What is Strategic Foresight?

What do we do when we cannot predict what comes next? Most default to better data, sharper models, and more precise forecasts. But prediction is not the answer. Preparation is. That is the core idea behind strategic foresight.

Strategic foresight is one of the most misunderstood disciplines in business today. Many leaders hear the term and assume it means forecasting, predicting market trends, extrapolating growth curves, or modelling the most likely future.

The Organisation for Economic Co-operation and Development (OECD) defines the purpose of Strategic Foresight clearly: to equip organisations with the capacity to explore and prepare for multiple plausible futures, sparking new ways of thinking and ensuring that all strategic plans are future-ready.

Traditional strategic planning optimises for a single expected outcome — the most probable path forward based on current conditions. Foresight deliberately does the opposite, where it asks: what if conditions change in ways we have not assumed? It builds organisational resilience not by betting on one scenario, but by stress-testing strategy across several. The final output is not a prediction, but rather, preparation and readiness regardless of which future becomes the new reality.

Foresight practitioners use several structured methods:

•        Horizon Scanning: detecting emerging signals of change to anticipate future impacts.

•        Megatrends Analysis: exploring large-scale shifts in economies and societies over time.

•        Scenario Planning: developing multiple future scenarios to explore their possible implications.

•        Visioning & Back-casting: starting from a desired future state and working backwards to identify actions today.

Why Strategic Foresight Matters More in the AI Era

The world is not just changing. It is dynamically evolving — faster than you think. A technology that did not exist in your strategy three years ago is now reshaping your industry’s workforce, competitors, and customers’ expectations. Artificial intelligence is transforming industries, institutions, and the nature of leadership itself — in an environment already defined by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). And yet most organisations are still planning as if the rules of yesterday still apply.

Nokia had engineers. It had data. It had global market dominance. But it never asked the question its competitors did: “What if the phone becomes a computer?” It lacked strategic foresight, and within a decade, it was irrelevant. Nokia lost its dominance in a world that moved slowly by today’s standards — no generative AI, no large language models, no overnight industry pivots. If a lack of foresight was fatal then, imagine what it does to an organisation in this unforgiving AI era.

This is why strategic foresight has moved from optional to foundational. The Disruptive Leadership Institute places it as the first pillar of its SHAPER AI Leadership Model — a framework built specifically for leading through AI-era disruption. Leaders who build foresight capability can identify signals of change before they become industry-wide crises.

“57% of senior executives report their organisation was highly disrupted in the past year. Executives caught off guard by major risk events jumped from 1% in 2021 to 35% by 2025”  —  AlixPartners 2025

How AI and Foresight Work Together

AI and strategic foresight are not in competition. They are complementary — and when combined deliberately, they form one of the most powerful strategic capabilities an organisation can build. A joint survey of 167 foresight experts across 55 countries by the OECD and World Economic Forum found that two-thirds of practitioners already use AI in their foresight work, primarily for repetitive tasks. This opens strategic foresight to leaner teams and smaller organisations.

Where AI adds the most value in foresight work:

•        Scanning Stage: AI processes thousands of news items, patents, and market signals simultaneously — detecting weak signals that human teams would likely miss or catch too late.

•        Synthesis Stage: Complex trend data that would take analysts weeks to cluster and interpret can be summarised and visualised in minutes.

•        Strategy Stage: AI produces first-draft scenario outlines, strategic radars, and risk maps — giving leadership teams a structured starting point for deeper conversation.

What AI cannot do is equally clear — it cannot interpret, challenge assumptions, or facilitate the strategic conversations that turn scenarios into decisions. Strategic foresight still requires careful human analysis and expertise (OECD/WEF, 2025). The result? Business leadership transformed from reactive problem-solving to anticipatory governance.

This is the new model of strategic leadership in the AI era. Not human vs machine. Not foresight vs data. But both working at their respective strengths, in the same direction.

Winning Case Example

In 2009, Singapore established the Centre for Strategic Futures — a dedicated government foresight unit built on a single conviction: the ability to anticipate and prepare for multiple futures is not a luxury, but a core governance capability. While other governments were reacting to disruptions as they arrived, Singapore was running horizon scanning exercises, building scenario frameworks, and asking uncomfortable questions about futures that had not yet materialised. It was not predicting what would happen. It was preparing for what could.

The results, measured against the AI era specifically, are striking. Singapore today ranks as the most AI-ready government in East Asia, scoring 84.25 on the Government AI Readiness Index — well above the global average of 47.59. That readiness was not built overnight. It was built over fifteen years of institutionalised foresight practice — consistently asking “what might happen next, and are we ready for it?”

Although a government, this lesson translates directly to any organisation operating in the AI era. Singapore did not become AI-ready because it had the best data or the most advanced technology. It became AI-ready because its leaders built the habit of thinking ahead, stress-testing assumptions, and preparing for futures before those futures arrived. That is precisely the organisational capability that separates the companies leading AI transformation today from those scrambling to catch up.

Pick it up. Or get left behind.

AI will continue to accelerate. The environment will continue to grow more volatile, more uncertain, more complex. The leaders who thrive will not be those who simply adopt the best tools. They will be those who asked the right questions before the rest of the market knew what questions to ask.

Strategic Foresight is how you build that capability. The future will not wait for your next planning cycle. The only question is whether your organisation will be ready for it. Be the ones who see disruption earlier, move with more confidence, and shape your industry rather than responding to it.

Foresight in Practice: An M & Sullivan Perspective

At M & Sullivan, the integration of AI into foresight engagements is already underway. In a recent project for a Thai state agency focused on SME development, the Insights & Advisory team deployed a structured workflow combining horizon scanning, scenario planning, and megatrends analysis — with AI embedded at each stage.

According to a Senior Consultant in M & Sullivan's Insights & Advisory team, AI contributes most heavily during primary research and data synthesis — including consolidating insights from stakeholder interviews that would otherwise take significant time to process manually, freeing consultants to focus on interpretation rather than aggregation.

Scenario generation is another area where AI contributes, though with an important caveat. The team uses AI-generated scenarios primarily as a source of ideas, not finished outputs. Refinement remains a human responsibility, and AI-generated scenarios are sometimes set aside entirely when consultant judgment points elsewhere.

The engagement delivered a tangible benefit: significantly reduced turnaround time per task. Yet speed is not the whole story. As he notes, "Before asking AI to analyse anything, you must know what you want to learn from it" — AI output is only as good as the direction behind it, which means experienced consultants remain central to the process. This extends to client deliverables: no AI-generated output is passed on without human review, drawing on both quantitative rigour and qualitative judgment exercised with project leadership.

The lesson M & Sullivan draws from this experience is one that business leaders would do well to internalise: AI is a powerful tool for accelerating foresight work, but it does not replace the strategic thinking that makes foresight valuable in the first place.