, Anticipatory, and Agile – to build adaptive capacity in this unpredictable environment.

1. From Risk to Radical Uncertainty

• Risk vs. Uncertainty: Building on Knight (1921) and Keynes, the paper emphasizes that traditional risk involves known probabilities, while uncertainty implies situations where neither outcomes nor probabilities are knowable.

• Deep Uncertainty: It describes a state where not only are outcomes unknowable, but stakeholders cannot even agree on what futures are plausible.

• Three Drivers of deep uncertainty:

  • Complexity – nonlinear, emergent, and interdependent systems.

  • Compound Parameters – too many unknown variables.

  • Unknowability – future events and their nature are unpredictable.

2. The New Normal: Systemic, Cascading Crises

We are living in a polycrisis or permacrisis age – where multiple global risks (climate, geopolitical, technological) interact in amplifying ways.

Events once seen as rare (“black swans”) are now increasingly frequent and entangled.

Examples: climate tipping points, AI-driven geopolitical shifts, cyber risks, and pandemic spillovers.

Historical frameworks and probabilistic models fail in the face of these nonlinear, cascading risks.

3. The “Complex Five” of Uncertainty

The paper introduces a taxonomy of uncertainty types:

• Gray Rhino – highly likely but ignored risks.

• Black Elephant – obvious risks that no one wants to confront.

• Black Jellyfish – misunderstood systems with nonlinear tipping points.

• Black Swan – rare, unpredictable events.

• Butterfly Effect – small events causing major, unpredictable consequences.

4. The Limits of Data, Models, and AI

Probabilistic models fail under deep uncertainty, especially when they rely on assumptions of stability.

AI & big data can reinforce bias if trained on flawed or incomplete historical data.

Despite its predictive power, AI lacks imagination and fails in chaotic, nonlinear systems.

Emphasis is placed on Bayesian methods, while acknowledging their limits in unknown territories.

5. The AAA Framework: A New Approach

To navigate and benefit from unpredictability, the authors propose:

Antifragile

Inspired by Taleb’s concept:

Go beyond resilience by building systems that gain from disorder.

Requires redundancy, optionality, modularity, and the ability to turn small failures into learning.

Example: companies with high cash reserves outperformed during crises.

Anticipatory

Linked to strategic foresight and futures thinking.

Tools: Scenarios, Futures Wheels, and Premortems to imagine and prepare for next-order impacts.

Examples:

Earth observation + AI for wildfire prediction.

Ex ante insurance models using wearables and preventive health data.

Agile

Emphasises emergent and strategic agility.

Supports “infinite loop bridges” between short-term actions and long-term foresight.

Case: Jakarta’s open data flood management system using real-time social media, IoT, and feedback loops.

6. Conclusion: Future Preparedness & Agency

Organisations need to embrace uncertainty instead of trying to eliminate it.

Core recommendations:

• Abandon overreliance on models.

• Build robust yet flexible systems.

• Encourage foresight-based decision-making.

• Develop agile governance and organisational agency.

• The AAA Framework is a call to action to shift from predictive control to imaginative navigation.

  
  

Key Concepts & Tools Cited

• Knightian Uncertainty

• Taleb’s Fat-Tail Risks & Antifragility

• Strategic Foresight & Scenario Planning

• Bayesian Reasoning & Cognitive Bias

• Complexity Science & Systems Thinking

• DMDU (Decision Making under Deep Uncertainty)