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With all the attention AI has received lately, it’s easy to assume that strategic foresight might soon be another thing machines do better than people. After all, AI is incredibly efficient at processing large datasets, spotting correlations, and summarizing vast amounts of information in seconds. That kind of analytical speed (even with its caveats) can be extremely helpful for any organization working to understand change.

Strategic planning has always been challenging. But today, the pace and complexity of change make it tougher than ever. Annual planning cycles that rely on past data feel increasingly out of sync with reality. To stay competitive, we need to do more than react: we need to anticipate, sense shifts as they happen, and adapt together when the change happens.

The arrival of AI in strategic foresight has ushered in a wave of excitement, and for good reason. Tasks that once took weeks can now be done in hours. Horizon scans are automated, trends are clustered, and foresight radars practically build themselves. For teams struggling with time, bandwidth, and data overload, the appeal is obvious.

Strategic foresight is built on the ability to detect, interpret, and act on signals of change. But as anyone working in foresight, strategy, or innovation knows, the volume of information we now have access to is staggering. From global news feeds and patent databases to social media streams and scientific journals, signals are everywhere and the flood is rising by the minute.

In our recent article, The dual horizon, we explored a growing tension in strategic foresight: AI is transforming how we scan, sense, and shape the future, but it’s also introducing new risks around bias, blind spots, and explainability.

If you work in strategic foresight, innovation, or business strategy, you’ve likely asked yourself: What role should AI play in shaping how we think about the future? You’re not alone. From boardrooms to think tanks, the conversation around AI in foresight is heating up, and for good reason.

Collecting trends is easy. Turning them into a shared vision and real action is where the value lies. But what actually makes that possible inside an organization? What turns scattered signals into a living, breathing foresight practice?

Strategic foresight is often described as both an art and a science — and for good reason. On one hand, it demands discipline: scanning signals, tracking patterns, and drawing conclusions from data. On the other, it requires imagination: the ability to ask “what if,” to envision futures that don’t yet exist, and to explore possibilities that defy linear thinking.

When the FIBRES team arrived at DTIM 2025—Europe’s leading event on innovation, digital transformation, and strategic development—we expected insightful sessions and future-focused conversations. But we also came away with something more: a set of powerful signals about the direction foresight is heading—and how organizations are starting to rethink how they use it.