Generative AI has quickly moved from speculative tech to everyday tool. It writes emails, generates images, answers questions, and even codes. But while the outputs have evolved at a staggering pace, something hasn’t quite kept up: how we actually interact with it. I’ve been reflecting on this gap—between capability and experience—and it’s clear we need a shift in focus. If generative AI is going to reach its full potential, we have to start rethinking the interfaces that connect humans to these systems. Because the real opportunity isn’t just in what AI can do — it’s in how we help people use it confidently, clearly, and effectively.
The Tech is Smart — The Interaction Isn’t
“We’re still designing like the AI is the main character. In reality, the user is.”
It’s fascinating how powerful these models have become. But often, using them feels like having a conversation with someone who forgets what you said 30 seconds ago. That’s not just a context window limitation—it’s a UX challenge. A challenge that calls for better continuity, memory cues, and scaffolding within the interface.
We’re still designing like the AI is the main character. In reality, the user is. Interfaces should be helping users manage long threads of thought, remember what matters, and build on past interactions. This means giving users tools to see conversation history, return to previous threads, or even label and group past interactions. We have to meet users where they are, not where the tech wants to be. We also need to acknowledge that real users come to these tools with mental models shaped by everything from email apps to search engines. The friction arises when AI interfaces behave inconsistently—sometimes chatty, sometimes task-focused, sometimes entirely unpredictable. Standardizing these behaviors and giving users a sense of continuity across tools could make the learning curve far less steep.
When Outputs Go Off-Track, Where’s the Recovery?
We’ve all seen generative AI generate things that feel… off. Maybe it misunderstood tone, missed nuance, or hallucinated something out of left field. What’s missing is a meaningful way for users to course-correct in the moment. Simple undo buttons or thumbs-down icons aren’t enough. We need smarter UI patterns that allow users to guide the AI more directly—without needing to start from scratch every time. Think branching suggestions, revision sliders, or editable parameters users can tweak without needing prompt engineering expertise. Recovery shouldn’t feel like punishment—it should feel like co-creation.
An opportunity here is in intent correction—not just fixing an output but understanding what the user actually wanted and offering choices that adapt. What if the interface asked clarifying questions when unsure or made alternative suggestions in real-time, the same way a smart colleague might?
Bias and Black Boxes
“Transparency can be a feature, not just a responsibility.“
Bias in AI isn’t just a data problem. It becomes a trust problem the moment it reaches the user. And trust is easy to lose, hard to earn. Right now, most interfaces do very little to help users understand why an output might be skewed—or what to do about it. What if users could toggle between “raw” and “filtered” results? Or receive contextual notes explaining why a certain suggestion was made? Interfaces could include brief, digestible insights into how the model reached its answer — without overwhelming the user with technical jargon.
Transparency can be a feature, not just a responsibility. Building trust means showing your work. And when AI fails, users deserve more than a shrug. Clear error messaging, paths to escalate or flag issues, and even explanations about how bias may have affected a particular output can go a long way in making users feel seen, respected, and safe.
Customization Shouldn’t Be a Hidden Skill
Some users want poetic prose. Others want bullet points. Most just want to avoid “regenerate response” fatigue. But giving feedback like “make it more concise” or “change the tone” still feels like a trick only power users know. Why not expose those controls up front? Think sliders, dropdowns, or prompt presets — UI elements that empower users instead of expecting them to know how to phrase their requests just right. These tools should work like design systems — intuitive, modular, and user-centered.
Let’s stop hiding the knobs and dials. Personalization should be visible, accessible, and enjoyable. More advanced personalization could even involve user profiles or intent modes—letting someone toggle between “brainstorm mode” and “production mode” so the AI tailors responses accordingly. We already do this in design tools with different workspaces—why not in AI interfaces?
Data Privacy Can’t Be an Afterthought
One of the most important—and least visible—parts of generative AI interfaces is how they handle sensitive user data. Especially in regulated industries or personal contexts, the stakes are too high for vague disclosures or buried terms. We need clear, embedded cues that tell people what’s being stored, what isn’t, and how their data is being protected. Trust isn’t built through a privacy policy—it’s built through interaction. Imagine if every prompt interaction had a privacy layer—one that allowed users to set session limits, redact certain inputs, or specify data usage preferences with a single click.
Privacy isn’t just a compliance issue—it’s a UX issue. And beyond just signaling safety, privacy affordances should also reinforce trust and control. Users shouldn’t have to dig through settings menus to find out how their data is used. Inline indicators, contextual opt-ins, and real-time privacy controls could bring much-needed clarity.
Design is the Missing Layer of Intelligence
“Design is what connects capability to confidence. It’s what turns complexity into clarity.“
At the end of the day, generative AI doesn’t live in isolation—it lives in products. And product design is where we shape how people experience its power. Models will improve, but experience must evolve in tandem.
Design is what connects capability to confidence. It’s what turns complexity into clarity. And most importantly, it’s what determines whether users feel overwhelmed by AI—or empowered by it.
We often celebrate model updates or increased token limits. But the next great leap may come not from bigger models, but from better-designed pathways for collaboration between human and machine. We need UX and product design teams at the center of this evolution—not just as an afterthought. The next wave of innovation isn’t just about what AI can do. It’s about how well we help people work with it. That’s where design becomes the real differentiator.
