AI was supposed to hand research teams their time back. Instead, most report more work, not less. Here's what the 2026 GRIT report and new workload data reveal about the AI efficiency paradox, and why the fix isn't slowing adoption down.
Every research team running AI experiments right now is stumbling into the same trap, whether they've named it or not. They add AI to survey design, first-pass coding, or report drafting, expecting a chunk of the week back. Then the calendar fills right back up. Seventy seven percent of employees say AI increased their workload instead of shrinking it, according to Deloitte, and insights teams are living that stat in real time. The 2026 GRIT Insights Practice Report puts a name to what's happening: AI compresses individual research tasks, but it doesn't shrink the overall demand for insights. It expands it. That's the AI efficiency paradox, and it's already reshaping how research teams operate.
The Jevons Paradox Comes for Insights
The logic behind this is older than market research itself. In 1865, economist William Stanley Jevons noticed that more efficient coal engines didn't cut coal consumption. They increased it, because cheap energy made new uses of that energy worth pursuing. Insights teams are watching the same pattern play out on a much shorter timeline. When a competitive analysis that used to take two weeks takes two days, that speed doesn't sit idle. Stakeholders notice, and they start asking for more of it.
AI adoption inside businesses has moved fast enough to make this concrete. Ninety one percent of companies now use AI in at least one business function, up from 78% in 2024 and 55% in 2023. GRIT's research points to three tasks where agentic AI is already doing real work inside insights functions: analyzing data, updating reports, and preparing and integrating data. Those three tasks used to create natural pauses in a research calendar. Remove the pause, and the calendar doesn't get lighter. It gets busier.
Why Faster Turnaround Creates More Requests, Not Fewer
Once a stakeholder learns a question can get answered in two days instead of two weeks, they stop saving up questions. They start asking as things occur to them. One well-scoped study turns into five smaller ad hoc pulls, each one landing in a different part of an analyst's week. Total request volume climbs, and so does the time spent switching between them instead of doing the work that actually needs a trained researcher's judgment.
This isn't hypothetical. Seventy nine percent of organizations report real challenges adopting AI, up from the year before, and 54% of C-suite leaders admit the rollout is straining their company more than it's helping. Speed without a plan for managing the demand it creates doesn't feel like a win to the people doing the work. It feels like more work, arriving faster.
77% of employees say AI increased their workload instead of reducing it. Speed didn't buy anyone free time. It bought them more requests. (Deloitte, 2026)
Rigor Still Sets Its Own Pace
Not every part of a research project should move at AI speed, and pretending otherwise is where quality starts to slip. GRIT's own data captures the tension exactly: teams want methods that are faster and cheaper, but they still depend on deep human judgment to keep the work trustworthy. Data prep, first-pass coding, drafting a report outline: reasonable places to let AI move fast. Framing the right question, catching an anomaly before it becomes a headline finding, deciding what a result actually means for the business: not reasonable places to cut corners.
The failure mode is treating every step as equally compressible just because some of them are. A team that lets AI accelerate the parts that deserve it, while protecting the time interpretation and sense-checking actually require, ends up faster and more trustworthy. A team that applies speed evenly across the whole process usually ends up with neither.
What 'Efficient' Should Actually Mean
The fix isn't slowing AI adoption down. It's changing what gets measured as success. Turnaround time is an easy number to report up the chain, but it's the wrong one to chase on its own. A better measure is how much strategic work a team can take on once the repetitive parts stop eating the day, and whether the organization will actually let a research function set boundaries on which requests get the fast lane and which ones need the full process.
The teams handling this well aren't the ones with the most AI tooling. They're the ones willing to tell a stakeholder that the fourth ad hoc request of the day can wait, so the one that actually matters gets a real answer instead of a fast one. AI didn't take that discipline away. It just made it optional, and a lot of teams are quietly letting it go. See how Quali-Fi keeps AI-assisted research from outrunning good judgment ->