Keywords: trends / strategic research / participant recruitment / study design / insight analysis / AI in research / user communities / mixed methods research / sensemaking

The research landscape is moving fast: more AI, more pressure to decide quickly, more focus on diversity and impact. In the middle of all this, the same core questions remain: who we listen to, how we design research, and how we turn what we learn into decisions.

At quantica, we use ENGAGE, FRAME and SENSE as three lenses to respond to these questions. In this post we share some of the trends we see growing and how they show up in each phase of the research work.

1. Engage: Trends in how we reach people.

1.1. From “panel” to ongoing relationship.

More and more, recruitment is shifting from being a one-off, operational task (“fill the sample”) to becoming a medium-term relationship:

Participant communities that come back to collaborate in different studies.
Mechanisms to share learnings and results with those who took part.
Spaces where customers, internal teams and partners contribute signals, not only when there’s a live project.

The trend: less extraction, more relationship.

1.2. More real inclusion, less “general population”.

Another clear trend is putting the focus on who gets left out when we talk about the “general population”:

Bringing accessibility needs, socioeconomic diversity and less-digital contexts into sample design as core elements, not add-ons.
Adjusting recruitment channels and formats so we don’t filter people out without realising (only online, only office hours, only one city…).

The question is no longer just “how many do we have per segment?”, but: “Who is missing here, and what are we not seeing because of it?”.

1.3. AI as recruitment support, not a blind filter.

AI is also starting to show up in “engage”:

Designing and reviewing screeners, simplifying language and catching ambiguities.
Analysing internal databases and suggesting segments based on usage patterns.
Generating variants of invitation messages and optimising operations.

The healthy trend: using AI as an assistant to gain efficiency, while keeping decisions about diversity, ethics and inclusion criteria in human hands.

2. Frame: Trends in how we design frameworks, dynamics and materials.

2.1. Inclusive research by design.

The way we design research activities and materials is also becoming more inclusive:

Activities that allow different ways of expressing oneself (visual, verbal, hands-on).
More accessible prototypes and materials (contrast, legibility, clear instructions, simplified versions).
Co-designing exercises with people who have accessibility needs, so the dynamics themselves don’t become a barrier.

2.2. Mixed by default: qual, quant and live data.

The idea of working with ecosystems of evidence is consolidating:

Qualitative for depth, context and language.
Quantitative for scale, frequency and prioritisation.
Business, product or store data as a third pillar.

Even if not everything is combined in every project, studies are increasingly designed with:

A clear view of how what we learn will be cross-referenced with existing data.
An intention to avoid duplicating NPS, analytics or surveys the organisation already has.

2.3. New formats: 3D, VR and hybrid experiences.

The formats of what we research are also changing:

Prototypes in virtual or augmented reality to explore spatial journeys or complex services.
Models, 3D layouts or first-person walkthroughs to test phygital experiences.
Sessions that combine objects, screens and immersive environments to get closer to how the experience actually feels in real life.

3. Sense: Trends in how we make sense of what we learn.

3.1. From static reports to shared sensemaking spaces.

Deliverables are moving away from being just static documents and becoming working spaces:

Sensemaking sessions with key teams to interpret findings together.
Materials that can be reused in workshops, prioritisation sessions or roadmapping.
Living insight repositories, not just decks archived in a folder.

The trend: less “deliver a report”, more “activate a conversation”.

3.2. Storytelling with context (and clear boundaries).

Another strong trend: being more transparent about where we’re speaking from when we tell the story:

Explaining what we’ve seen, but also what we haven’t been able to see.
Making the real scope of the study explicit (markets, profiles, journey moments).
Showing tensions and contradictions, not just comfortable messages.

3.3. AI for synthesis, human judgement for decisions.

AI is also present in “sense”, mainly to:

Group open-ended responses, detect recurring themes and generate summaries.
Help navigate large volumes of feedback.

But the most interesting trends are about:

Using AI as a first pass of synthesis, reviewed and refined by the research team.
Preserving human judgement to connect what we learn with context, culture and strategy.

4. How we approach this at quantica.

At quantica, this is how we position ourselves in front of these trends:

In ENGAGE, we focus on inclusive recruitment and on using AI responsibly as support, not as an automatic filter.
In FRAME, we design frameworks, dynamics and materials that combine mixed approaches, inclusion and new formats (from classic interviews to immersive prototypes).
In SENSE, we combine synthesis (sometimes supported by AI) with working spaces where findings translate into priorities, changes and next steps.

Because trends are a compass, but the important thing remains the same: listening carefully, framing with intention, and making sense of what we learn so we can decide better.

2026: Key trends that are redefining research