The market research industry faces unprecedented change as AI reshapes how organizations produce and consume intelligence. At Barcelona’s SCIP Intellicon 2024, practitioners shared insights about this transformation that point to deeper shifts in how market intelligence functions. In the weeks since my workshop on the topic there, I’ve been speaking with participants and others about the topic and have some initial conclusions
From Tools to Infrastructure
AI isn’t just another market research tool – it’s becoming the foundation that makes modern research possible. “We’ve invested millions in AI models trained on our proprietary data,” shared one technology executive. “But what we’ve built isn’t simply automation – it’s an entire ecosystem for generating insights.”
Yet this same infrastructure enables both excellence and mediocrity. “There’s more terrible AI than good AI,” noted one veteran analyst. “The difference between sophisticated AI research and superficial automation is stark, but many buyers can’t tell them apart.” This mirrors the broader industry trend where research quality varies dramatically despite using similar underlying technology.
Quality Assessment in an AI World
Organizations are developing new approaches to evaluate AI-generated research. One procurement leader described their systematic approach: “We test providers with specific market numbers we already know. If they’re off by orders of magnitude, they’re immediately disqualified.”
Another participant emphasized the importance of transparency: “If they can’t explain their methodology clearly, we walk away. The black box excuse doesn’t work anymore.” This focus on methodological clarity marks a shift from treating AI as magical to treating it as infrastructure that must be understood and validated.
The Infrastructure Stack
The discussions revealed distinct layers where AI is transforming research. At the data collection level, companies described systems that continuously gather and integrate information from multiple sources. “Our models don’t just collect data,” explained one research director. “They’re constantly learning which sources are reliable and which patterns matter.”
In analysis, organizations are building sophisticated validation processes. “We used to just cross-reference numbers,” shared one participant. “Now we’re examining the entire chain from data collection through to conclusions.” This comprehensive approach treats each step as part of an integrated infrastructure rather than isolated tools.
The delivery infrastructure has evolved similarly. “Five years ago, we manually created every report,” recalled one research provider. “Now our infrastructure automatically generates insights, but more importantly, it helps clients understand where those insights come from.”
Organizational Impact
This infrastructural shift forces changes in how organizations work with research. “We had to rebuild our entire approach to market intelligence,” admitted one strategy director. “It wasn’t enough to just buy new tools – we needed new skills, new processes, new ways of thinking.”
Another participant described their learning curve: “At first we thought AI would just make everything faster. We learned that it actually requires more expertise, not less – just different expertise than before.”
What’s Next
The future demands new approaches to market intelligence. “The organizations succeeding with AI aren’t treating it as a magical solution,” observed one consultant. “They’re treating it as infrastructure that needs to be purposefully designed, carefully maintained, and skillfully used.”
Success requires understanding both the technology’s capabilities and its limitations. As one participant summarized: “The real challenge isn’t implementing AI – it’s building an infrastructure that combines AI capabilities with human expertise in ways that actually improve decision making.”
The shift from AI as tool to AI as infrastructure demands thoughtful consideration of how organizations collect, analyze, and use market intelligence. Those who understand this shift will be better positioned to build effective research capabilities for the future.
