In this follow-up session, Recollective’s VP of Research, Laura Pulito, and Lenny revisited last year’s predictions, examined what has changed and explored how AI is reshaping qualitative research at every level. What emerged was a clear picture of an industry in acceleration and a future defined by flexibility, authenticity and smarter use of technology.
From Evolution to Acceleration
When Laura revisited last year’s session on the evolution of insight communities, a few predictions stood out:
- More non-traditional researchers stepping into the process
- New tools making research more approachable and accessible
- Communities growing in importance as technology lowered the barrier to entry
None of that has faded. If anything, communities have become even more powerful as the ecosystem around them has expanded. What did change faster than expected was the pace.
Drawing on the latest GRIT data, Lenny described the past year as a true tipping point for AI adoption. A year ago there was still a meaningful “wait and see” segment. Now, he said, the idea that you can sit on the sidelines has basically disappeared. Most brands and suppliers are using AI somewhere in their stack, even if the methodological playbook is still being written.
The shift isn’t just about tools. It’s about expectations around speed, cost and quality. The pressure to move faster is now strong enough that long-standing inertia — “we’ve always done it this way” — is starting to give way.
As Lenny put it, there’s no putting the genie back in the bottle.
Qualitative at Scale Becomes the New Normal
One of the clearest shifts since last year is how quickly qualitative at scale has moved from an emerging idea to an expected capability. Surveys long served as the most efficient way to capture structured data, while qualitative work delivered depth at the expense of time and resources. Digital communities helped narrow that gap, yet practical limitations still remained.
Those limitations are now disappearing.
AI-assisted moderation, rapid transcription and scalable analysis have made it possible to run deeper, more conversational studies with much larger audiences. In the latest GRIT data, Lenny noted a notable rise in qualitative’s share of projects and expects that trajectory to continue as these tools mature.
What’s emerging is a quiet but significant shift away from relying on traditional surveys as the default. Teams are building insight programs around ongoing, conversation-led environments, then adding structured questions where needed. Qual and quant are no longer separate lanes but parts of the same continuous system.
On Recollective, that shift comes to life through:
- Always-on communities that support ongoing relationships
- Activity-based research that feels more like a dialogue than a form
- Conversational AI task types that bring the depth of an interview to the scale of online research
The goal is simple. Preserve the human depth that makes qualitative powerful, then use technology to deliver it with the speed, structure and scale decision makers now expect.
Trust, Quality and the Role of AI
If there was one theme in the latest GRIT data that everyone felt, it was trust.
Who are we actually talking to? How do we know they are who they say they are? How do we protect data quality when everything is digital and moving quickly?
Lenny pointed out that data quality problems aren’t unique to research — advertising and other digital industries wrestle with fraud and noise as well. But he also noted that communities and qualitative approaches can often be easier to manage from a quality standpoint, because you know who you are engaging with and can validate them more directly.
Laura shared how Recollective has been intentionally weaving AI into the platform with trust in mind. Yes, there are the obvious steps — thoughtful screeners, video validation and ongoing engagement signals — but our Conversation Task Type adds another layer.
Because it understands your target profile and your objectives, it can:
- Assess whether a participant fits the audience you’re looking for
- Listen for authenticity and relevance in real time
- End conversations that don’t appear genuine
It’s not about replacing human judgment. It’s about giving researchers more signals and safeguards at the point of capture, instead of discovering problems at the end of fieldwork.
At the same time, both Laura and Lenny emphasized that trust isn’t just about catching bad actors. It’s also about respecting how people want to show up.
Some participants are happy engaging with AI. Others are more skeptical and want human-led interactions. Younger audiences in particular may push back against anything that feels artificial or over-engineered.
That means researchers need to stay flexible — using AI where it adds clear value, while protecting space for authentic, human experiences where that matters most.
Centralization, Democratization and a Changing Ecosystem
Acceleration isn’t just changing methods. It’s reshaping the entire ecosystem around insights.
Laura highlighted a growing trend she’s seeing in customer conversations: enterprises are looking for a single, trusted environment where they can centralize insight work, spin up studies quickly and keep depth without sacrificing speed.
Instead of scattering projects across many tools, teams are using platforms like Recollective to:
- Run long-term communities and ad hoc projects in one place
- House stakeholder-led studies alongside researcher-led work
- Build a living repository of conversations, activities and outputs
That centralization is happening at the same time as another big shift: the diffusion of the insights function across the organization.
Lenny described three broad categories of technological change:
- Utility — tools that help you do the same work faster or cheaper
- Augmentation — tools that let you do familiar things in more powerful ways
- Net new — tools that enable entirely new types of value
Across all three, the pattern is the same. More stakeholders can now run some form of research themselves. Brand marketers, product managers and analytics leads increasingly have access to platforms and templates that let them answer specific questions without going through a traditional briefing cycle.
That doesn’t eliminate the need for researchers. Instead, it changes their role. Insights teams become:
- Stewards of quality and governance
- Guides for when to involve partners or advanced methods
- Strategic thinkers who connect signals across all those decentralized efforts
Centralized platforms and communities give them the visibility they need to do that, while AI helps knit everything together into something coherent.
What’s Coming Next: Consolidation, Synthetic Participants and AI-Driven Consulting
When Laura asked Lenny to look ahead, he pointed to several forces that are likely to shape the next few years.
First, consolidation. As AI matures and investment continues, we can expect more financial engineering that brings platforms together — larger “hubs” that integrate communities, CX, UX and more. That may mean the partners you work with today end up under a different umbrella tomorrow.
Second, the rise of AI-driven consultancies and new models of value creation. As more of the process work gets automated, the real differentiation will come from critical thinking — the ability to frame the right questions, interpret noisy signals and connect insights to business outcomes.
Third, new types of “participants.”
We are already seeing serious interest in synthetic respondents for use cases like concept testing, where it’s simply not feasible to show hundreds of ideas to real people and expect meaningful feedback. Lenny noted that we should expect to see a world where sometimes you need to talk to the human, and sometimes a well-trained digital agent is more than adequate.
The implication for researchers is not to pick a side, but to:
- Be clear about where synthetic inputs are appropriate
- Understand when human nuance is non-negotiable
- Design studies that treat AI-moderated conversations and human-moderated conversations as complementary, not competing
In other words, instead of starting with “what methodology should I use,” start with “what is the real question and what mix of tools will get me to the best answer with acceptable risk.”
Practical Advice for Insight Professionals
A lot of what’s happening right now can feel overwhelming. New platforms, new features, new acronyms — and not a lot of extra time to figure it all out.
So what should researchers, agencies and stakeholders actually focus on?
A few themes surfaced repeatedly in the conversation:
- Lead with purpose, not an approach: Start with the business question and the decision at stake. Use that to determine where AI and automation fit, rather than leading with the approach and trying to retrofit a use case.
- Double down on fundamentals: No matter how fast things move, the core of our work doesn’t change: understanding people, surfacing what truly matters and translating that into action. Good screening, sharp objectives, thoughtful design and clear storytelling still win.
- Experiment in low-risk spaces: Try AI-moderated tasks, new analysis workflows or synthetic respondents on projects where the stakes are manageable. Build internal proof points so you know when and how these tools add value for your organization.
- Embrace democratization without losing rigor: Make it easier for non-researchers to run basic work, but give them guardrails. Central platforms, templates, training and clear “when to call in a pro” guidelines go a long way.
- Remember we’re built for this: Lenny closed with a simple piece of advice: have hope. This industry has already adapted through major disruptions, from the rise of online research to the rapid pivot in 2020. We’re trained to work with information, spot patterns and project outcomes. Those skills are exactly what the moment requires.
Laura echoed that sentiment. Technology will keep evolving, but the value researchers bring — curiosity, empathy, critical thinking and the ability to turn complexity into clarity — is not going away.
Embracing the Future with Recollective
For us at Recollective, this “then, now and next” conversation reinforced something we see every day with our customers: qualitative research is having a real moment.
Communities are becoming intelligence hubs where brands can centralize engagement, test ideas, and stay close to people over time. AI is making it possible to scale conversations, surface themes faster and protect data quality in ways that weren’t practical even a few years ago.
Our focus is on building that future with researchers, not just for them — from our Conversation AI Task Type and AI-powered analysis, to flexible community structures that support both long-term programs and rapid-fire projects.
We’re excited to keep partnering with brands, agencies and researchers to push the boundaries of what qualitative can do.
If you missed the session, you can watch the full on-demand recording and hear the conversation between Laura and Lenny in their own words.
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