From Interview Insights to Product Hypotheses: A Guide for HR Leaders

As an HR leader taking on product management responsibilities, you're sitting on a goldmine of information in qualitative user interviews, meetings, and “human operations”. But how do you transform those raw conversations into actionable hypotheses that drive meaningful improvements across your People Operations Team? This post will walk you through that journey in no time at all, using the incredible technology afforded to us with large language models (LLMs, or AI).

What kind of User Research do I mean?

I generally bucket my “User Research” into 4 main areas:

• Cyclical conversations: Exit interviews, onboarding feedback calls

• Regular all-company 1:1s: I have regular 1:1s with random members of our team every week (I try to get around the whole company each year) where I ask the exact same four questions. I also ask my team to do this and also ask their own four questions. I’ll give more info on the content of these in a future post.

• A/B and User Testing: Before sharing communications, policy updates, or big pieces of information, I will select a handful (5-10) of folks in our team and ask them to (camera on!) read the process/policy or go through the tool (performance reviews etc) so that I can get a feel for:

  • How they use the tool

  • What they understand

  • Anything else that may help our team independently interact with what we’re building

• Ad hoc meetings and calls: I often still use places like 1:1s, scheduled meetings, and anywhere else where information may be shared, and I can either tag these for specific review, or I may just include them in the search to see if it widens my perspective.

I hold all of these calls on Google Meet, and always have my Sana AI agent join. I will then group these meetings into Categories so that I can “interrogate” those categories like I would with GPT or Claude. From there, I can ask the meetings specific questions to get an understanding of:

• Common themes

• Significant challenges

• Areas for further exploration or research (which can then be added to the pool of User Research to broaden my understanding).