AI-Ready UX Documentation for Digital Product Owners
AI-ready UX documentation is becoming a concrete, buildable thing rather than an abstract idea: it means putting your design standards and research findings into files that live alongside your product’s code, where AI tools read them directly every time they generate a screen, a component, or a line of copy. Two examples already exist or are being actively proposed: DESIGN.md and UX.md.
In short: AI-ready UX documentation isn’t an abstract concept: it’s an actual practice already forming around files like DESIGN.md and UX.md, kept in the same repository as the code, that AI tools read directly every time they generate a screen, a component, or a line of copy.
Why does UX documentation need a second audience now?
For years, UX deliverables, wireframes, style guides, research findings, decision logs, were written with one reader in mind: the next human on the project. A new hire, a client, a developer picking up the work six months later. The reasoning behind a decision often lived in someone’s head, or in a Slack thread, rather than in the document itself.
The Nielsen Norman Group made this case in UX-Context Design: Using UX Knowledge to Inform AI-Generated Design, published in July 2026. Their argument: AI agents are now a genuine consumer of design documentation, and what they consume is context, not decks built to persuade a human stakeholder. We think it’s one of the more important shifts in B2B digital delivery this year, and one most organisations haven’t caught up with.
Where does AI-ready UX documentation actually live?
This is where AI-ready UX documentation stops being abstract. DESIGN.md grew out of Stitch, Google’s own AI design tool, and Google Labs published it as an open specification on GitHub, Apache-2.0 licensed, so any organisation can use or adapt it, not a proprietary in-house format. It’s still labelled alpha, currently at release 0.3.0, so treat it as an emerging convention rather than a finished standard. Adoption already looks real for something this new: over 26,000 stars and 2,100 forks on the repository.
A DESIGN.md file sits in the same repository as the product’s code and holds two layers: exact machine-readable tokens (colours, type sizes, spacing, corner radii) that tools can read precisely, and plain human-readable prose explaining what those values are for and how to apply them. There’s no handoff. AI tools read the file directly, every time they generate something.
Google also ships a command-line tool alongside the format: it lints a DESIGN.md file for accessibility failures like poor colour contrast, flags design tokens nobody is using, and exports the same tokens straight into a Tailwind config or the W3C Design Tokens standard. That’s a stronger signal than a blog post proposing an idea. Google built the plumbing to make the file actually drive a build, not just describe one.
Nielsen Norman Group has proposed a broader companion file they call UX.md, which is a hypothesis at this stage, not an established standard. The idea is that it would reference DESIGN.md for visual standards and point to where a product’s components actually live in the code, while also holding the parts of UX knowledge that DESIGN.md doesn’t cover:
- Research synthesis, written as constraints an AI can act on, not just insights in a report. “Users abandon setup when asked for information they don’t have on hand” becomes a limit on what gets generated, not a line in a slide.
- Interaction standards: when to confirm versus allow undo, how errors are worded, whether the product is built for experts or novices.
- A glossary of the words your users and domain actually use, so the AI doesn’t default to generic terminology.
- User and world models: who your users are, their expertise, and the real conditions they operate under, interrupted mid-task, under time pressure, bound by compliance rules.
For larger organisations, a single file isn’t necessarily enough. NNG suggests UX.md could serve as an index to a folder of context files, or the same knowledge could be delivered through MCP servers and agent skills instead of one document. The through-line in every version: this content lives with the product, is version-controlled alongside it, and is read fresh by AI tools each time they generate something, not consulted occasionally from a shared drive nobody opens.
What does AI-ready UX documentation actually look like?
| Documentation written for humans | Documentation written as AI context | |
|---|---|---|
| Decisions | Outcome is shown; reasoning is assumed or verbal | Reasoning and constraints are written down alongside the outcome |
| Structure | Loosely organised; relies on a shared mental model | Explicit relationships between rules, components and use cases |
| Edge cases | Covered informally, if at all, in conversation | Documented directly, so novel situations can be handled consistently |
| Ownership | Assumes a person will interpret and apply judgement | Built to be applied correctly without a person present |
| Location | Shared drive, wiki, or slide deck, separate from the product | Lives in the same repository as the code, next to what it describes |
Where should B2B teams start with AI-ready UX documentation?
You don’t need AI-ready UX documentation to be a finished, standardised practice before you start building it. NNG’s own advice is to start today: take a sample of the insights your research has already established, write them into a plain markdown file your team’s AI tools can actually see, and get your existing design system into a machine-readable state alongside it. That’s a rewrite of what already exists, not a new research programme.
To make that first step concrete, we’ve put together a sample UX.md template you can download and drop straight into your own repository. It follows the same structure NNG proposes: research findings written as constraints, interaction standards, a glossary, and models of your users and the conditions they work under. Fill in what your team already knows, delete the instructional comments, and commit it alongside your code so your AI tools can actually read it.
The commercial case is straightforward. Every hour an AI tool spends guessing at intent because that file doesn’t exist is an hour added back onto a human’s desk. Organisations that get a working version of this in place, even a rough one, will move faster through design and development cycles than those still keeping design standards in a slide deck the AI never sees.
If your organisation doesn’t have UX expertise in-house to build this out, that’s exactly the gap our User Experience Design service closes. And if you’re also weighing up how AI is reshaping other parts of your B2B brand and digital presence, our piece on AI brand foundations is a natural next read.
Frequently asked questions
What is DESIGN.md?
A real, open file format for describing a product’s visual identity to AI coding tools, grown out of Google’s Stitch design tool and published as an Apache-2.0 licensed specification on GitHub, so any organisation can use it. It’s still labelled alpha, meaning treat it as an emerging convention rather than a finished standard. It holds machine-readable design values, colours, type sizes, spacing, corner radii, alongside plain-language guidance on how to apply them, and it lives in the same repository as the product’s code.
What is UX.md?
A proposed companion file from Nielsen Norman Group, explicitly a hypothesis rather than an established standard. It would sit alongside DESIGN.md and carry the rest of UX knowledge: research findings stated as constraints, interaction standards, a glossary of the terms your users actually use, and models of who your users are and the conditions they work under.
Where should these files actually live?
In the same repository as your product’s code, not in a shared drive, wiki, or design tool that AI tools never read. For larger organisations, one file can index a folder of context files, or the same context can be delivered through MCP servers and agent skills instead.
Do we need to wait for UX.md to become an official standard before starting?
No. You don’t need to wait to start building AI-ready UX documentation: write your team’s established research insights into a plain markdown file your AI tools can see, and work on making your existing design system machine-readable. Both are things you can begin this month.
Key takeaways
- AI agents are now a real audience for UX documentation, not just a future consideration.
- DESIGN.md already exists: Google Labs open-sourced it in April 2026 as a real, machine-readable file for design values, kept in the codebase.
- UX.md is Nielsen Norman Group’s proposed companion file, a hypothesis, not yet a standard, for research findings, interaction rules, glossary, and user context.
- The place to start is building AI-ready UX documentation as a plain markdown file your AI tools can actually see, not another slide deck or shared drive document nobody opens.
Source: Tony Alicea, Nielsen Norman Group, 24 July 2026, UX-Context Design: Using UX Knowledge to Inform AI-Generated Design
