aytm CRO Glenn Fleischman joined Insight Platforms for a live webinar on how AI is reshaping the research workflow—and where human expertise remains irreplaceable. Here's a recap of what he covered, with a link to the full recording below.
What is the human-AI research workflow?
The human-AI research workflow is how consumer insights teams divide execution tasks between AI tools and human researchers. AI handles the time-intensive mechanical work—survey design, data cleaning, and reporting—while researchers focus on the strategic, interpretive, and decision-driving work that requires judgment, curiosity, and context.
This isn't a future state. It's how leading insights teams are working today.
How much time does a quantitative study actually take?
According to aytm's research, a standard quantitative study takes roughly 40 hours of hands-on work from brief to boardroom-ready output. That breaks down roughly as:
- 4 hours: writing the brief and aligning with stakeholders
- 12 hours: questionnaire design and programming
- 8 hours: fielding management
- 16 hours: analysis and reporting
That last category—16 hours to turn raw data into actionable insights—is where teams consistently lose the most time. It's also where AI is having the biggest impact right now.
Where is AI compressing the research workflow?
Glenn walked through four areas where AI is meaningfully reducing execution time:
1. Use case templates and customization
When a team builds their research templates ahead of time—locking in how they like to ask questions, their brand standards, their stage-gate process—they can react to incoming requests much faster. AI helps configure and maintain those templates so the team doesn't start from scratch each time a business question lands.
2. Survey design and instrument building
Starting from a blank page every time is expensive. When AI can take the study objectives, the stim, and the company's past research preferences and draft a first-pass survey—cutting 8 to 12 hours of design time down to under an hour—that's a real change to how teams operate.
3. Data quality
Cleaning data after fielding is painful and time-consuming, especially when you can't be sure the underlying responses are trustworthy. The better approach is building quality controls across every stage: verifying panel members before they ever see a survey, designing instruments that don't drive dropout or speeding, and reviewing respondent-level quality automatically at the end. When that's done right, there's no cleanup phase.
4. Reporting
Turning a completed dataset into a narrative your stakeholders can act on is where a lot of research time goes. AI can pull the key insights to the surface, structure the report, and surface the "so what"—leaving researchers to review, refine, and add the strategic layer rather than building from a blank slide.
Will AI replace consumer insights professionals?
No. The webinar addressed this directly, and it's worth being clear about why.
The decisions that follow research—launching a product, committing capital, handling a PR crisis, presenting a business case to leadership—are still made by humans. No AI model is standing in a boardroom pitching a line extension. No LLM has been connected to a company's bank account and given authority to make a purchase independently.
What changes is where the research professional spends their time. The traits that matter most—critical thinking, curiosity, storytelling, empathy, integrity—are becoming more valuable, not less. Execution skills like survey programming matter less. The ability to work with AI tools, interpret their outputs, and turn data into decisions matters more.
What is aytm Bridge?
aytm Bridge is aytm's AI-native consumer intelligence platform, set to launch at TMRE in Denver in October 2026. It's designed to bring together the full research workflow—study design, fielding, data quality, and reporting—in one connected system, customized to how each company does research.
Inside Bridge, Skipper acts as an AI research co-pilot. It reads the study objectives and available stim, drafts the survey, monitors fielding, runs data quality through aytm's Data Centrifuge, and produces a structured report with insights surfaced automatically.
The key distinction is that Bridge is built around the team's own preferences, templates, and historical data—not generic defaults. A company's past studies, their stage-gate process, their preferred audiences, their quality standards—all of it will be configured into the platform so that every new study starts from institutional knowledge, not a blank page.
What's aytm's view on synthetic audiences?
Synthetic audiences have a role, but they don't replace human respondents—and aytm is direct about that.
The most promising near-term use case Glenn described is using statistically grouped consumer personas as a reporting interface: after fielding with real humans, aytm groups respondents into target consumer personas based on their answers and demographics. Researchers can then chat with those personas to explore the data more naturally. The underlying data is always human—the personas are a more accessible way to interact with it.
Wholesale replacement of human sample with synthetic data? Not there yet. The range of responses, the nuance, the genuine curiosity that shows up in human answers—it isn't replicated reliably enough to make that case.
How does aytm handle concerns about AI-generated research quality?
Two things matter here: transparency and built-in rigor.
On transparency: aytm surfaces the underlying data at every stage. Researchers can always see respondent-level responses, review flagged quality issues, and drill into the data behind any AI-generated insight. The AI doesn't replace human judgment—it gives researchers a faster starting point and a cleaner dataset to work with.
On rigor: the value of an AI-native platform is only real if it produces better quality outcomes, not just faster ones. aytm's position is that by building survey design best practices, data quality standards, and methodology into the platform itself, the average quality of studies goes up—not down. The researcher's role shifts to configuring those standards and reviewing the outputs, rather than re-executing every step manually.
Watch the full webinar here.
To learn more about Bridge or join the early access list, visit https://bridge.aytm.com
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