In the first two posts of this series, we did two things deliberately. First, we mapped the trends reshaping market research as GenAI moves from experiment to expectation. Then, in Part 2, we we used scenario planning to construct four plausible futures for 2026, based on two defining uncertainties: how much organizations trust AI-derived outputs, and what ultimately counts as “good enough” research.
Together, those pieces were designed to explore a single, underlying question: How will GenAI be reshaping the researcher’s role in 2026?
That question sits beneath every conversation about tools, automation, speed, and cost. As GenAI becomes embedded in everyday work, it shifts what researchers are expected to own, influence, and be accountable for, not just what the technology itself can do.
This final post is about applying that question to your own organization.
Scenario planning only creates value when it informs real decisions. The goal is to navigate uncertainty deliberately, so the decisions you make now shape the researcher’s role you actually want to build toward.
For readers who want to work through this exercise hands-on, we have built a custom GPT that guides you through each step in real time. It acts as a structured thinking partner, helping you apply the same framework from our last blog, while tailoring the variables to your specific context.
Step 1: Anchor the Exercise in the Role Question
Effective scenario planning begins with a clear focal question. In this series, that question is: How will GenAI be reshaping the researcher’s role in 2026?
This framing works because it is broad enough to accommodate uncertainty, but specific enough to anchor real decisions. Nearly every near-term choice, from tooling and workflow design to hiring, training, and client engagement, ultimately shapes the answer to this question.
For your own organization, this question can be sharpened. For example:
- How will GenAI reshape the role of B2B qualitative researchers working with complex buying committees?
- How will GenAI change what client-side insights teams own versus what is automated or self-served?
- How will GenAI affect the role of research partners when stakeholders increasingly “ask the AI first”?
When starting your own scenario plan, this question should be the lens through which you evaluate decisions such as:
- Which parts of the research process you automate versus protect
- Where human judgment remains essential
- How researchers create value beyond execution
At this stage, our custom GPT helps translate this framing into a concrete, time-bound planning question tied directly to the decisions you are actively making, ensuring the exercise stays grounded in your reality rather than abstract theory.
Step 2: Identify the Uncertainties That Shape the Role
In our last scenario planning blog, we focused on two uncertainties because of how directly they influence the researcher’s role:
- Trust in AI-derived outputs
- The threshold for “good enough” research
Small shifts in either one can push organizations toward very different futures, from augmentation to automation to fragmentation. For many teams, these will still be the right starting point. But they are not the only uncertainties that matter.
Depending on your context, other forces may feel more urgent, such as regulatory pressure, talent retention, client concentration, procurement behavior, or internal change capacity. What matters is not which uncertainties you choose, but whether they meet three criteria:
- They materially affect the decision you are making
- Their outcomes are genuinely unresolved
- You would act differently depending on how they evolve
This is where you can customize to fit your own organization. The methodology would stay the same, but the variables would change.
At this stage, our custom GPT can either reuse the uncertainties from our past scenario planning blog, or help you define alternatives, including clear low and high endpoints so they remain usable in a 2×2 framework.
Step 3: See Which Role You Are Already Drifting Toward
Before imagining new futures, you must examine the present honestly. Most organizations are already drifting toward a future that implies a particular answer to the role question. That drift shows up less in strategy decks and more in everyday behavior. The risk is not choosing an imperfect path, but arriving there unintentionally through a series of small, unexamined decisions.
As you look around your organization, pay attention to signals such as:
- How often AI-generated summaries or syntheses are forwarded without being challenged or contextualized
- Whether speed is rewarded more visibly than depth in timelines, incentives, or performance reviews
- How frequently researchers are asked to validate outputs versus shape the original questions
- Where accountability lives when an AI-informed decision turns out to be wrong
- Whether researchers are included early in decisions, or brought in after directions are already set
- Which types of work are quietly disappearing from scopes, budgets, or role descriptions
Questions that help surface this drift include:
- How much do we trust AI-derived outputs in practice, not just in principle?
- When speed and depth conflict, which usually wins?
- Where has accountability shifted from people to tools without an explicit decision?
- Which of the futures from Part 2 feels uncomfortably familiar?
This step often produces the most insight. Drift is subtle, and once it hardens into operating norms, it becomes difficult to undo.
Here, the custom GPT helps map your current practices against multiple plausible futures, making implicit trajectories visible without judgment.
Step 4: Stress-Test Decisions Across Futures
This is where scenario planning becomes operational. Using the futures you defined, test your decisions under different conditions:
- What breaks if trust in AI-derived outputs rises faster than expected?
- How do outcomes change if “good enough” becomes the default standard?
- What shifts if a regulatory change or public failure suddenly resets tolerance?
This exercise is about understanding where decisions are robust and where they are fragile.
Importantly, this step is also about stress-testing the researcher’s role. Each future implies a different balance between execution, judgment, oversight, and influence.
The custom GPT walks through these implications step by step, helping surface second-order effects that are easy to miss when planning against a single expected outcome.
Step 5: Separate No-Regret Moves From Directional Bets
By this point, patterns begin to emerge. Some actions make sense across nearly all futures. Others clearly push you toward a particular role configuration. No-regret moves often include:
- Building AI literacy across the research team
- Strengthening validation and transparency practices
- Clarifying where human judgment remains essential
- Improving how insights are socialized and acted on
Directional bets reflect conscious choices about which future you are leaning toward, such as:
- Investing in always-on insight systems
- Redesigning roles around orchestration and sensemaking
- Expanding AI-led qualitative methods
- Repositioning research as strategic infrastructure
The value here is not avoiding bets, but making them deliberately.
At this stage, the custom GPT helps categorize actions, document assumptions, and clarify which version of the researcher’s role each bet supports.
What Scenario Planning Reveals About the Researcher’s Next Move
“The future is not something we enter. The future is something we create.”
— Leonard I. Sweet
GenAI is moving fast, and the researcher’s role is being reshaped in the background through everyday choices about speed, rigor, automation, and accountability. Scenario planning helps you spot drift early and make those choices on purpose.
So before you move on, pause for a moment and consider:
- Which version of the researcher’s role are you already drifting toward?
- Where are assumptions about speed, trust, or “good enough” going unspoken?
- Which near-term decisions are quietly shaping your future without being examined?
If you’d like to continue this thinking in a more interactive way, we’ve built a tool to support it. Our custom GPT extends the conversation beyond the page, guiding you through a focused scenario planning exercise to explore possible futures, stress-test decisions, and clarify the researcher role you’re actively shaping.