The new interface paradigm is here

Mike, a longtime tech industry and startup veteran with stints at Apple and Palm, compared the rise of conversational AI to the graphical user interface revolution of the 1980s and ’90s. Just as menus and icons displaced command lines, AI is now shifting us from clicks and taps to human-computer interaction via conversation.

“It’s not AI in general. It’s specifically the conversational interface in generative AI,” he explained. “We’re now increasingly talking with our computers instead of giving them commands by clicking on buttons and menus.”

This isn’t speculation about what might happen in 2026. It’s about recognizing what’s already in front of us in tools like ChatGPT and other AI-powered interfaces.

Why UX for AI is fundamentally different

The shift toward conversational AI doesn’t just require technical adaptation—it demands a full rethinking of UX design principles.

In traditional interfaces, users can visually explore menus or click around to learn what’s possible. In contrast, conversational interfaces start with a blank prompt, making discoverability a major challenge.

And there’s more: users bring human expectations into AI interactions. That means emotional nuance, personality, and tone matter more than ever.

“People react to a conversation with a computer the same way they react to a conversation with a human being,” Mike said. “They form sweeping emotional judgments like, ‘The bot was disrespectful’ or ‘The bot was pushy.’”

UX for AI means designing not just for functionality, but for credibility, empathy, and a brand-aligned personality. A support bot can’t sound like a sarcastic marketer. A financial assistant bot can’t be overly casual. And your brand voice must stay consistent across every customer interaction.

Designing AI with your brand in mind

Tone and personality aren’t fluff—they’re critical components of AI success. Mike emphasized the importance of aligning the AI personality with the company’s existing brand strategy.

He used the example of Jack in the Box, known for its snarky brand spokesperson. That works in marketing, but what happens when a customer is frustrated about a messed-up order and interacts with a bot that cracks jokes?

“If it talks like Jack, you’ve got serious problems,” he noted. “Most developers are not used to thinking in terms of brand personality. We’ve got to bring that stuff together.”

This is where collaboration between UX, product, marketing, and brand teams becomes essential. A disjointed voice can erode trust and confuse users—especially in AI-driven experiences where tone is magnified.

How to test AI experiences effectively

The good news? You don’t need to guess. User testing still applies—just with a few key updates for AI usability testing.

Mike outlined several ways teams can validate and refine their AI-driven customer experiences:

• Run usability tests where people perform tasks using tools like ChatGPT.
• Observe behavior patterns—some users ask for step-by-step help, while others provide detailed prompts up front.
• Evaluate for tone and emotion—ask users how the bot made them feel, not just whether it “worked.”
• Test for discoverability—can users figure out what the AI can do without a menu?

Mike’s advice is to “watch how they interact on different tasks and look for differences.” The diversity in how people use conversational tools is a goldmine for insight—and it helps surface opportunities for refinement before launching widely.

The risk of waiting too long

The tech industry moves fast, and AI adoption in business is accelerating. While many companies are treating AI as a feature to be bolted onto existing products, Mike warned that this mindset mirrors how outdated software tried (and failed) to adapt to past transitions.

“That’s like Lotus 1-2-3 trying to add menus after the fact,” he said. “You need to rethink the entire interaction paradigm.”

The companies that wait 18 months to act could be leapfrogged by startups that are already building with AI-first mindsets today. Even if your product or website isn’t traditionally “software,” the shift applies. If your business interacts with customers digitally, you’re part of the transformation.

“Any of us who go to market through online interactions, lots of different industries, we are creating software. Our websites are software,” Mike said. “For anybody who goes to market electronically, how do you market? How do you sell in an age when you're when your corporate spokesperson and your corporate sales agent is a bot? How does that infinitely adjustable, infinitely customizable, infinitely responsive bot that can have an ongoing conversation with every customer you know?

“On one hand, that is the dream of every marketer and salesperson I've ever met. On the other hand, we've got to actually implement it and we need to get cracking.”

Start small, but start now

Adapting to conversational AI doesn’t require a full product overhaul overnight. Mike encouraged teams to start by experimenting with how AI can serve their existing customer journeys.

A simple exercise: have users perform a task related to your product or service using ChatGPT. Watch how they phrase their requests. Note what the bot gets right, and what it misses.

Then, build on that with mockups, small prototypes, and iterative testing. The key is to start learning, adapting, and moving before the competitive pressure becomes overwhelming.

“There’s no right answer yet,” Mike said. “But you don’t want to wait until it becomes urgent.”

Final thoughts

The rise of conversational AI is more than a trend; it’s a signal that the rules of user experience design are evolving. Teams that embrace this shift, test deeply, and think holistically about tone, personality, and brand alignment will be best positioned to lead.

“There’s so much to do,” Mike said. “And it’s going to be really exciting and fun.”

Episode links: 

• Mike on LinkedIn
• Nathan Isaacs on LinkedIn
• Leveraging AI in UX design — On-demand webinar that explores how to treat AI as a strategic tool in UX, integrating AI‑driven capabilities into design workflows.
• Effective AI: how to choose the right generative AI features—and build them fast — On-demand webinar that offers practical advice on building conversational AI features and optimizing for emotion, brand tone, usability and discoverability.
• How to test AI: a practical guide for evaluating AI user experience and product design — covers how AI-enabled experiences differ from traditional UX, and offers methods to evaluate usability, emotional response, trust, and adoption.
• UX for AI: Designing intelligent experiences with Greg Nudelman — part of the Insights Unlocked series; discusses how UX must evolve in the AI era, focusing on content, context, data, and intelligent interaction rather than just visual design.