In complex times overwhelmed by information, the question leaders are asking is no longer “Do we have enough data?” but rather “Are we making the right decisions with it?” — and it’s in this context that AI has found its relevance across various industries.
In a recent conversation hosted by Samir Sayani, Global Head of AI Products & Strategic Partnerships at QuestionPro, and Louise Keeley, Head of Advanced Analytics at Bain & Company, we explored how artificial intelligence is transforming the way organizations generate, interpret, and act on consumer insights.
The Rise of Decision Intelligence
According to Louise, the focus in 2025 and beyond will be on collecting better data and making faster, smarter decisions.
“The winners will be those who can translate data into action quickly,” she explained.
AI has moved beyond being a data-processing tool; it’s now a critical layer in modern decision-making infrastructure. Many large enterprises are scaling their AI investments, especially in customer strategy, segmentation, and innovation.
Forward-thinking companies are:
- Using AI to simulate consumer behavior before launching new products
- Building adaptive feedback loops that fine-tune strategy in real time
- Creating synthetic personas to enrich and diversify segmentation work
Research with Synthetic Data
Samir and Louise also addressed the growing role of synthetic data in accelerating research. With synthetic data, researchers can simulate diverse consumer scenarios, reduce fieldwork costs, and pre-test concepts faster than traditional methods allow. But they quickly emphasized that synthetic data should augment, not replace, high-quality real data.
But even in the age of AI, Louise made it clear that human insight is irreplaceable. “AI is a co-pilot, not an autopilot,” she said. The most effective research teams are those that combine human judgment with machine precision, maintaining ethical rigor and strategic perspective along the way.
Looking Ahead: AI in Research is Inevitable, But Maturity Varies
Not all organizations are equally prepared to adopt AI at scale. Louise underscored the importance of assessing your organization’s AI maturity:
- Some teams are ready for full AI integration
- Others are still overcoming data infrastructure and quality challenges
- Many are just starting with pilot projects tied to specific use cases
“The key is to start where you are, with a clear use case and a measurable outcome”
Louise Keeley | Head of Advanced Analytics at Bain & Company
Leaders should focus on measurable ROI, ethical AI usage, and cross-functional collaboration between research, analytics, and product teams to ensure AI doesn’t become a siloed initiative.
What should insights professionals be doing today to prepare?
First, start using AI in small ways. Tools like ChatGPT or Claude can be powerful writing and research assistants. If you’re not using them, now is the time to start. Use them responsibly—especially if you’re not on an enterprise plan—but don’t wait for perfection. You’ll learn best by experimenting.
Second, stay curious. Read about emerging companies and use cases. Ask colleagues what tools they’re trying. Explore vendors building AI into their offerings and consider piloting one of their solutions. Even just having a conversation with these providers can be illuminating.
Third, keep the human element front and center. We must preserve authentic human voices in data and ensure real human expertise in analysis. AI should be used to amplify the human voice—not replace it. And on the analytical side, nothing can substitute for lived experience, cultural context, and nuanced understanding.
Finally, as AI models grow more powerful, it’s important to remember that they’re still models—representations, not reality. They can appear more accurate or complete than they truly are. That’s why domain expertise matters more than ever. Studies show that experts tend to get more out of these tools, because they know how to assess quality and spot gaps.
AI is already reshaping the way market research is done. But its success depends on responsible leadership, thoughtful integration, and a commitment to keeping humans—and their insights—at the heart of the process.
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