By Sam Ladner, PhD Independent Researcher and Foresight Strategist

Sam Ladner is a sociologist and student of the future of work. After having spent years working in the technology sector, she is now an independent researcher and consultant. She is currently authoring a book called Practical Foresight which will be published by Routledge. Connect with Sam on LinkedIn: https://www.linkedin.com/in/sladner/ Visit Sam’s website: Sam Ladner | Discover Future Work Strategies

Written for the University of Surrey’s CAQDAS Blog. Dr. Ladner maintains copyright over this content.

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Guest posts express the views of guest contributors and do not necessarily represent the views of the CAQDAS Networking Project or constitute an endorsement of any product, method or opinion.

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Qualitative researchers have long used narratives and stories as a primary component of their empirical data. Interviews, focus groups, and ethnographies typically include dozens, if not hundreds, of stories of varying lengths. But qualitative researchers may be less familiar with the use of stories in strategic foresight. In this article, I outline the practice of strategic foresight, the important role stories play in foresight, the unique ability that applied social scientists can bring to foresight. I also outline how to use generative  artificial intelligence (GenAI) in foresight and offer some guardrails on how to use GenAI safely and effectively.

Strategic foresight is the practice of systematically anticipating the future. Tsoukas and Shepherd (2009) define foresight as “the ability to see through the apparent confusion, to spot developments before they become trends, to see patterns before they fully emerge, and to grasp the relevant features of social currents” (Tsoukas & Shepherd, 2009, p. 2), in order to act before it is too late. Framed in this way, foresight is a critical practice for most companies today (if not most individuals).  Strategic foresight is often practiced within corporations, by trained social scientists. In their literature review on foresight, Marinkovic et al (2022) define corporate foresight as “a firm’s capacity to interpret changes in the business environment, outline and evaluate [a] plausible future based on these changes, and then utilize this information to build and sustain competitive advantages” (Marnković et al., 2022, p. 290).

The telling of stories is key to strategic foresight, partly because it involves what psychologists call “episodic thinking,” or the ability to envision experiences with rich detail. This is exactly what Proust did in his 1913 book À la recherche du temps perdu. He tells the story of his aunt’s petites madeleines cookies with such detail that we feel transported to her cottage in the French countryside. It is this same episodic description that underpins strategic foresight. In their paper on foresight methods, Stratigea et al (2012) note that narrative – a qualitative phenomenon – is a fundamental ingredient to strategic foresight. Not only do expert interviewees typically offer insights in the form of narrative, but future scenarios are narratives too. This would feel very familiar to the qualitative researcher.

Given the narrative focus, it’s unsurprising then that foresight has deep roots in the ethnographic tradition. Textor (1989), for example, outlines a narrative-driven method he calls “ethnographic futures,” which emphasizes deep listening to participants as they tell stories about the futures they can imagine. Strategic foresight additionally relies heavily on expert assessments, such as with the Delphi method. It also relies on interviews with scientists and artists who are on the cutting edge of change (Wygant & Markley, 1988).  Wygant and Markley suggest specifically using a qualitative data analysis tool, such as ATLAS.ti, to analyze the narratives in foresight practice.  You simply cannot do strategic foresight without some amount of qualitative data.

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A career option for applied qual researchers

Developing anticipatory and interpretive skills is incredibly useful for the applied qualitative researcher. It offers a career path outside traditional academia that is ideally suited to the qual researcher’s core skill set. It also offers potentially deeply satisfying work; qual researchers practicing foresight can advise and direct various stakeholders on strategic actions. This type of work can be exciting, interesting, and fulfilling.

Applied qualitative researchers work in a variety of contexts, including technology companies, consumer packaged goods companies, financial institutions like consumer banks, and of course market research consultancies. Roles such as UX researcher, focus group moderator, or advertising planner are typical for the applied qualitative researcher. Organizations like the Ethnographic Praxis in Industry Community (EPIC) and the Qualitative Research Consultants’ Association (QRCA) are some of the many professional homes for applied qualitative researchers. Less commonly, qualitative researchers are members in organizations like the Association of Professional Futurists (APF). But the skill of qualitative analysis is a massive advantage for foresight work.

The analysis process of episodic narratives would feel very familiar to qualitative researchers who have used tools like ATLAS.ti, NVivo or MAXQDA to analyze interviews, or ethnographic fieldnotes. In foresight, using such a tool allows the practitioner to provide a faithful analysis of future scenarios offered by stakeholders and external experts. In their case study, Stratigea et al (2012) asked expert participants to envision a distant future and then used ATLAS.ti to analyze and code their experts’ narratives. Using ATLAS.ti allowed them to map the commonalities between them with precision. They were able to see patterns that were grounded in the actual words participants used. If you rely solely on pulling out quotes from transcripts manually, or even simply just trying to remember what people said, your results will lack both precision and validity. Yet, because strategic foresight is not dominated by qualitative researchers, this kind of analysis is not done as frequently or with as much rigor as it is within more traditional qualitative methods like interviews or ethnography.

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Using AI in Strategic Foresight

Given that foresight practitioners are not typically trained in qual analysis, it is very common to want to off-load analysis or stories onto artificial intelligence. There are a plethora of new GenAI-driven tools that purport to do this analysis within seconds. I myself have been asked about the role of AI in foresight more times than I care to count. My answer is always the same: GenAI tools like ChatGPT from OpenAI or Claude from Anthropic offer potentially exciting new ways to practice foresight, but they do not replace the need for human foresight practitioners.

This is for three reasons. First, it’s because all foresight roles rely upon human’s ability to think critically and creatively. Recent research has shown that GenAI can actually reduce critical thinking. Gerlich (2025) found that people who offload the cognitive task of evaluating and synthesizing exhibited lower critical thinking themselves, while Lee et al (2025) found that people who have higher confidence in GenAI also report engaging in less critical thinking. There is also evidence that GenAI produces less creative results. Artists using GenAI demonstrate higher creative output, as judged by their peers, than those who did not use AI (Zhou & Lee, 2024), but the key may be because such tools make it easier for humans to cycle through many different ideas quickly (B. C. Lee & Chung, 2024). Imagine a curator off-loading analyzing a painting’s artistic significance to AI! It would be both less critical and less creative.

Kees van der Heijden, one of the founders of the scenario planning method, has described scenario planning an “art” specifically because it required human creativity and intuition, something we now know that GenAI tools simply cannot offer. (Rowland & Spaniol, 2022). These tools allow us to expand, speed up, and enhance our strategic foresight practice, but they cannot create what Rowland et al describe as “a practitioner’s unique approach, honed over years of practice, the occasional new challenge, and a few failure to learn from along the way. In this way of thinking, art is generally not thought of as formulaic or merely the outcome of an algorithmic list of phase, stages, and steps” (Rowland & Spaniol, 2022, p. 5).

The second reason GenAI will never replace human foresight practitioners is about accountability. An GenAI tool can never be held accountable for poor foresight recommendations. Imagine the ‘hallucinations’ that are inherent to AI models being responsible for a company divesting itself from a very lucrative line of business, or even to unwittingly break the law. Human foresight practitioners can be held accountable for poor recommendations. And they can use their very human emotional signals to gauge what might be incorrect or even irresponsible foresights.  But AI cannot be charged with a crime, such as fraud, or be fired for lying about a weak signal that never actually happened. Only humans can be held accountable for visions of potential futures, so only humans can actually do foresight.

The third reason GenAI will not replace human foresight is quite practical.  Artificial Intellience,  in general is  a backwards-looking tool at its core. As philosopher Shannon Valor argues, AI is based on probability models of past human information,. GenAI is build “literally …to conserve the patterns of the past and extend them into our futures” Vallor, 2024, p. 32). In other words, artificial intelligence is not just artificial, but old.  It cannot predict new things because it is only based on old things. This is the problem that humans already have inside corporations, being too conservative to believe change is happening, or being too conservative to make necessary changes in time to accommodate an emerging trend.

For these reasons, GenAI tools like ChatGPT should be thought of as an assistant to humans, not as a replacement for humans. This is true for AI tools in general, not just GenAI and not just for foresight work. In her paper on using GenAI in research, UC Berkeley lecturer Stef Hutka (2024) suggested that we use GenAI for tasks that are a) unenjoyable for humans and b) done well by GenAI. So in the case of foresight, we should leverage GenAI’s ability to be vigilant and consistent. For example, we can ask GenAI to monitor weak signals, from reputable sources we specify, on an ongoing basis. We can also ask GenAI to analyze masses of quantitative or qualitative data that may be too boring or take too long for humans to do. GenAI will stay vigilant, do consistent work, and without boredom. These are perfect use cases for it. But the sensemaking of weak signal scanning is clearly a human skill. Making interpretations is better left to creative, forward-thinking, and accountable human beings.

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Developing and deepening qualitative skills

AI doomerism may be quite common for many professions these days. Lawyers, software engineers, writers, and artists are all grappling with the prospect of GenAI replacing their work. But since we know human creativity is the core of all of that work – as it is for qualitative research – we should instead embrace AI’s potential for productivity boosts. But we should do it on our terms. For strategic foresight in particular, it means relying more heavily on the interpretation of weak signals, and the interpretation of imagined futures. Human interpretation has always been the core of qualitative research – hence the term “interpretivism”! – so qualitative researchers are well placed to become foresight practitioners.

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AI Disclosure: No artificial intelligence was used to write this document. The author wrote the entirety herself, with the assistance of human editors.

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Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Hutka, S. (2024, August 14). Designing AI to Think with Us, Not for Us. Ethnographic Praxis In Industry Conference. https://www.epicpeople.org/

Lee, B. C., & Chung, J. (2024). An empirical investigation of the impact of ChatGPT on creativity. Nature Human Behaviour, 8(10), 1906–1914. https://doi.org/10.1038/S41562-024-01953-1;SUBJMETA

Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. CHI 25. https://doi.org/10.1145/3706598.3713778

Marinković, M., Al-Tabbaa, O., Khan, Z., & Wu, J. (2022). Corporate foresight: A systematic literature review and future research trajectories. Journal of Business Research, 144(January), 289–311. https://doi.org/10.1016/j.jbusres.2022.01.097

Rowland, N. J., & Spaniol, M. J. (2022). The strategic conversation, 25 years later: A retrospective review of Kees van der Heijden’s Scenarios: The Art of Strategic Conversation. Futures and Foresight Science, 4(1). https://doi.org/10.1002/ffo2.102

Stratigea, A., Grammatikogiannis, E., & Giaoutzi, M. (2012). How to approach narratives in foresight studies: Qualitative data analysis. International Journal of Foresight and Innovation Policy, 8(2–3), 236–261. https://doi.org/10.1504/IJFIP.2012.046112

Textor, R. B. (1989). A Brief Explanation of Ethnographic Futures Research. Anthropology News, 30(8), 1–2.

Tsoukas, H., & Shepherd, J. (2009). Managing the future: Foresight in the knowledge economy. https://books.google.com/books?hl=en&lr=&id=K959LzCEi3wC&oi=fnd&pg=PR1&ots=EvwMpfKxCt&sig=6_D2ZhQ54vggiMnzOQ0qKd4iP9E

Vallor, S. (2024). The AI mirror : how to reclaim our humanity in an age of machine thinking. Oxford University Press. https://global.oup.com/academic/product/the-ai-mirror-9780197759066

Wygant, A. Chambers., & Markley, O. W. . (1988). Information and the future : a handbook of sources and strategies. 189.

Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3). https://doi.org/10.1093/PNASNEXUS/PGAE052

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