The AI job grief piece was about what happens when old roles stop feeling stable. Sarah Gibbons, writing for Nielsen Norman Group, gets at the next question: what are we actually supposed to call the new work?
When someone says “AI design,” everyone in the room pictures something different.
One person is thinking about using AI to generate component variations for a design system. Another is designing a chat interface. A third is structuring data so an AI agent can parse it. A fourth is defining an LLM’s behavior.
They all fall under “AI design” but they are not the same work.
I see this in job postings, LinkedIn posts, and conference talks. Someone says, “We need to figure out our AI design strategy,” and every person at the table nods — while imagining a completely different thing. Six months later, everyone’s frustrated because the “AI-design initiative” was not what they expected.
The conversation around AI and design is forking. What used to be a single (admittedly vague) topic has split into at least four distinct orientations. Each one focuses on a different type of design work, sits in different organizational structures, and uses different definitions of what “good” looks like. Most teams are staffed for only one of these orientations, confused about which one they’re doing, or trying to dance between all of them without realizing it.
Gibbons’s agent-facing category moves the taxonomy beyond the human interface:
This one is going to feel like a departure from user experience. Stay with me.
In this type of work, you design content, data, or interactions that AI agents (not humans) will read, parse, or act on. You’re building the infrastructure that autonomous systems navigate. If AI agents are the self-driving cars, you’re designing the roads, the signage, and the lane markings. AI agents are your users.
That might mean structuring product data so a shopping agent can compare options on behalf of a user, writing instructions that an AI assistant will follow, or optimizing content for AI search and discovery instead of (or in addition to) human search and discovery.
Some of this infrastructure won’t even be human-readable. A road sign designed for AI-controlled vehicles might encode information in ways no human driver could parse — data transmitted via radio or embedded in nonvisible parts of the spectrum. The “user” of that sign is an agent, and the design constraints are entirely different.
This is the orientation most design teams are still ignoring, but mostly because many organizations don’t have anyone explicitly responsible for how AI agents experience their product. Which means it’s either not happening, or it’s happening by accident inside an engineering team with no design input.
Designing for AI agents matters because it involves design decisions. What data gets exposed? How is it structured? What can an agent do versus what requires human confirmation? These shape the end-user experience just as much as any interface, they’re just one layer removed.
She then ties that category shift to the market for design expertise:
Understanding which type of AI design you’re building expertise in matters because the market is moving fast. Right now, few designers have deep experience across multiple orientations. However, this window won’t stay open for long. Within a year, many more designers will have meaningful AI experience. Those who build depth in a specific direction now will have a significant advantage over those who stay broadly “AI-adjacent.”
Here’s where the field is right now:
Most designers today are using AI as a tool in their workflow. A growing number are designing AI products and features. Very few are designing for AI agents or designing the AI itself.
The demand for those last two is growing. The supply of designers who understand them barely exists. So, if you’re a designer, its an opportunity gap that’s widening.
