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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Is your foresight stuck in handoffs? Do your leaders make strategic decisions based on the past? Let's get back to basics for a bit.
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I keep hearing the same concern from foresight, strategy, and innovation leaders: expectations go up, headcount does not.
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If your foresight output lives in one “final_v7” slide deck and five spreadsheets, you are probably not behind on thinking. You are behind on system.
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When multiple people contribute to foresight and you are still running it in decks and spreadsheets, alignment quietly becomes your full-time job.
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In many strategy conversations, someone eventually says something like: “The future of our industry is clearly heading toward X.” At that moment, something subtle happens in the room. The conversation narrows. The discussion shifts toward confirming that one direction. Alternatives quietly disappear and we start to assume one option for the future.
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AI is transforming strategic foresight. What once took weeks of horizon scanning, signal collection, and trend analysis can now happen in hours. AI can scan thousands of sources, surface weak signals, and draft early descriptions of emerging trends.
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Many organizations say they want to “start foresight” and ask for assistance or advice. They scan trends, build reports, facilitate workshops, and publish future-related presentation decks. Yet, when critical decisions are made under pressure, those foresight insights often fail to be mentioned in the room. “It is not reliable” is the persisting opinion, an opinion often expressed behind the scenes.
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Traditional risk management is built to manage what is already known. Risk registers capture risks whose impacts and likelihoods can be estimated based on historical data, past experience and existing categories.
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