Why AI is reviving the Renaissance designer

For decades, the design industry drifted toward specialization. Researchers researched. Designers designed. Engineers engineered. Entire careers became increasingly narrow lanes of expertise.

Pedro remembers a different version of the industry.

“When I arrived in Europe,” he recalled, “I met communications people, architects, stage designers — people who became UX designers because they understood how humans move through spaces and experiences.”

Early UX practitioners were often hybrids by necessity. There were no formal interaction design degrees in the late 1990s. Teams assembled themselves from adjacent disciplines, borrowing methods wherever useful.

Over time, professionalization brought rigor, but also fragmentation. Product organizations became factories of specialists handing work downstream like relay runners passing batons.

AI may be quietly dismantling that model.

Today, a designer without formal research training can use AI to structure interview guides, summarize findings, or identify patterns in qualitative feedback. Researchers can prototype concepts without waiting for visual designers. Engineers can rapidly explore UX alternatives.

The walls between disciplines are becoming more permeable.

“I think AI is very good at expanding those capabilities,” Pedro said.

That expansion is not replacing expertise so much as widening participation. The modern product team increasingly resembles a jazz ensemble instead of an orchestra: less rigid hierarchy, more improvisation, more shared fluency across roles.

That flexibility could become one of the defining characteristics of customer-centric product development over the next decade.

The hidden danger of AI-generated certainty

The real risk of AI is not that it produces poor work. The risk is that it produces convincing work.

A prototype generated in an hour can look polished enough to bypass healthy skepticism. Leaders may mistake completeness for correctness. Teams may confuse velocity with validation.

That is why Pedro keeps returning to research.

“You need to know your subject matter,” he said. “At the end of the day, it’s humans using those applications. You’re not building an MCP bridge between two AIs. You’re building something for a human.”

The line lands because it cuts through the current AI discourse with unusual clarity. Much of the conversation around AI and user experience focuses on tools, automation, or productivity gains. Far less attention is paid to whether teams are still deeply connected to the humans behind the metrics.

Good UX research has always functioned like friction on an icy road. It slows momentum just enough to prevent catastrophic mistakes.

Without that friction, AI-powered design workflows risk becoming beautifully optimized systems for scaling mediocrity.

Pedro’s perspective feels particularly notable because he is not anti-AI. Quite the opposite. He actively experiments with AI agents, custom workflows, and automation systems. But he draws a hard line between assistance and abdication.

“The craft is going to come from you and your ability not to accept the first response that the AI tool gives you,” he explained.

That mindset may ultimately separate strong design teams from weak ones.

The future likely does not belong to organizations that use AI the most aggressively. It belongs to organizations that question AI most intelligently.

The global split in design culture

One of the more fascinating observations Pedro shared involved the growing divide between how different regions are adopting AI in design systems and product strategy.

In Latin America, he sees aggressive experimentation. Fewer regulatory constraints. Faster adoption curves. More willingness to improvise.

“We’ve always had this ask for forgiveness, not permission mentality,” he said with a laugh.

European teams, by contrast, tend to approach AI with greater caution, shaped partly by stricter regulations and more formal governance structures.

Neither approach is entirely right or wrong. They simply optimize for different risks.

But Pedro believes the Latin American design community is gaining momentum partly because of its comfort with experimentation. Teams are more willing to test unconventional workflows, embrace motion design, and push visual interaction boundaries.

He illustrated this philosophy with a story about public buses in Mexico.

Instead of functioning stop-request buttons, some buses use rubber chickens attached near the back doors. Riders squeeze the toy to signal the driver.

It is absurd. Functional. Memorable. Entirely human.

“That is a design decision,” Pedro said. “Very conscious.”

The story works as more than comic relief. It captures something many product teams lose as they scale: the willingness to solve problems creatively instead of conventionally.

Enterprise product development often sands off these rough edges in pursuit of consistency. AI could worsen that tendency by generating increasingly homogenized interfaces optimized around established patterns.

But innovation rarely emerges from perfect optimization. It usually arrives sideways, wearing the disguise of something slightly strange.

The return of soft skills

As AI becomes more technically capable, the most valuable leadership traits may become less technical.

Pedro repeatedly returned to qualities like curiosity, care, critical thinking, and communication. In other words: soft skills.

For years, many organizations treated soft skills as secondary competencies — nice to have, but difficult to quantify. AI may reverse that hierarchy.

If machines can produce outputs rapidly, human value shifts toward framing the right questions, navigating ambiguity, and exercising judgment under uncertainty.

“We need to provoke that in our teams,” Pedro said. “We need to understand those new workflows.”

This presents a challenge for current leaders, many of whom rose through traditional career ladders before AI transformed the nature of creative work. A design manager who built their career reviewing static mockups may now oversee teams generating interactive concepts at machine speed.

That leadership gap is real.

“How can I help them get better at using AI if I never did it?” Pedro asked.

It is a surprisingly vulnerable admission — and perhaps an increasingly common one across product organizations.

The managers who thrive in the next decade may not be the ones with the deepest technical expertise. They may be the ones most willing to keep learning publicly.

Why speed alone is no longer impressive

There is something almost adolescent about the current obsession with AI productivity metrics. Entire industries seem intoxicated by output volume: more concepts, more campaigns, more prototypes, more code.

But abundance changes value.

When everything becomes easier to produce, rarity shifts elsewhere.

In design, rarity is becoming intentionality.

Anyone can generate an interface now. Fewer people can generate clarity. Fewer still can create experiences that feel emotionally resonant, strategically coherent, and genuinely useful.

That is why Pedro’s definition of craft feels important.

“Craft is the care that people can put in a project,” he said.

Not perfectionism. Not aesthetics alone. Care.

Care for users. Care for context. Care for the consequences of what gets built.

The best design leaders understand that speed is not inherently virtuous. Sometimes the smartest strategic move is acceleration. Sometimes it is restraint.

Pedro described modern product development less like a rigid process and more like a rubber band that stretches differently depending on the project. Some initiatives demand rapid experimentation. Others require deliberate precision.

The organizations that survive the AI transition will likely be the ones capable of both.

Because eventually, the novelty of instant generation fades. The market stops rewarding teams simply for moving fast. And when that happens, companies rediscover an old truth hiding beneath the new technology:

People still remember how products make them feel.

Or, as Pedro put it near the end of the conversation: “AI should reason with you, not for you.”

Episode links

• Design confidence under pressure: making decisions you can defend — An on-demand webinar exploring how design leaders can balance AI-driven speed with evidence-based decision-making, research, and human insight. Especially relevant to Pedro Hernandez’s discussion around “craft,” defensible design decisions, and balancing rapid prototyping with quality.
• How to test AI experiences: a practical guide for evaluating AI user experience and product design — A practical guide focused on embedding UX research into AI product development workflows to ensure human-centered outcomes. Aligns closely with the episode’s themes around customer-centric product development and the importance of research in AI-assisted design.
• When everyone is designing: AI and the future of craft — This Insights Unlocked episode featuring Figma’s Andrew Hogan explores many of the same ideas discussed by Pedro Hernandez, including AI in product design, the future of craft, judgment in UX leadership, and how design teams are adapting to AI-powered workflows.
• The hidden risk of moving too fast with AI in product design — A blog post examining how AI accelerates product development while increasing the need for thoughtful judgment, intentionality, and human-centered design practices. Strongly connected to Pedro’s ideas around balancing speed, experimentation, and design craft.

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