Weighing AI Strengths and Limitations in Creative Work

What AI Still Can’t Do in UX Research (YET!) In my HCI class, we discussed LLMs, and their expanding role in UXR. I had a mix of students from psychology and computer science, and the contrast in their perspectives was one of the most engaging parts of the discussion. While the CS students were excited by AI’s analytical power, the psychology students were quick to point out what gets lost when you remove the human element. That tension sparked an honest and nuanced conversation, one I think many UX teams are navigating right now. We often hear how AI can streamline processes and surface insights faster than ever before. But the real question is: where does AI stop, and where must human insight step in? That led us to areas where AI still can’t match human capabilities. For example, while AI can analyze transcripts or summarize sentiment, it doesn’t understand people. It won’t notice a pause before someone answers, frustration in a participant’s tone, or the subtle body language that signals confusion. These small cues are often where the richest insights emerge, and they’re simply not available to models trained on words alone. Creativity and innovation were another focus. AI can recombine existing ideas, but it can’t invent something fundamentally new or rethink a problem from the ground up. In UX, this matters. We’re not just validating existing solutions, we’re often trying to uncover unmet needs or explore new directions. That requires intuition, original thinking, and the ability to question the assumptions behind the product itself. When it comes to strategy and synthesis, the limitations become even more apparent. Sure, AI can cluster feedback or generate dashboards. But it can’t resolve contradictions, weigh trade-offs, or prioritize conflicting user needs against business goals. Those are messy, context-dependent decisions that rely on human judgment and experience. Another key gap is environmental awareness. AI doesn’t experience screen glare, background noise, or a laggy connection. It won’t notice how someone adjusts their behavior when a feature breaks or when a multitasking parent is using an app one-handed. These real-world conditions often reveal the most meaningful usability issues, things that can’t be spotted in clean, structured data alone. We also talked about trust. AI can confidently generate summaries or highlight patterns but it doesn’t know when it’s wrong. It can reproduce bias, oversimplify nuance and give the illusion of certainty. This is why human oversight isn’t just helpful, it’s necessary. Researchers need to validate, question, and interpret results, not just take them at face value. These are the kinds of conversations we need to be having as AI becomes more integrated into our research practice. AI is a powerful assistant, but it’s not a researcher. The most meaningful UX insights still come from empathy, critical thinking, and a deep understanding of context. And for now, those remain human strengths.

🎨 Can AI be truly creative—or just brilliantly combinational? This question hit me hard the other day when I was discussing with an artist. We’ve all seen AI generate jaw-dropping art, haunting music, and prose so beautiful it felt human. And yet… I can’t shake the feeling that it’s just the most sophisticated cut-and-paste machine in history. The numbers are fascinating: → 90% of creators say AI sparks new ideas — yet over 50% fear it’s making all ideas look the same. → Across 28 studies, AI matches human creativity… but when humans + AI work together, creativity jumps significantly. → AI can generate more ideas, but humans still win on originality and diversity. → With AI, writers boost novelty by 8% and usefulness by 9% — but risk creative convergence. → Creativity scholars call this “artificial creativity” — outputs that may be original and effective, but lack the self-actualization, emergence, and human context that define true creativity. It reminds me of the 4P and 6P theories of creativity: it’s not just the product that matters—it’s the person, the process, the environment. AI can simulate the product, but without human intent, the process feels hollow. It reminds me of the 6P theory of creativity: Creativity isn’t just about the output (product) — it’s also about the person creating it, the process they follow, and the environment they’re in. AI can generate an output, but it doesn’t have a lived experience, emotions, or intent, which are what give creativity meaning. In IRREPLACEABLE, we call this the “Creative Co-Pilot” approach: ✅ Let AI generate combinations at scale. ✅ Filter through our uniquely human ethics, emotions, and lived experience. ✅ Add intent—because meaning is what turns remixing into originality. For me, the future of creativity isn’t AI or human. It’s AI + human. One brings infinite combinations. The other brings meaning. 💬 So here’s my question to you: When AI “creates,” do you see true creativity… or something brilliant yet hollow without us? #AI #Creativity #Innovation #HumanPlusAI

The essay from Ted Chiang in The New Yorker explored his perspective on “Why A.I. isn’t going to make art” [1]. I found the article very thought-provoking, especially considering that some artists started utilizing #AI tools & techniques as part of their creative process. One of Chiang’s core arguments is the fact that ”#art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception […] the interrelationship between the large scale and the small scale is where the artistry lies.” His main critique is the perspective that “Generative A.I. appeals to people who think they can express themselves in a medium without actually working in that medium. But the creators of traditional novels, paintings, and films are drawn to those art forms because they see the unique expressive potential that each medium affords.” This essay prompted an interesting debate about the role of AI and what constitutes “Art”. Matteo Wong published a rebuttal in The Atlantic [2] and his position is best summarized by the statement that “humans are creative enough to make and even desire a space for generative AI”. He outlined several stages where #GenerativeAI could be helpful in the creative process, such as “role-playing a character or visualizing color schemes or, in its ‘hallucinations,’ offering a creative starting point." In other words, this technology can be useful as part of the creative process, yet “AI need not make art ex nihilo to be used to make artworks”. As I contemplated both sides of this debate, I noticed the latest essay from B. Earl [3], who summarized the issue very succinctly: “The question will always go back to #intention.” He further posits that “art is meant to evoke a feeling. There is something sacred in this, as the artist’s intention drives the creation into existence. The artist, be it visual, aural, or text, has taken their amalgamation of experiences (both lived and studied) and coupled it to their own spiritual being. In this new realm of generative AI, the only thing that exists is the studied amalgamation (trained data) — it is missing both the experiences and the soul. But with a human intention driving the creation, can this gathered data […] actually create meaningful art?” Chiang similarly mentioned that “[w]e are all products of what has come before us, but it’s by living our lives in interaction with others that we bring meaning into the world.” So, the unique experiences and intention of the artist – the human element – is what makes art meaningful. Or in the words of B. Earl, there are those “who will let inspiration be their guide and create art that taps into the spirit, activating others in ways they may have not yet been opened to see the world.“ [1] https://lnkd.in/gx-uzvZw [2] https://lnkd.in/gM6squXV [3] https://lnkd.in/gWveiPiM