AI has become one of the loudest topics across all industries right now. In research, it’s excitement, mixed with anxiety and fatigue, as teams attempt to keep up with the technology’s rapid pace of growth.
In a recent Industry Spotlight webinar with VP of UX Research at Angi, Lauren Everett, she presented the idea that AI is not replacing researchers, rather it should be utilized as a “helpful intern.” Instead of asking whether AI will replace researchers, we should be asking what kinds of work should AI support, and what still requires human judgment?
Why AI feels so different
As Lauren pointed out, fear around new technology is not new. Writing, books, phones, movies, cars, and the internet all triggered panic in their early days. But AI does feel different for two reasons. First, it arrived in a world that is already noisy with social media and 24-hour commentary. Second, and most importantly, the adoption curve is currently much steeper. Teams are feeling a real pressure to become AI “experts” as soon as possible. Lauren used the S-curve of innovation to explain the pattern:
- At first, new technology feels like a threat
- Then it feels like magic
- Eventually, it becomes infrastructure. This last point is the one most people miss: infrastructure does not equal replacement.
AI is useful, but it still needs direction
The “intern” analogy works because it captures both the strengths and limitations of AI. Like a strong intern, AI is fast. It can help with summaries, first drafts, pattern recognition, transcription, desk research, and early structure. Lauren shared that she has used AI to compress a week of desk research into minutes. But also like an intern, AI lacks the things that matter most in strategic research work: context, judgment, accountability, and lived experience. You wouldn’t send an intern alone into the CEO’s office to deliver a high-stakes recommendation, the same way you shouldn’t feed a basic prompt to AI then immediately ship the report it generates to your stakeholder.
While there are many places where AI is genuinely helpful, there are also many areas where human expertise remains essential. The deeper you move into research, the more human judgment matters. Researchers still need to lead in the field, in synthesis, and in communication. AI cannot independently know which themes are meaningful, which tradeoffs matter, or which recommendations are actually right for the business. That’s the area for human judgement.
The skills that become more valuable for human researchers
The most important research skills are not becoming obsolete. They are becoming more valuable. Lauren highlighted three skillsets researchers must continue to strengthen:
- Empathy: noticing hesitation and contradiction
- Judgment: deciding what matters, what is ethical, and what is worth pursuing
- Clarity: turning information into meaning that teams can understand and act on
Research only matters if it helps an organization make better decisions, and that translation layer still belongs to human researchers.
AI-assisted work may look more creative on the surface, but it also tends to converge toward the middle. In Lauren’s framing: AI raises the floor, but narrows the ceiling on originality. That’s a useful warning for research teams that if you prompt your way through synthesis, insight development, and storytelling, you may save time in the short term, but you may also outsource the very work that creates differentiation.
What this means for research teams
The takeaway is not to resist AI; it’s to use it intentionally. Researchers should learn the tools, experiment early, and figure out where AI can remove tedious work, while remaining in charge of what cannot be delegated. When AI becomes infrastructure, the opportunity for researchers is to become the people who know how to direct it well. Every wave of new technology creates panic and inflated expectations. But the future of research may not be less human and actually demand more human judgment, clarity, and empathy.
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