Human in the Loop: How AI is Redefining Insights in 2025

During Quirk's Virtual – AI and Innovation 2025, Rival Technologies CEO Andrew Reid and Head of UX & Founding Partner Dale Evernden took to the virtual stage to explore how AI is transforming the world of insights. Their session, AI in 2025: Industry Adoption, Practical Tips and What’s Ahead, unpacked both the promise and challenges of AI in market research today.

And the key takeaway? AI isn’t about replacing researchers—it’s about augmenting their work.

If you missed the session, a recording is available here. Below, we highlighted highlights from the presentation. 

The changing relationship between AI and market research

The discussion opened with an acknowledgment of the shifting dynamic between AI and research professionals. As AI becomes increasingly embedded in workflows, it’s natural for people to feel a mix of excitement and anxiety. Dale pointed out that the industry is undergoing a major paradigm shift, moving at an “enormous” pace. AI systems, while powerful, still behave in ways that can feel foreign, which contributes to uncertainty.

Rather than resisting AI, Andrew and Dale advocate for leaning in. AI adoption is surging: research from our sister company Reach3 Insights shows that 78% of Americans are now familiar with AI, a sharp increase from last year. Anxiety around AI has quadrupled in tandem with this increased familiarity; however, Dale and Andrew said that the more researchers engage with AI, the better positioned they’ll be to harness its potential. AI won’t replace insights professionals, but those who fail to embrace it might find themselves at a disadvantage.

The foundational truths of AI in research

To guide researchers in effectively integrating AI, Dale outlined four core principles:

1. AI models are probabilistic, not deterministic – Unlike traditional systems, AI-generated results can vary with each use. This adaptability is a strength, not a flaw, but it requires a mindset shift.
2. AI tools hallucinate – Large language models (LLMs) generate outputs based on probability, which means they sometimes present incorrect information with confidence. Researchers must verify AI-generated insights before relying on them.
3. AI requires human guidance – Market research applications of AI should be iterative, with researchers refining outputs to ensure quality.
4. Human oversight is essential – AI should augment research, not fully automate it. Keeping humans in the loop ensures the insights remain trustworthy and actionable.

Where AI is making the biggest impact in research and insights

Andrew and Dale highlighted five key areas where AI market research tools are already making an impact:

• Input management – AI-driven data collection optimizes how and when respondents are engaged.
• Authoring – AI tools help researchers refine surveys, adjust tone, and ensure engagement.
• Fielding – Automated triggers ensure surveys reach the right audience at the right moment.
• Analysis – Capabilities such as AI Summarizer accelerate the process of identifying themes and trends within qualitative data and video feedback.
• Knowledge management – AI structures and organizes insights for easy retrieval.

While AI’s role in these areas continues to evolve, Andrew emphasized that it should function as an “exoskeleton” for researchers—enhancing their capabilities rather than replacing them.

Bringing AI innovation to life

At Rival Technologies, these principles are being put into action through Rival Labs, our in-house innovation hub designed for rapid experimentation. Dale explained that the Lab operates under three key principles: rapid ideation, agile iteration, and collaboration. The goal is to continuously push the boundaries of AI-driven insights while maintaining the necessary level of human oversight.

Several Rival Technologies features have emerged from this approach, including:

• AI Tone Refinement – A tool that helps adjust the tone and style of surveys, ensuring they resonate with specific audiences.
• AI Summarizer – A feature that distills unstructured qualitative data into key themes, complete with confidence scores and evidence-backed insights.
• AI Video Reels – An innovation that compiles highlight reels from qualitative and video feedback, making storytelling more compelling.
• AI Probing with Thoughtfulness Scores (coming soon) – A system that dynamically adjusts follow-up questions based on the depth and clarity of initial responses, ensuring richer insights without unnecessary probing.

If you’re curious about these AI capabilities on the Rival platform, we have short demos of each one here. 

The ŌURA case study: AI in action

To showcase the real-world impact of AI-driven research, Andrew and Dale shared a case study with our clients at ŌURA. The Rival platform enabled ŌURA to test AI probing. 

The results were compelling:

• 94% of participants found AI-generated questions relevant and appropriate.
• 99% said the questions were easy to understand.
• AI-probed responses were 293% more thoughtful than initial responses.

This study demonstrated that when AI is used strategically, it enhances research outcomes, leading to deeper engagement and richer data.

Best practices for AI adoption in insights

As AI adoption accelerates, Andrew and Dale outlined best practices to ensure responsible and effective implementation:

• Keep humans in the loop – AI should augment human expertise and creativity, not replace it.
• Be transparent – If AI is being used to generate insights, acknowledge it to maintain trust.
• Consider indirect stakeholders – Ensure that downstream users of insights understand when and how AI is being applied.
• Prioritize data privacy and compliance – AI regulations are evolving, and researchers must stay informed about requirements such as disclosure policies.

The future of AI in research

Looking ahead, Andrew and Dale emphasized that AI is not just a market research trend. Rather, they see AI continuing to reshape research in fundamental ways in 2025 and beyond. This technology is not slowing down—if anything, innovation is accelerating. Researchers who embrace AI and experiment with its applications will be well-positioned to thrive in this new era.

As AI becomes an integral part of research, the key is to approach it with curiosity, adaptability, and a commitment to maintaining the human touch in insights. With the right balance, AI is set to unlock a new level of research sophistication—empowering insights professionals rather than replacing them.