The 2026 GRIT Insights Practice Report shows the insights industry splitting into two operating models. Analytics teams are expanding on nearly every dimension, while traditional research functions face flat budgets and a narrowing method portfolio. Here's what the divide means for teams caught in the middle.
Market research and data analytics have spent a decade circling the same table, competing for the same budget line, and telling roughly the same story about becoming one function. The 2026 GRIT Insights Practice Report, Greenbook's annual survey of more than 15,000 researchers, marketers, and executives, says that story is over. Analytics teams are expanding on nearly every tracked dimension: headcount, tooling, executive access, method range. Traditional research functions are stuck with flat budgets and a shrinking list of methods they can actually afford to run well. That's not a minor industry trend. If you run an insights function in 2026, it's the budget conversation headed your way next quarter.
The Data Behind the Divide
GRIT's numbers make the split concrete. Mid-size research suppliers, the 101-to-500-employee range, now lead the industry on revenue growth, capability expansion, and AI governance maturity at once. The largest firms are three years into a deliberate exit from fieldwork toward consulting and analytics work, chasing higher margins and leaving the commoditized survey business behind. On the brand side, fraud detection has gone from optional to standard practice: 70 to 88 percent of users across every segment now run quality tools regularly. Brand-side researchers are also building proprietary panels faster than before, a direct response to years of declining trust in third-party samples.
Where Analytics Teams Are Pulling Ahead
The clearest evidence of the split shows up in where agentic AI has actually landed. GRIT found the industry has converged on three tasks where agentic AI is already embedded in daily work: analyzing data, updating reports, and preparing and integrating data sources. Those are exactly the tasks that play to an analytics team's strengths. Structured data. Repeatable pipelines. Clear inputs and outputs. Separately, Qualtrics' 2026 market research trends report puts AI tool usage at 95 percent of researchers, either using it regularly or actively experimenting with it. So adoption isn't the story anymore. The real gap runs between teams with a clear AI strategy, investment, and governance, and teams still finding their way one tool at a time.
What’s Actually Squeezing Traditional Research
Qualitative work hasn't gotten easier to defend on a budget line just because AI got faster elsewhere. GRIT still describes qualitative research as a craft of deliberate choices: smaller samples, longer timelines, a level of depth that resists the kind of scaling analytics teams are built for. That's not a flaw in the method. It's the reason the method works. But when a budget owner is weighing a six-week qual study against a dashboard an analytics team can stand up in three days, depth loses the argument unless someone in the room can translate it into a number that budget owner cares about. Research functions that can't make that translation are the ones facing the freezes GRIT is now tracking.
Agentic AI has arrived. Governance hasn't caught up. AI policy is the only formal decision role that fails to reach the top three priorities in any segment GRIT surveyed.
The Governance Gap Nobody Owns
That last point deserves more attention than it's getting. Nearly every segment of the industry has agreed on where agentic AI belongs in the workflow. Almost none of them have agreed on who owns the policy governing how it gets used. That's a real risk sitting underneath a real opportunity. Teams are shipping AI-assisted analysis into decks that reach the C-suite with no clear answer for who checked the model's work, what data it touched, or how a client would even know if something got it wrong. So who actually owns that risk on your team right now? If the honest answer is nobody, you're not alone, but you're exposed. The teams pulling ahead in GRIT's data aren't necessarily the ones using AI the most. They're the ones who built the guardrails before the agent started writing the report.
What Closing the Gap Actually Takes
None of this means research teams should try to become analytics teams. Depth is still the thing a rushed dashboard can't replace, and GRIT's own numbers back that up. But surviving this split means closing the operational gap: proving impact in numbers a budget owner recognizes, building quality control into the workflow instead of bolting it on afterward, and owning AI governance instead of waiting for someone else to write the policy. The teams that make it through this next stretch won't be the ones with the newest agent. They'll be the ones who can move at analytics speed without losing what made the research worth trusting in the first place. If your team can't show that yet, this year's budget review is the moment to ask what closing the gap would take, before someone else asks it for you. See how Quali-Fi brings qual and quant into one connected research program ->