User research for strategy and innovation
Stepping into the role we've been asking for
👋 Hey, I’m Nikki. Each week I write about UX research strategy, communicating impact, and using AI to do your best work. For more: Claude Skills Bundle | AI Prompt Library | Team Training
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If you’ve ever sat in a strategy meeting and watched the PM, the head of product, and the leadership team trade views on where the company should be in three years, and waited politely for someone to turn to you and ask the “user perspective” question, you’re not alone.
You probably know more about your users than anyone else in that room. You know the gaps in the product, the workarounds people have been building, the unmet needs that have been showing up in interviews for the last four quarters and that nobody has acted on yet. But none of that translates into “where should we be in 2030,” and I don’t think it’s because you don’t know enough. I think it’s because you’ve been pinned to a completely different kind of question for so long that the strategic muscle has gone unexercised.
The roadmap is loaded, the stakeholders need a usability test by Friday, the PM wants to know if the new flow tested cleanly, and there’s another tactical question on your desk before this one is finished. That’s been the whole job, every quarter, for years. The “be more strategic” feedback keeps showing up in performance reviews, the strategy offsite invitation keeps going to product, and you keep getting looped in only when something has already broken.
I think the frustrating part is that we, as user researchers, are the most under-utilised strategic foresight resource in any company. We already do half the futurecasting job by training and instinct, but we just have only ever been allowed to point those skills at next quarter.
Why user researchers were trained for this work
I want to make a case I haven’t seen anyone make clearly enough yet, which is that user research methodology is, structurally, the foundation of futurecasting. We don’t think of it that way because nobody has framed it that way for us, but once you see it, the whole career path opens up.
Here’s what I mean.
We study the past to understand the future
Every method in our toolkit is retrospective. Interviews ask people about past experiences and past behaviors. Diary studies capture recent past behavior. Behavioral analytics is, by definition, a record of what already happened. Journey maps reconstruct past sequences. Usability tests reveal how users have learned to interact with patterns over time. We are trained from day one to extrapolate forward from observed past behavior, which is exactly what futurecasting requires at its core.
When I conduct a generative interview about how someone manages their finances, I’m not really asking about today, I’m building a model of how their behavior has evolved over the last 5-10 years and using that trajectory to predict what they’ll need next. That’s futurecasting, just at the individual user level. The strategic version simply scales it up to the population, the segment, the industry.
We already make defensible calls under uncertainty
Every research recommendation we make is a forecast. “Users will struggle with this flow” is a forecast. “This concept will resonate with mid-market buyers” is a forecast. “The drop-off here will increase if we don’t address X” is a forecast. We just don’t call them forecasts, we call them findings, and futurecasting is the same skill applied to longer time horizons.
We are signal-trained
Researchers spot weak signals in qualitative data for a living. The unprompted aside in interview number seven that becomes the seed of an insight three weeks later, the comment three different participants make in slightly different ways, or the friction users describe but cannot quite name. We are pattern-matchers by craft, which is the single most valuable skill in foresight work.
We have direct access to the most valuable signal source in any futurecasting model
Qualitative user truth, gathered first-hand, is something no industry analyst report can replicate. Most foresight practitioners get qualitative input through commissioned panels and second-hand transcripts. We have it as our day job, which is a structural advantage we have never used at strategic scale.
The reason we haven’t been doing this work is not that we can’t, it’s that we’ve been too busy.
What AI actually changes for researchers
I want to be careful here because I think synthesis is one of the worst use cases for AI. Synthesis is interpretation, and interpretation is the job. The moment we hand interpretation over to a tool, we lose our edge.
What AI actually changes is three different things.
The from-scratch tax goes down
Every research project used to start from a blank page. New discussion guide, new screener, new analysis structure, new readout template, new stakeholder briefing format. With AI, you build the scaffolding once, save it as a reusable skill or prompt, and never start from scratch again. That gets you 2-4 hours back per project, which compounds into days you didn’t have before.
Smarter democratization eases the bottleneck
Custom agents and trained skills mean a PM can run a first-pass usability review or a screener draft without me, which gets me out of the queue and into the strategic seat. I’m not training stakeholders less, I’m training them better, with agents that hold the rigour I’d want them to hold even when I’m not in the room.
AI brings together radically more signal types than one researcher could process by hand
This is the actual unlock for futurecasting, and it’s the one nobody is talking about enough. Doing the work futurecasting requires (interviews plus behavioral analytics plus market trend reports plus competitor product moves plus regulatory shifts plus macroeconomic indicators plus public conversation in customer communities plus adjacent-industry signals) has been functionally impossible at the individual researcher level. AI changes that, because it can hold many signal types in working memory at once, surface cross-source patterns, and let you read the clusters and form your own interpretation.
To be clear, AI is the cross-signal-bringing tool, not the meaning-making tool. We still do the interpretation, the calls, the recommendations.
What futurecasting actually is
Futurecasting is a discipline that generates actionable strategic data for organisations by extrapolating from past and current large-scale trends in a given industry or operating sector. The output isn’t a prediction, it’s a set of plausible scenarios, each with named landmarks the organisation can watch for to determine which scenario is becoming the actual future. The point is to help the organisation prepare for, weather, or thrive through the futures it’s most likely to face.
Three things separate futurecasting from “trend-watching” or “predicting the future.”
Rigor. Futurecasting builds models with structured inputs across past, present, and projected signals. Trend-watching is reading newsletters and forwarding interesting articles. The difference is the same as between a research plan and “let me just chat with some users.”
Scenarios, not predictions. Futurecasting generates multiple plausible futures rather than one bet, because single-point predictions at strategic horizons are reliably wrong. The UXR analogue is the difference between a hypothesis you test and a guess you defend.
Revisitability. Futurecasting models are living documents you revisit quarterly, marking landmarks and updating scenario probabilities as events unfold. Trend reports are dead the moment they ship. The UXR analogue is the difference between a journey map you maintain and a journey map that lives forgotten on a Confluence page.
The reason user researchers are particularly well-positioned for this work is that each of the four points I made in the previous section maps to a specific stage of the futurecasting method. Past-behavior training maps to model-building. Defensible calls under uncertainty map to scenario generation. Signal-spotting maps to the quarterly revisit. Direct access to qualitative user truth runs through every stage.
A short example of what this could look like. A B2B SaaS researcher I worked with noticed across six months of interviews that buyers kept asking variants of the same question, “How do I know your AI features are actually saving us time?” That single weak signal, layered against support ticket data showing rising requests for ROI dashboards and a Gartner trend report on outcome-based pricing, became a futurecasting topic about how mid-market buyer trust in AI-generated work would shift between 2026 and 2029. The model produced three scenarios, the leadership team picked one as the North Star, and the product roadmap shifted from feature-led to outcomes-led six months ahead of any competitor in the space. That’s what this method produces when it’s done well.
If “be more strategic” has been the feedback you keep getting in performance reviews, and you’ve never had a clear path to actually do it, this is the path.
Below, I walk you through the full 5-stage futurecasting method I use as a researcher, with the templates, prompts, and worked examples you can copy this week:
The futurecasting topic-selection framework (the 5 questions I ask before I commit to a topic, plus the worksheet I use to test whether my topic is actually viable)
The 3-layer model template (past, present, projected, with the exact 8 signal categories I include and the spreadsheet structure I use to organise them)
A step-by-step scenario generation process for your specific industry, with worked examples across B2B SaaS, healthcare, consumer fintech, and internal productivity tools
The North Star workshop format (the 90-minute agenda, the slides, and the “likely vs desirable” voting move that surfaces the strategic disagreement nobody usually names)
The landmark log template I use to revisit models quarterly and track which scenario is becoming the actual future
12 Bad/Better rewrites you can copy across topic, model, scenario, workshop, and revisit stages
The minimum viable futurecasting practice (how to start with two hours next week)
If you’ve been waiting for a method that turns “more strategic” from a vague performance review note into a real practice, this gives you one.
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