Artificial intelligence is reshaping how we think about products, services, and experiences. The excitement is palpable—and justified. AI opens doors to possibilities we're only beginning to explore, promising to transform how people interact with technology and solve problems.

But amid this enthusiasm lies a critical opportunity: to approach AI design with the maturity, nuance, and inclusivity that the moment demands. As designers, we have a responsibility to ensure that AI serves everyone, not just those who think and work like us.

The Designer's Blind Spot

One of the most fundamental principles in design is often the hardest to remember: you are not your user. This truth becomes even more critical when designing AI experiences. Just because we, as designers, get excited about a particular AI interaction doesn't mean it meets the diverse needs of real people living messy, complex lives.

People experience the world differently. They have varying technical capabilities, different cognitive preferences, and unique accessibility needs. Some thrive with voice interfaces, others need visual feedback. Some want AI to handle everything, while others prefer granular control. The challenge isn't choosing sides—it's designing systems that accommodate this beautiful diversity.

The Conversational AI Trap

Conversational AI represents one of the most compelling developments in recent years. The ability to simply talk to an AI agent and get things done feels almost magical. For many tasks and many people, this interface is genuinely transformative.

However, conversation alone isn't a silver bullet. Consider the principle of recognition over recall—it's easier to see something and be reminded of it than to remember it from scratch. Conversational interfaces, by their nature, can make it harder to maintain that visual context. You might lose track of what you've discussed or struggle to reference previous parts of the conversation.

This is why designers must think carefully about how content and data within conversational flows are visualized and how users maintain agency over that information. Can users easily see what the AI is working with? Can they edit, reorganize, or build upon the AI's output? The most effective conversational AI experiences often combine natural language interaction with visual representation and direct manipulation capabilities.

But we should think even more broadly than conversational interfaces. AI's true potential lies in how seamlessly it can integrate into people's lives—working in ambient ways that provide contextual value, or proactively detecting moments where it can augment an experience without requiring explicit interaction. Imagine AI that notices you're struggling with a task and quietly surfaces relevant information, or that adapts an interface based on your current context and needs.

Some tasks simply require visual information, data manipulation, or granular control over the process. This is why graphical user interfaces emerged in the first place, moving us beyond the command line era of MS-DOS. The challenge now is creating AI experiences that know when to stay invisible, when to surface contextual help, and when to offer direct conversational interaction.

Learning from Interface History

The evolution from command-line interfaces to graphical user interfaces teaches us something important about AI design. Command lines were powerful—they still are. Power users could accomplish incredible things by typing precise commands. But this approach had a fundamental limitation: not everyone is a power user, and not everyone wants to be.

Graphical interfaces succeeded because they made computing accessible to more people. They provided visual hooks, immediate feedback, and ways to interact with information on your own terms. You could see what you were working with and manipulate it directly.

As we design AI experiences, we risk recreating the same accessibility gap. If we assume everyone wants to hand over control to an AI agent, we may inadvertently exclude those who need or prefer more direct interaction with their tools and data.

The Inclusive AI Design Imperative

This moment calls for inclusive design thinking more than ever. We need to:

• Design for diversity of interaction preferences. Some people excel with conversational interfaces, others need visual layouts, and many benefit from hybrid approaches that combine the best of both worlds.
• Remember those on the margins: People with disabilities, those with limited technical experience, and users with complex or specialized needs shouldn't be afterthoughts. Their requirements often lead to better solutions for everyone.
• Preserve user agency: While AI can handle many tasks autonomously, people still need to feel in control of their experience. This means providing options, maintaining transparency, and allowing users to intervene when needed.
• Apply proven UX principles: The fundamentals of good design—like making relevant information visible when needed—don't disappear just because AI is involved. If anything, they become more important as systems grow more complex.

Finding the Right Interface for the Job

The goal isn't to choose between conversational AI and traditional interfaces—it's to thoughtfully match the interaction model to the task and the user. Some jobs genuinely benefit from natural language interaction. Others require visual manipulation of data. Many fall somewhere in between.

The most successful AI experiences will likely be those that seamlessly blend different interaction modes, allowing users to converse when that feels natural, visualize when they need to see information, and maintain control when the stakes are high.

The Path Forward

As we shape the future of AI experiences, we have an opportunity to do something remarkable: create systems that are not just powerful, but truly inclusive. This means designing beyond our own preferences and assumptions, testing with diverse users, and remaining humble about what we don't know.

The excitement around AI is warranted, but it should be tempered with the wisdom that comes from decades of learning about human-centered design. The most transformative AI experiences won't be those that showcase the latest technology—they'll be those that genuinely improve lives for the widest possible range of people.

In the end, good AI design is simply good design: it solves real problems for real people, making their lives easier and more productive while respecting their diverse needs and preferences. The fact that AI is involved doesn't change this fundamental truth—it only makes it more important to get right.