It's easy to fall into the trap of confusing "output" with "impact." But as a researcher in a world of AI tools, that's a good way to devalue your work.
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As more designers, PMs, founders, and researchers themselves begin to use generative AI tools for research synthesis, it has many researchers wondering whether it's good enough to replace their work. The short answer is it's a lot like using AI for anything else: AI output is "adequate."
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AI is great at producing generic, average output. Average work may be perfectly fine to keep bringing home a paycheck, but if you don’t value it yourself, it will not be valued by others in the long term.
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From Seth Godin: "Once we see a pattern of AI getting tasks right, we're inclined to trust it more and more, verifying less often and moving on to tasks that don't meet these standards.”
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Should user researchers prepare to move into the role of “orchestrating and validating the work of AI systems” like OpenAI’s Deep Research?
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A perennial question for the tech field is: when will spatial computing finally “arrive”? The reality is spatial computing has, in fact, already arrived, many times over, and learning to recognize opportunities to lean into this trend will ultimately make every interface you design more human.
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With renewed attention to smart glasses thanks to Meta’s Orion, spatial computing is once again on people’s radar. AI’s natural interface capabilities and multimodality are proving to be powerful, leverageable tools for bringing information technology into the physical world.
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At Austin Tech Week 2024, companies from startups to Meta shared their design process for AI. These were the four principles they all agreed on.
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“For truly transformational innovation, markets are hard to size. And often the bigger question is whether people will actually use this.” This quote from Austin Tech Week rings very true with my experience in the world of emerging tech product innovation. Here’s what I do instead of calculating TAM.
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In 2020, I shared remote product discovery techniques for businesses trying to pivot during the pandemic. Before I could do that, I had to dismantle some common myths that prevent teams from conducting research in the first place.
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Emerging technology creates perplexing problems for user-centered design, such as: how do you take a user-centered approach when it’s too early to have a defined user? Does this mean you should throw out user-centered design, or choose your target user based solely on TAM?
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Last week I conducted in-person interviews with a high-risk participant pool. Normally I would have worn an N95 mask, but at least 1/3 of our participants also had moderate to severe hearing loss. So instead, I wore a transparent face mask to ensure we’d still be able to communicate. Here's what worked and what didn't.
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Observing how different generations interact with technology can be a surprisingly successful strategy for discovering new users to serve. In tech, we often have a blind spot when it comes to designing for users older or younger than ourselves. But broadening our perspective can reveal new opportunities for creating not just business value, but also meaningful changes in people’s lives.
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With all the hype around AI, it’s hard to recognize what issues are attention-worthy. So last week’s Rosenfeld Media community workshop on AI was a valuable opportunity to see what questions are top-of-mind for the UX community.
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AI is a technology with rippling systems-level effects. Collaborating across diverse disciplines is the only way to begin to understand the full implications of our AI design decisions. This is the focus of an upcoming community workshop I'll be moderating on artificial intelligence.
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In Rosenfeld Media’s upcoming Advancing Research community workshop on artificial intelligence, I’ll be talking with Rachael Dietkus, Nishanshi Shukla, and David Womack about what researchers and tech professionals can do to mitigate issues in AI tool use, from psychological harm to users, to damaging our knowledge bases.
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Friction is the #1 killer of research repositories. I recently spoke with insurance technology company Guidewire about strategies for keeping friction low and ensuring that research repositories provide enduring value for researchers and research consumers alike.
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Analysts argue we’re entering the “Trough of Disillusionment” for AI. That may be bad news for investors, but for product teams, it offers new opportunities to build products that solve more meaningful problems for their users.
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