AI Design Tools Need Direction, Not Decoration
2026-05-22 · 4 min read · Janaina Maia
AI design tools are entering a new phase. They are no longer only helping people create images. They are starting to sit inside the everyday surfaces where teams plan, critique, edit, collaborate, and ship.
Google announced Pics, an AI-powered design and image-generation app for Google Workspace. The promise is simple: people can generate social graphics, invitations, marketing materials, and mockups from text prompts, then edit individual elements by clicking on them or leaving comments, more like Google Docs than a specialist design tool.
At the same time, Figma is reportedly partnering with OpenAI and Anthropic to bring AI agents into the design canvas, so teams can generate, modify, and manage concepts using natural language. The interesting part is not that AI can make more screens or more assets. We already knew that. The interesting part is that design tools are becoming places where agents participate in the work.
The bottleneck is moving.
For many teams, the old bottleneck was production. Who can make the image, mockup, deck, component, or first visual direction? AI is reducing that friction quickly.
That does not mean design becomes less valuable. It means the value moves. When anyone can create ten visual options in a minute, the important work becomes knowing which option is strategically right, which one is misleading, which one is off-brand, which one will confuse users, and which one should never leave the room.
In other words: AI makes generation cheaper. It makes judgement more exposed.
Prompting is not the same as direction.
I worry about teams treating natural-language design tools as if the prompt itself is the design brief. It is not.
A good prompt can describe an output. A good direction explains why that output should exist, who it is for, what trade-offs matter, what constraints are non-negotiable, and what quality bar the work must meet. That distinction matters more when AI tools are embedded in collaborative products like Workspace and Figma, because the output will feel close to the real workflow.
If the team has weak intent, AI will help them make weak intent faster and prettier. That is not progress. That is decorative noise with better lighting.
Design leaders need to protect critique.
The risk is not only that AI tools produce bad visuals. The bigger risk is that teams skip the human critique because the output looks polished enough to move on.
Polish is dangerous in enterprise work. A neat mockup can hide a confused workflow. A confident generated concept can hide missing edge cases. A beautiful slide can hide an untested assumption. The more finished AI output looks, the more deliberately teams need to slow down at the review point.
For AI-assisted design work, I would want the product surface to support:
- Intent: what problem the design is solving, not just what visual style it should use.
- Alternatives: what other directions were considered and why they were rejected.
- Source context: which brand, research, product, or customer inputs shaped the output.
- Human edits: what the designer changed, approved, or challenged.
- Accountability: who owns the final decision before it ships.
The enterprise version is not Canva with more buttons.
For casual content, fast AI design generation is mostly a productivity story. For enterprise product teams, it becomes a governance story.
If an AI agent can modify product concepts, generate variants, touch a design system, or turn a rough idea into an artifact that engineers and stakeholders treat as real, the product needs boundaries. Which parts of the design system can the agent use? Can it invent new components? Can it suggest interaction patterns that violate accessibility or domain constraints? Where does a human need to approve before the work becomes part of delivery?
These are product design questions, not only platform questions.
My take.
The next generation of AI design tools will not be judged by how many assets they can generate. They will be judged by whether they help teams make better decisions.
Google Pics is interesting because it brings AI visual creation into the collaborative productivity layer. Figma's agent direction is interesting because it brings AI closer to the design canvas itself. Both point to the same future: design work will be less about manually producing every artifact and more about setting direction, critiquing options, governing patterns, and owning consequences.
That is good news for strong designers. It is uncomfortable news for teams that confuse making something with knowing what should be made.
AI can create the decoration. It cannot take responsibility for the direction. That remains the work.