Design Changing from Artifact-Production to Intent-Shaping

Summary: AI is not merely making designers faster at producing screens, prototypes, and research summaries. It is changing the object of design itself. The UX profession’s most valuable contribution stops being UI production and becomes the design of intent itself: defining what good means, encoding judgment into live systems, setting boundaries for agentic behavior, maintaining coherence across many parallel outputs, and preserving human purpose when software starts to act rather than merely wait for commands.

(All my images in this article were made with GPT-Images-2)

Except for die-hard holdouts in denial of the real world, most UX professionals understand that the profession has fundamentally changed as AI takes over lower-level tasks. Humans are left to orchestrate the AI, but will shortly stop performing traditional craft-related activities, such as UI design and user research. AI moves the design problem to a higher level of abstraction. The work is shifting from drawing artifacts to defining intent, taste, constraints, behavior, coherence, and responsibility across human-and-agent systems.

AI uplevels the responsibility of human UX professionals: they will not design the user interface, but the higher-level parts of the user experience.

The fundamental nature of the change in the UX profession crystallized for me during a recent workshop titled “Design Futures Assembly,” which Jeff Veen organized for a small group of UX leaders. Participants included the UX chiefs for most major AI labs, heads of UX for several major software vendors and Internet properties, a few UX thinkers like myself, and a couple of deans from top design schools. An impressive group.

The ground rules for the workshop were intended to encourage frank discussions and prohibited quoting anybody by name. Thus, I’m only naming the workshop organizer, Jeff Veen, who has had an illustrious career, including the founding director of interface design for WIRED magazine and its pioneering website HotWired (which interviewed me in 2000), UX Director at Google, Founder of Typekit, VP at Adobe, and even one of my invited speakers back when I ran a usability conference.

From Artifact to Intent

We know that computer–human interaction is becoming intent-driven, as AI shifts the user’s role from operator of a UI to supervisor of the AI doing the work. The exact same change is happening for UX design. Not really a surprise: why should design be different than any other job?

Users and designers both experience the same fundamental change in knowledge work: humans are no longer operating the computer to directly produce work; instead, we supervise the AI that’s doing the work.

UX is not exempt from the same abstraction shift that is reshaping every other knowledge profession. The difference between users and designers is that the intent itself becomes a designed object.

But there is an important distinction between intent as a user input and intent as a design goal. A prompt is not intent. A prompt is merely the user’s first rough attempt to externalize intent. Real intent includes the desired outcome, the unstated tradeoffs, the user’s tolerance for risk, the context in which the result will be used, the standards by which success will be judged, and the failure modes the user would find unacceptable.

A prompt is not the user’s true intent. We must dig deeper.

AI turns design from an artifact-production discipline into an intent-shaping discipline. The designer’s new job is not personally making every screen, but rather defining what good means, encoding that definition into systems, steering agents and collaborators toward it, and knowing when speed produces value rather than merely volume.

Design jobs will not be saved by defending old tasks. Decels who stick to the old ways will be unemployable in 5 years. Design jobs will be saved by taking responsibility for the new design material. AI will generate, prototype, code, adapt, personalize, conduct user research, and analyze the findings. The UXer’s job is to make sure all that new power has direction, coherence, and human purpose.

Chat is only one possible input/output pattern for AI use. It is not a complete product paradigm, and likely not the most important way we will be interfacing with AI in the future. The richer opportunity is to design systems where intelligence changes the shape of the interface, the sequence of work, the division of labor, and the user’s relationship to the task.

Chat was the first popular UI for AI, but is highly limiting, so we’ll need a richer world of AI UX.

Our new design problem is behavior design for systems. Designers are no longer specifying every path through a product. They are defining the rules, boundaries, tools, permissions, escalation points, and feedback loops that let humans and agents work together productively. That is a much larger design canvas than screen layout.

“Taste” vs. “Opinion” as Human Moats

A crucial part of providing human direction for AI involves drawing a sharp distinction between “taste” and “opinion.” Many designers mistakenly believe their refined taste will serve as a permanent moat against automation. This is a dangerous illusion. AI models are exceptional pattern matchers and will soon commoditize UI taste just as they have commoditized boilerplate code.

The true, durable high ground is opinion: a distinct, assertive point of view about what actually matters in the real world and what is genuinely worthy of a user’s increasingly scarce attention. When generating infinite product variants becomes practically free, the core skill shifts to editorial coherence. Success means knowing which of the million possible AI-generated iterations to ship, and which to aggressively undo, ensuring the product still feels like it was crafted by a single, deliberate mind rather than a committee of autonomous agents.

Opinion is our judgment according to our values. Taste will be AI’s domain. Opinion will be human. (My apologies to Danish artists P. S. Krøyer, Anna Ancher, and Michael Ancher for making their paintings the subject of this art critic’s opinions.)

The word “opinion” implies that there is no one truth, but that has always been the case for UX. Many solutions can all help users, and it has always been UX’s job to identify a solution that balances multiple stakeholders’ requirements with customer needs. The very fact that something is an opinion means that it’s well-suited for humans to defend, based on our evolutionary animal nature and inherent drive to impose our will on the world.

When production is scarce, organizations reward the person who can make the thing. When production becomes abundant, organizations need the person who can decide what is worth making. This is the editorial turn in design. The designer becomes the person who can say: this variant is fluent but wrong; this workflow is impressive but unnecessary; this automation is fast but disrespectful of the user’s need for control; this generated interface is attractive but has lost the company’s voice; this agent completed the task but taught the user nothing and left no recoverable trail.

The danger is not that AI will produce a few bad screens. It will certainly do that at first, but less so each year. In any case, bad screens are an old problem that we know how to defeat through user testing. The new danger is that AI will produce many adequate screens that all seem defensible in isolation and incoherent in aggregate. Mediocrity will arrive well-dressed. The designer’s role is to prevent the organization from drowning in plausible options.

This is why opinion becomes more valuable, not less. Taste chooses the best alternative design, and AI will do that just fine. Opinion chooses what deserves the user’s time. AI will become very good at taste, because much of taste is pattern matching over precedent. Opinion is harder. It requires a point of view about what matters, what should be avoided, what should be simplified, what should be refused, and what kind of product the organization is willing to become.

The Source of Truth Is Live Systems, Not Static Design Artifacts

AI also collapses the old separation between design mockup, prototype, and production. In the new world, designers increasingly work in or near the codebase, where prototypes are branches, where agents operate against real components, and where the design system becomes machine-readable infrastructure rather than a static library.

This changes the politics of design work. A Figma frame used to be interesting because it represented a suggested future. A working pull request, live branch, or cloud-deployed prototype is more persuasive because it is a future version of the product. Design communication becomes less about handoff and more about direct participation in the product substrate.

AI allows us to prototype a rapidly branching set of design ideas, all of which become live and can be tested with users.

The old unit of design work was the artifact: the sketch, the wireframe, the mockup, the prototype, the research report. The new unit of work is increasingly the workspace: a live environment that contains the code, components, prompt instructions, product requirements, research context, analytics, brand rules, agent roles, and critique history needed to move a product forward.

This is a major conceptual shift. A file is something you hand off. A workspace is something you inhabit. A file asks others to interpret your intention. A workspace carries intention forward, because the relevant context travels with the work. It lets a designer, engineer, content designer, product manager, and agent swarm operate on the same problem without reducing collaboration to a sequence of screenshots and Slack messages.

Store context in a single workspace that is shared among many AI tools and all human collaborators.

The design challenge is therefore no longer just the interface inside the product. It is also the interface around the work. How does a team see what the agents changed? How does it compare ten parallel explorations without choosing the flashiest one? How does it preserve rationale? How does it know which constraints were active when an output was generated? How does it recover when a promising branch goes wrong? How does it avoid two agents solving the same local problem in ways that create a global inconsistency?

This is where design operations and product design merge. The workflow surface becomes part of the product’s quality system. A poor workspace produces poor design, even when the individual outputs look good, because decisions lose their lineage. Future design maturity will depend on whether organizations can make intent, context, and critique persistent enough for both people and agents to use.

The traditional cross-functional handoff is dead. But while killing the handoff removes a major bottleneck, it also destroys the shared understanding that the friction of old processes naturally forced upon us. Today, a designer with an AI can bypass development, and a developer with an AI can bypass design. To prevent organizations from devolving into parallel chaos, the new collaborative primitive must become the shared, machine-readable workspace. These workspaces bundle an isolated copy of the live codebase with the team’s codified positions, such as brand voice, testing conventions, and accessibility rules, all version-controlled together. Only by explicitly encoding a team’s collective opinions into the very prompt layer of these operational workspaces can swarms of human and AI agents operate simultaneously without stepping on each other’s toes.

Making Design Intent into Machine Objects

The most important implication is not that “designers should code.” The deeper implication is that design intent must become operationally legible to machines. Design systems, brand rules, research insights, accessibility standards, content voice, and interaction principles need to be encoded in forms that agents can use. Otherwise, AI will faithfully accelerate incoherence.

The design system thus stops being a component library and becomes an operating system for taste. Tokens, components, and usage rules are only the visible layer. Underneath must be a deeper set of instructions about brand behavior, interaction philosophy, accessibility standards, motion logic, content tone, escalation patterns, and product judgment. The system must know not only which button to use, but when not to add a button at all.

This is a major upgrade in the status of design systems. In the pre-AI era, many design systems were partly theater: useful for consistency, but often bypassed under deadline pressure and treated as documentation rather than infrastructure. In the AI era, a weak design system becomes dangerous. It gives agents a bag of parts without a theory of assembly. The result is not chaos in the old sense, but a smoother, faster, more scalable form of inconsistency.

Machine-readable does not mean machine-owned. Humans still decide the principles. But principles that remain only in slide decks, critique conversations, or the memories of senior designers will not survive contact with agentic production. They must be encoded, versioned, tested, and updated. In other words, design taste needs an API.

This also suggests a new form of design debt. We used to accumulate design debt when teams shipped inconsistent components or patched over poor flows. Now we will accumulate intent debt: undocumented assumptions, vague brand guidance, missing escalation rules, untested agent permissions, and research insights that never become usable by the systems doing the work. Intent debt will be harder to see than visual inconsistency, but it will be more damaging because it compounds invisibly through every generated output.

Intent debt will accumulate unless we’re careful.

No Single AI Tool Stack

The sheer velocity of AI adoption has completely outpaced the structural maturity of organizations and educational institutions. The era of standardizing around a single, unified design software stack has ended, replaced by a chaotic “wild west” of bespoke internal micro-tools, parallel agent swarms, and constantly shifting workflows.

While individual practitioners are expanding their technical capabilities at an unprecedented rate, UX teams don’t have a shared understanding of their toolkit. This disconnect leaves teams to navigate the transition in highly unstructured and unpredictable ways.

AI is by no means a settled field: new tools emerge monthly, if not faster, as do upgrades to old AI tools, which change what they can do, and therefore how they should be used. It’s simply too early to mandate a single AI stack and standardize on a small set of enterprise licenses that everybody should use.

The absence of a canonical stack should not be viewed only as a procurement headache. It is evidence that the UX profession has entered an exploratory phase comparable to the early web, when the job had not yet hardened into stable titles, methods, or toolchains. In such periods, premature standardization ties you down. It protects the organization from confusion by protecting it from discovery.

However, “wild west” should not mean “anything goes.” The better model is marked trails through unstable territory. Organizations should allow many tools, but require shared reporting about what was attempted, what failed, what was learned, what risks appeared, and what should be reused. The standard should move up one level: not standard tools, but standard reflection.

This is a subtle but important leadership move. Leaders should not ask, “Which AI tool should every designer use?” They should ask, “How does our team metabolize new capabilities faster than our competitors, without turning every designer into a lonely tool scout?” The answer is a learning system: demos, critique sessions, shared playbooks, internal examples, reusable prompts, agent configurations, and a culture that treats experimentation as communal infrastructure rather than private advantage.

The winning UX organizations will not be those that guess the right tool six months earlier. They will be those that learn how to learn tools continuously.

Currently, many UX department heads at the workshop use similar solutions to AI tool overload: frequent team get-togethers, where people would demo new AI workflows they were experimenting with.

Such lunch-and-learn experience sharing is the right lightweight response for the current stage, but it should evolve into something more systematic. The goal is not to standardize prematurely on one tool stack. The goal is to standardize on what counts as learning. A mature UX organization should have a regular mechanism for asking: which workflows actually improved product quality, which merely increased output, which created hidden cleanup work, and which changed the designer’s judgment for the better?

Which new AI workflows improved the quality of the final output, and which didn’t? Finding out creates strong alpha at this moment where nobody knows.

Evaluation, therefore, becomes part of design. Teams will need AI-era critique rubrics, not just AI-era tools. These rubrics should ask whether an output preserves user agency, whether it reflects the product’s point of view, whether it reduces or increases cognitive load, whether it fits the user’s real context, whether it provides recoverability, whether it has an intelligible rationale, and whether the system would behave acceptably at scale.

We need new evaluation rubrics for AI designs to guide our human opinion on where to tell it to proceed and when to stop it.

Eventually, design critique will be partly automated, but it should not become fully automated. Automated critique can catch violations, compare against standards, and flag likely slop. Human critique must still ask whether the thing is worth doing. That distinction matters. Machines can increasingly enforce the rules, but people must keep revising the reasons.

Role Boundaries Dissolve Faster Than Organizations Can Absorb

Workshop discussions demonstrated a mismatch between practice and structure. Designers are using more tools, shipping more code, blurring into product and engineering work, and collaborating with agents as well as people. But performance metrics, compensation systems, career ladders, hiring criteria, and collaboration rituals have barely caught up.

This creates a strange organizational condition: individuals are moving quickly, while institutions are still calibrated for the previous workflow. Teams can now produce more variants, more prototypes, more code, and more experiments, but they often lack a shared operating model for deciding what counts as good work.

This calibration gap leads to a structural danger: measuring the impact of AI adoption through obsolete proxies. When designers and product managers are evaluated on the sheer volume of features shipped or pull requests merged, AI will gleefully help them hit those targets, masquerading metric inflation as genuine business value. Rewarding output volume in an era of infinite algorithmic generation actively incentivizes the production of digital slop. Organizations urgently need new performance frameworks that discard legacy productivity metrics. Career ladders must pivot to evaluate a practitioner’s ability to orchestrate multi-agent workflows, their editorial judgment in curating high-quality outputs, and their success in designing trust and seamless delegation between humans and intelligent systems. When AI provides infinite scale, the most valuable design skill becomes knowing when to say no.

Loneliness among UX worker bees is one of the most important side effects. AI makes individual designers more capable, but it also pulls work into private loops between one person and a machine. The old cross-functional friction was inefficient, but it created shared understanding. The new design organization must deliberately manufacture collaboration, critique, and shared taste, because they no longer happen automatically through handoff bottlenecks.

UX workers are experiencing loneliness as they are pivoting to judging work rather than creating it, which was usually more collaborative.

Once recognized, UX staff loneliness is solvable. UX leadership needs to create more communication, critique, and team-building, especially through in-person work sessions. The one thing humans can do that AI cannot is to be physically present: embodied animals in a shared room, reading each other’s hesitation, energy, humor, confusion, and commitment. Bringing these human animals together is not nostalgia. It is infrastructure for judgment.

The counterweight is not nostalgia for old tools. It is stronger grounding in process, culture, research, systems thinking, and human judgment. Designers will need more cultural fluency, not less; more ability to critique, not less; more understanding of users and organizations, not less. AI can generate screen designs, but it does not yet know what a suggested step means in a given culture, company, market, or human situation.

AI’s contextual knowledge will obviously grow, so its analysis of the interplay between UI design and organizations and culture will surpass that of humans in a few years. But humans still retain that raw animal superiority, making their contributions to decision-making meetings more valued than even the most accurate data from AI.

Coherence Is the New Scarcity

AI makes design production abundant, but UI abundance makes UX coherence scarce. When a product team could only produce a few design alternatives, coherence was partly protected by scarcity. The same small group of people saw most decisions. The same designers touched most surfaces. The same bottlenecks that slowed the work also forced some consistency.

AI removes those bottlenecks. That is good, but it also removes the accidental coordination those bottlenecks provided. Ten people and twenty agents can now create more work in a day than a traditional team might have created in a month. The risk is not only inconsistency in the visual layer. The deeper risk is a fractured product mind: each feature locally optimized, each flow plausible, each agent doing its assigned job, and the whole product feeling as if nobody is home.

This makes coherence a management problem, a design problem, and a cultural problem at the same time. Coherence is not sameness. A product can have variety, surprise, personality, and local adaptation while still feeling as if it has one mind behind it. Coherence comes from shared intent: a common view of the user, the product promise, the acceptable tradeoffs, the brand’s behavioral character, and the product’s theory of value.

In the AI era, the designer becomes a guardian of this product mind. That does not mean personally approving every output. That would recreate the old bottleneck in a more exhausting form. It means creating the conditions under which many contributors can make local decisions that still add up to a coherent whole.

Research Is Fuel for Agentic Systems

User research also changes when design moves from artifacts to intent. The traditional research deliverable was often a report, a deck, a journey map, or a set of recommendations. These artifacts were already lossy. They depended on busy product teams reading them, remembering them, believing them, and applying them later under deadline pressure.

In an AI-mediated design process, research must become operational context. Findings should not merely be summarized for humans. They should be structured so agents can use them: user goals, vocabulary, misconceptions, decision criteria, emotional triggers, accessibility needs, domain constraints, observed workarounds, and unresolved questions. A research repository that cannot be queried by agents will become as obsolete as a design system that cannot be used by code.

Once user research findings become part of the AI’s background repository, they can drive agent behavior.

This does not make researchers less important. It makes their judgment more important. AI can summarize transcripts, cluster observations, and generate plausible themes, and soon do so better than 90% of human user research staff. But the human’s distinctive contribution is converting research insights into business profits by driving change across the organization, despite inevitable resistance.

The goal is not faster research reports. The goal is a living model of users that improves the behavior of the product. When agents design, test, personalize, or act on the user’s behalf, they should be drawing on the best available understanding of users, not on generic assumptions scraped from the Internet.

The Future of UX

The next generation of UX professionals will not be saved by proving that humans can make better mockups than machines. That is a lost cause, as AI gets better every year while humans don’t. They will be saved by taking responsibility for the terms under which intelligence enters the product. That includes intent, permission, evidence, escalation, recoverability, coherence, culture, and taste. It includes knowing when speed is a gift and when speed is a way to avoid thinking.

The UX profession is not becoming smaller because it’s no longer protected by the old monopoly on designing good-looking artifacts. The design surface has expanded from the screen to the system, from the flow to the agent, from the component to the rule, from the prototype to the live branch, and from the handoff to the workspace. The work is harder to see because it is no longer conveniently contained inside a rectangle. But it is still design: the shaping of what people can do, understand, trust, and become through technology.

It’s OK not to sweat the small stuff anymore (let AI handle it!). Let’s embrace a broader view of the UX profession.

Our new goal: shape intent, don’t design low-level UI artifacts.