The Human-AI Research Workflow: Where Technology Helps and Human Expertise Matters

AI is supposed to make research faster. The more useful question is where it’s actually landing across the workflow: which tasks compress, which stay stubbornly human, and what that means for the value of your expertise.

We’ll walk a real research workflow stage by stage: drafting and reviewing a study, translating it into new markets, automatically coding open-ends and exploring statistical significance and verbatim responses, then reporting out. At each stage, we’ll show exactly what AI gives back and what a person still has to catch. 

aytm has been at the front of building AI into this workflow with Skipper and, soon, the Bridge, so we’ll ground the walkthrough in what that actually looks like in practice: one real, anonymized before-and-after.

Then we’ll look at where it’s heading: querying insight across your whole research library, and what it takes to open that up safely to the rest of your org.

You’ll leave with:

• A stage-by-stage map of where AI compresses a real research workflow (drafting, reviewing, translating, automatically coding, exploring statistical significance and verbatim responses, and reporting) and where a person still has to check the work
• A concrete answer to “what protects quality when AI drafts and reports”: source attribution and insight-card indexing, not a policy memo
• One real, anonymized before-and-after showing exactly which hours came back
• A clear picture of how the same standards extend past your desk to non-researchers in your org, without you reviewing every query yourself

What we’ll cover:

• Where AI is actually landing across the research workflow right now, and where it isn’t
• A worked walkthrough of drafting, reviewing, translating, automatically coding, exploring statistical significance and verbatim responses, and reporting: what AI changes at each stage and where a person stays in charge
• One real, anonymized before-and-after from a research team that’s already made this shift
• What’s next: querying insight across every study you’ve run, and what it takes to open that up safely to the rest of your org