Why the Real Barriers to Success Are Often Structural
Here’s the thing: the biggest barrier to AI success in research teams isn’t choosing the right tools or use cases—though those are important.
The less glamorous, but far more critical issue? Organizational structure.
This isn’t just theory. It’s something I’ve seen time and again—starting early in my career at Motorola, and continuing through three decades in market research.
When major technological shifts hit corporate America, some organizations thrive. Others stall. And more often than not, the make-or-break factor is organizational preparedness—whether the structure actually supports the new strategy.
Disruptive Tech Requires Structural Change
The Kodak story is a well-known business school case study. They saw the digital photography revolution coming. The company was full of brilliant scientists and visionary product thinkers. They had the ideas.
But the company’s highly centralized, top-down decision-making structure made it difficult to act on the innovation already happening inside the organization.
It wasn’t a failure of vision or creativity—it was a failure of structure that impeded execution.
Today’s market research and insights teams face a similar challenge. Yes, AI means new tools. But it also brings:
- New workflows
- New KPIs and performance expectations
- New cross-functional demands
And all of that stresses the structures many teams have had in place for years.
A Conversation That Brought This Home
I was reminded of this hard truth during a recent conversation with Benjamin de Seingalt, Esq., Corporate Counsel and Director of Compliance and Privacy at MarketVision Research.
Benjamin’s background is rare. He blends deep research expertise with legal and compliance fluency. That dual lens brought added depth and objectivity to our conversation about AI’s potential to advance market research.
Why AI Isn’t Delivering Big Wins (Yet)
In our chat, Benjamin and I unpacked three common blockers that research leaders are facing:
- Overlooking “low-hanging fruit”
Many teams aim high with AI—yet miss simple use cases like note-taking, grammar assistance, and other basic productivity uses. These may seem small, but they free up time, build comfort, and deliver early ROI. - Silo collisions between CX, UX, and MRX
When teams explore AI in isolation, it creates duplication, tool fragmentation, and missed opportunities to scale or synthesize. - Misaligned team structures and outdated labels
Benjamin raised a provocative point: are labels like “market research” still serving us? As AI pushes convergence across research roles, it might be time to rethink how we define ourselves—and our value.
🎥 In this episode of Conversations for Research Rockstars, watch clips from our conversation
Where Do We Go From Here?
Benjamin broached the idea of a broader, more integrated function—like Business Intelligence—to support AI adoption and break down silos.
Other possibilities might include:
- Customer & Decision Analytics, to emphasize the value of cross-functional insights
- Data-Driven Customer Strategy, to reflect the shift toward integrated, outcome-focused research functions
No matter the label or taxonomy, one thing is clear: we need to reconsider how our teams are structured, how we describe the work we do, and how the research function fits into a rapidly changing business landscape.
Exploring AI means more than adopting new tools—it means asking whether we’re willing to rethink our structures so they’re fit for the strategies AI now makes possible.
Thanks again to Benjamin de Seingalt for the thoughtful conversation—and for offering a rare perspective that bridges research operations, legal insight, and strategic clarity.
Related material:
🎥 Watch: Is it (finally) time to ditch “market research”?
🎥 Also worth watching: Are we overlooking AI’s “small” wins?
