"What I've found is that it can genuinely speed up the mechanical side of research without watering down the rigor. But you have to be intentional about where you bring it in and where you don't."
"I had a choice: rethink how I work, or keep delivering insights after the decisions were already made. So I started experimenting with AI in my own workflow."
"Now, I use Claude to pull together structured lit reviews, surfacing academic work, industry reports and prior findings much faster than manual searching. I still do the hard part: layering in internal context, deciding what's relevant and identifying gaps that primary research needs to fill."
Research timelines must adapt to faster product development cycles. AI tools can efficiently handle mechanical research tasks—literature reviews, data organization, and initial drafting—without compromising rigor. However, AI should not replace human decision-making about what matters or which insights drive strategy. The key is intentional deployment: use AI for sorting and structuring information, while reserving human expertise for contextual analysis, gap identification, and stakeholder alignment. This approach enables research insights to arrive before product decisions finalize, rather than after.
#ai-in-research #research-methodology #product-development #human-ai-collaboration #research-efficiency
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