How to Apply Strategic Foresight in User Research
Most user research is oriented toward the present or the recent past: understanding what users think right now, diagnosing friction in a current product, validating a decision that's already in flight. This is valuable work. But it leaves a significant opportunity untouched. The ability to see where things are heading before the rest of the organization does.
That opportunity is what strategic foresight is about. At its core, foresight is the practice of systematically identifying signals of change at the margins, imagining multiple plausible futures, and helping organizations make better decisions today by thinking more rigorously about tomorrow. Foresight is the discipline of finding where it already exists.
„The future is already here — it's just not very evenly distributed.“
In a recent Condens webinar, Senior Principal Researcher Dr. Sam Ladner made the case that UX researchers are uniquely well-positioned to do this work.
The skills that define good research (systematic inquiry, pattern recognition, deep interpretation, and the ability to synthesize across domains) are precisely the skills foresight requires. And developing a foresight practice may be one of the most powerful ways the field can demonstrate and defend its value.
What Is Strategic Foresight?
Dr. Ladner describes strategic foresight as the systematic practice of identifying change at the margins before it becomes mainstream. It's grounded in a simple but underappreciated truth: the future isn't a mystery waiting to be revealed. She argues that in many cases, it's already present and findable if you know how to look.
Dr. Ladner notes that it's not a set of precise predictions. It's not a list of things to do. It's not about dreaming up new features or envisioning novel products. And it's certainly not about being able to answer hyper-specific forecasting questions that stakeholders might request, like “What's the probability that hourly workers will adopt geofenced smartwatch-enabled clock-out systems?” (a question that Dr. Ladner has personally encountered).
Foresight is the practice of scanning broadly across multiple domains, identifying weak signals of change, imagining multiple plausible futures (not just one), and helping organizations plan for the futures they want while remaining prepared for those they don't. It is structured, methodical, and a skill.
Why Strategic Foresight Fails: The Twin Challenges
Why does foresight fail? Dr. Ladner argues that the reasons are almost never about methodology, but about the organizational conditions in which research operates.
Dr. Ladner has identified two root causes, which she calls the twin challenges.
The temporal challenge is about the frenetic pace of modern organizational life. A state of being bombarded with information and stimuli to the point of paralysis. In this environment, long-term thinking feels like a luxury. Stakeholders reach for quick wins. Planning horizons compress. And research that requires sustained attention and patience gets deprioritized in favor of whatever is urgent right now.
The epistemological challenge is the phenomenon of willful ignorance. Knowing something, or knowing that something is knowable, and choosing to act as if it isn't. Organizations do this constantly with research findings they find uncomfortable or inconvenient.
The epistemological challenge can be represented as a “black elephant”, which, unlike a black swan (an event that arrives without warning and that no one saw coming), is an obvious, visible problem that everyone in the room can see but that nobody openly discusses.
Dr. Ladner provides the example of Nokia’s situation in 2006. The researchers working with the company, including Dr. Ladner, were already surfacing signals about the threat posed by touch-enabled operating systems like the iPhone.
The knowledge existed, but the executives ignored it. Nokia's collapse wasn't a failure of research, but rather a failure of organizational will to act on what researchers were already saying.
The solution to both challenges is not primarily methodological. It’s structural and relational. Researchers need to build practices that widen time horizons and make it harder for organizations to credibly claim they didn't know. In the following section, we get into how this can practically be accomplished.
The Foresight Process: From Scanning to Prospection
This practical foresight framework is adapted from the work of Joseph Voros and moves from inputs to scanning, analysis, interpretation, prospection, and ultimately strategic action.
Scanning is the foundation. It entails systematically monitoring a wide range of sources across multiple domains, on a regular basis, in order to continuously build up a body of intelligence that's ready to use when needed.
The organizing framework for this process is STEEP: Social, Technological, Economic, Environmental, and Political.
Most organizations, particularly in technology and finance, default to a narrow focus on the technological or economic dimensions. STEEP deliberately widens the aperture.
The inputs from scanning are weak signals. Individual data points that, on their own, might seem unremarkable, but that point toward meaningful change. Dr. Ladner argues that a researcher's job is to surface and frame these signals (not to count them statistically) and discern which ones matter.
Analysis comes next and in her forthcoming book, Dr. Ladner describes this as a mise en place (the chef's practice of prepping and organizing every ingredient before cooking begins). Analysis, she argues, is not interpretation. It’s not about finding meaning. It’s about structure and getting everything ready so that interpretation can happen quickly and well when the moment arrives.
„Analysis is giving shape to things so you can determine what it means. And that's a really important skill that researchers tend to have. We tend to be really detail-oriented people. We like to get our arms around things, and we try to give things structure. That's analysis.“
Interpretation Dr. Ladner contrasts as where meaning gets made and where a genuine point of view becomes essential. Interpretation is not neutral summarization: it requires researchers to bring a disciplined, opinionated perspective to the signals they've gathered and make an argument about what they mean.
Interpretation is what enables proactive readiness, and it’s also important to note that analysis and interpretation aren’t mutually exclusive. They’re iterative, and going back and forth between the two is to be expected.
AI in the Foresight Process
Dr. Ladner once built her own automated foresight scanning tool and ran it on cryptocurrency trends. But she got back a report that she described as "not that interesting”. The scenarios were predictable, the process was a black box, and the output entirely lacked the creative lateral thinking that makes foresight genuinely valuable.
Her conclusion was not that AI is useless in this context, but that it has specific, bounded uses.
Scaling the scanning reach so that a researcher can surface a larger volume of signals than would be possible manually.
Structuring and tagging weak signals once the researcher has identified what they mean
Serving as a discussion partner to explore disruptive potential
Where AI falls short is with interpretation. AI is a backwards-looking system trained on historical data (there aren’t any "future facts" to train on), so it’s not a reliable tool for making predictions about the future.
„Don't use AI for interpretation. It's going to give you this bland, very simple, uncreative, and not very exciting interpretation. And it’s not able to think about the future.“
Building a Foresight Practice: Where to Start
The good news is that building a foresight practice doesn't require a major overhaul of how you work. Just a few deliberate habits, applied consistently.
Step 1: Set up your scanning database
It doesn't need to be sophisticated. You can set it up in Notion or Condens, organized by STEEP categories. A Google Sheet can also be a perfectly acceptable starting point.
What matters is having a place where signals can be captured, tagged, and revisited. Without somewhere to put things, the practice of strategic foresight can’t grow.
Step 2: Scan regularly (in small slices)
Foresight scanning doesn't need to be a major undertaking. It should be a habit. A modest, consistent investment that builds a body of data over time.
Step 3: Share signals continuously
Internal newsletters, Slack channels, and team digests can all be good channels for sharing signals. Then, when a signal shows up in a formal report six months later, people who have been hearing about it already will be more likely to engage with it seriously.
Conclusion
There's been discussion in the UX research field about a "reckoning". A sense that the profession may be doing too much of the wrong kind of work by focusing too much on “middle-range” topics. Strategic foresight is what the alternative looks like.
It’s research that operates upstream, shapes strategy rather than informing tactics, and provides value that no algorithm or AI tool can replicate. It requires deep human judgment, disciplinary knowledge, creative interpretation, and organizational patience. All things that researchers already have.
Sam Ladner (she/her) is a sociologist who helps teams innovate, design, and learn. She is the author of Practical Ethnography: A Guide to Doing Ethnography in The Private Sector and Mixed Methods: A Short Guide to Applied Mixed Methods Research. She is now an independent researcher and consultant, writing her third book, tentatively titled Practical Foresight: Strategic Foresight in Applied Settings.
Phillis is a Content Marketing Strategist at Condens. As a content creator, she produces both long and short-form content in various formats, from blog articles and social media posts to video snippets. She is passionate about ensuring that her work is high-quality and strives to publish content that is both informative and actionable for the UX research community.
