AI for AI’s Sake
Suraj Rajdev, Head of Analytics at Google, put things in perspective by making an important distinction between a mere AI strategy and a customer solving strategy that leverages AI. In simple terms, it is more important to figure out what problem you are trying to solve first and then understand if and how AI can help you solve it.
The rush to implement AI for AI’s sake, in my opinion, is probably one of the reasons some people in the market research community and beyond think we’re in an AI bubble. Rajdev addressed this too with his astute quote restating Amara’s Law: We tend to overestimate the effect of technology in the short run and underestimate its effect in the long run.
AI as Productivity Booster
Yogesh Chavda, Founder of Y2S Consulting, and Joahne Carter, Chief Marketing Officer of Semaine Health, showed how AI can be a productivity booster. Key to their success was identifying a starting point and then using custom GPTs throughout the workflow to enhance decision making. By deploying intelligent agentic solution architecture and amplifying HI, Semaine Health stated that it was able to significantly lower customer acquisition cost.
AI as an Equalizer
The discussion beyond the summit on “Disability and Inclusive Research” struck me as being very timely, considering that 1 in 4 adults have a disability comprising vision, hearing, cognition and/or mobility. It is most heartening how AI can level the playing field for people who have long suffered from dyslexia and other learning disabilities. Companies like Verizon showed how they elevate HI in people with disabilities.
That said, many of us without these disabilities sometimes struggle with data analysis. Enter companies like Displayr that showed how AI can be an equalizer for data analysis and in some cases making “survey analysis as easy as having a conversation.” Getting to consumers has also been notoriously difficult and Dom Wong, CEO and Co-Founder of Pogo, showed how AI can enable getting credible insights from hard-to-reach consumers.
AI as The Great Explainer
“Explainable AI” (xAI) is the new subterranean channel in the ongoing AI insights discourse. It literally begins at the outset i.e. from an initial hypothesis. xAI aims to debug false signals and thereby drive customer credibility. Demos focused on trust building to demonstrate false signal “noise mitigation” and ultimately drive better internal strategic decision making.
To that end, leaders with expertise in this space showed how multi-layered xAI can help with adaptive storytelling. In other words, while traditional AI may give us the WHAT, the xAI goes into the WHY and HOW decisions are made to have transparency and accountability in an AI-driven research model.
AI and Decision Intelligence
All the above put together makes AI a force in decision intelligence. By marrying human expertise and insights into advanced analytics and predictive modeling, with AI-powered decision intelligence platforms, leaders can simulate outcomes, assess risks, and optimize strategies—literally at the speed of thought. This is a big leap because it enhances the quality and speed of decision-making by combining quantitative data with qualitative human insights, at scale.
AI and Responsibility
This brings up the one crucial issue many wonder about—responsible AI (RAI). This was widely debated and there was agreement that as insights leaders we are guardians of the data and must shine a light on transparency and fairness in the AI systems that our organizations deploy. Increasingly, insights leaders will be called upon to activate regular audits of algorithms for bias and/or testing models in real-world scenarios to identify and address unintended consequences. By integrating human oversight at each crucial step, insights leaders will earn the trust and confidence of their organizations.
AI in Action
As AI becomes the gold standard across sectors and AI tools become more accessible, standardized and prolific, insights leaders and market research professionals can keep abreast with the latest development models and best practices through efforts like the AI in Action Summit. Other pathways include continuing education through workshops, webinars, and collaborative exchanges. Finally, fostering a culture of lifelong learning, technical know-how and ethical frameworks will help leaders harness the full potential of their HI.
In the foreseeable future, the upshot for all users will be that a combination of HI and AI will continue to be top of mind and remain an integral part of both the conversation and the action.
Video: All Things Insights interviewed Christina Speck, chair of the AI in Action Summit, at TMRE 2025. Speck discussed the current state of AI adoption in the insights profession.
