Your team scans for weak signals. You track trends. You run scenario workshops. Maybe you maintain a trend radar and produce regular reports for leadership. Then the project ends.
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When growth slows, we tend to look outward first, as nothing seems to change internally. The market changes. Regulation updates. New competitors. The slowing economy in the region. Difficult customer behavior. All of these can become real barriers. Many of them are also difficult to influence.
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What if one of the biggest limitations in organizational foresight isn’t our ability to anticipate change, but the boundaries we’ve drawn around who gets to observe it?
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AI can scan thousands of sources, summarize weak signals, cluster trends, and draft scenarios in minutes. Yet many organizations still struggle to turn foresight into action.
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In recent roundtable discussions we hosted in Paris, we asked two simple questions: When did your organization get the future wrong, and when did you see change early enough to act?
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At the recent Innovation Roundtable event in Paris, my colleague Panu Kause and I hosted two discussions with leaders working across strategy, innovation, foresight, risk, and transformation:
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A recent Futures Finland discussion on how AI is changing futures work raised a familiar question for many futurists: What actually changes when AI enters the foresight process?
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Over the past year, many teams working in strategy, innovation, and foresight have started experimenting with generic AI tools and large language models.
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In my previous post, I described a pattern I see often: foresight work that is active, structured, even high quality, and yet, it doesn’t lead to better decision when ignored. In this one, I’ve tried to go deeper into how we could move from foresight data into futures knowledge, as knowledge becomes part of decision-making when produced and used together.
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