AI is cutting the cost of market research—but not in the way most people think. It’s not just about reducing headcount or slashing budgets. The real impact is broader and more democratizing: when insight gets cheaper, more people across an organization can afford to ask questions. And when more people ask questions, the volume and diversity of insights grow.
Let’s start with where the savings come from. AI is automating the time-intensive, manual work that’s long defined the back office of research: scripting surveys, checking logic, cleaning data, generating cross-tabs, summarizing open ends, building decks. What used to take a team days or weeks can now be handled in minutes—or at least dramatically accelerated. This allows research teams to do more with less, but it also frees them to focus on strategy rather than admin.
The cost savings show up in all kinds of ways. Instead of outsourcing verbatim coding, you can run it through a large language model and get 90% of the value in a fraction of the time and cost. Instead of spending hours QA-ing survey logic, AI can flag potential issues in seconds. Even stakeholder reports—one of the most time-consuming parts of the process—can be drafted by AI and then refined by a human analyst.
This automation doesn’t just shrink costs. It flattens the process. You don’t need to be a seasoned researcher to get started. With natural language tools and AI-assisted platforms, a product manager, marketer, or UX designer can spin up a pulse survey, analyze responses, and share findings without ever waiting for the next big study. In other words: insight becomes self-serve. And that self-serve capability often removes the bottlenecks that make research feel slow and expensive.
And here’s where Jevon’s Paradox kicks in again. Lowering the cost of a single study leads to more studies being run. Teams that once ran one big piece of research a year might now run five or ten. Questions that were previously dismissed as “not worth the budget” are now easily testable. Curiosity scales.
It’s not just about quantity either—it’s about inclusivity. AI is lowering the barrier to entry for teams that traditionally had to wait in line for research support. Regional marketers, junior product managers, even sales teams—when the cost and complexity drop, insight becomes something everyone can reach for. That creates a more connected, customer-aware organization.
This shift has a multiplier effect. With more studies across more functions, organizations develop a more granular understanding of their customers, products, and markets. The cost-per-insight drops, but the value of those insights often increases—because they’re more targeted, more timely, and more relevant to the people making decisions.
Ultimately, making research cheaper doesn’t just change the economics. It changes the culture. AI enables a world where insight isn’t a scarce resource allocated from the top down—it’s a shared capability embedded across teams. And when everyone has access to insight, everyone can make better decisions.