As artificial intelligence reshapes our digital environments, a critical question has emerged in UX and interface design: are we still designing for humans or increasingly for machines?
In the past, digital experiences were shaped exclusively by human users. The interfaces, language, visuals, and architecture of a site or product were all oriented toward human perception, cognition, and emotion. But with the widespread adoption of large language models (LLMs), recommendation engines, and generative tools, designers now face a new challenge: optimizing for systems that read, interpret, and sometimes surface our work before a person ever encounters it.
When the User Isn’t Human
Take SEO, for example. While search engines have always acted as intermediaries, LLMs take that one step further. These models not only crawl and index your content—they summarize it, rephrase it, and sometimes replace the user’s need to visit your site at all. Designing in this context is no longer just about visual clarity or usability. It’s about structuring information in a way that both humans and machines can process and retrieve meaning from.
- Headings must be semantically accurate, not just styled for visual impact.
- Alt text must serve screen readers and help machines understand imagery contextually.
- Metadata and schema markup have become first-class citizens of modern design still hidden, but powerful.
We’re not just designing for the screen anymore. We’re designing for the interpretation layer.
Language that Speaks Both Ways
Language is another space where dual-audience design has emerged. Microcopy, button labels, help text, and even navigation categories must now balance clarity for people with crawlability and coherence for machines.
Too vague, and the user is confused.
Too specific, and the LLM may overfit or truncate key nuance.
Too clever, and neither understands.
There’s a growing need for what could be called machine-legible content design: writing that’s structured, accessible, and semantically rich enough for a machine to parse, but still warm and human-centered.
This includes:
- Using consistent terminology for concepts across pages.
- Writing clear, descriptive alt and title tags.
- Prioritizing declarative language over poetic abstraction (especially for CTAs or product attributes).
It’s not about writing like a robot—it’s about writing for the robots, so they can better serve the humans.
The Risk of Optimization Drift
In AI-dominated environments, there’s a creeping risk of optimization drift. That is: when teams start designing primarily to please the algorithm, rather than the end-user.
It’s a subtle but dangerous shift. Search engines prioritize relevance and clarity, but when the algorithm becomes the ultimate stakeholder, human experience can degrade. You see this in interfaces cluttered with trending keywords, visual bloat for algorithmic ‘freshness,’ or pages structured for crawl rather than comprehension.
And as designers we must resist this drift. Optimization should serve the user, not the other way around.
Contextual UX in AI-Oriented Workflows
Designing for AI isn’t just about LLMs reading your site. Increasingly, users themselves are interacting with your product in AI-enhanced ways.
- They’re using voice assistants to search your content.
- They’re copying and pasting text into chatbots to summarize it.
- They’re asking AI to generate answers using your product data.
So we ask: is your experience designed for this new behavior? Can your interfaces support fast parsing, data extraction, and multi-device interaction?
Design must now account for:
- Copyability of text blocks (avoiding embedded images of text).
- Context clarity (making sure that pasted text holds meaning out of context).
- Interoperability (designing in a modular way that works across interfaces, including AI-powered assistants).
Human Experience Is Still the Priority
In all this, the answer isn’t to design for the machines. It’s to design with the understanding that machines are now intermediaries in the user’s experience. The goal remains the same: resonate with real people.
But that means designers must adopt a layered mindset:
• Visual hierarchy still matters—for human scanning.
• Content architecture still matters—for human logic.
• Emotional tone still matters—for human connection.
We’re just adding another layer: AI systems that interpret, restructure, or reframe those decisions.
The best interfaces today recognize this duality. They’re structured semantically but feel intuitive. They’re machine-readable, but unmistakably human in intent. They don’t chase trends—they pursue clarity.
Conclusion: Design for Interpretation, Not Interruption
In a world mediated by AI, we aren’t choosing between designing for people and designing for machines. We’re designing so that machines don’t get in the way of people.
That means writing with context. Structuring with meaning. Visualizing with care.
Because when machines understand us better, they can help others do the same.