Figma reveals how to measure AI’s impact on design – and why speed metrics are not enough
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Figma reveals how to measure AI’s impact on design – and why speed metrics are not enough
When a team implements AI, usually the first question is, “How much time have we saved?” The question is understandable, but weak. The quick first option can create more iterations, reduce the quality of the handoff, or shift the load to the review. That’s why Figma has described a broader approach: measuring not just personal productivity, but how collaboration, solutions and the product itself change.
This is not an independent study of the market: it was conducted and published by Figma itself, and some of the results are based on the self-esteem of participants. But the technique is useful as a template for internal validation, not as a ready-made proof that AI already brings an effect to each team.
What exactly does Figma measure
The approach is based on an index with a scale from 0 to 100. Participants are asked not only if it got faster, but also how much the AI changed the six parts of the work:
- objectives and priorities of the organization;
- personal productivity and daily processes;
- teamwork;
- types of projects that people take on;
- company products and services;
- toolkit.
This division is more useful than one KPI. For example, a prototype can be built faster, but if the team has not agreed on quality criteria and has not tested the script with users, speed becomes a false victory. And better collaboration can be more valuable than another percent of speed — especially in teams where designers, developers and managers make decisions together.
What the Figma report showed
In its 2026 report, Figma collected 8,403 survey responses and 639 quality interviews among designers, developers and product managers in ten markets. According to the company, 41% of respondents already believe that AI has markedly changed collaboration, up from 7% two years ago.
There is another shift: designers are more involved in development, and developers are more involved in design. Figma reports an increase in design participation from 21% to 41%, and developer participation in design tasks from 44% to 60%. This does not mean that the roles will disappear. Rather, a shared canvas, prototype, and working interface become a place to discuss and test a solution more quickly.
It is important not to turn these figures into a universal forecast. The selection, question formulation and context of Figma influence the outcome. For a particular product, it is more reliable to measure your own baseline and repeat the same measurement in a few weeks.
How Figma Used AI for Research
The most interesting fragment is not only the final index, but the process of analyzing the interview. Figma conducted 639 AI-moderated conversations, obtained about two million words of transcription and applied AI for thematic analysis.
There is a principle here that should be taken into any research practice: the conclusion should be traceable. Figma is not limited to a brief summary of the model. For each topic, the team could go back to the original snippets of the interview and check whose words it was based on.
It's more important than a beautiful dashboard. If an AI wrote “users are unhappy with onboarding,” but you can’t open up real answers and see context, that conclusion can’t be used for a product solution. AI does a good job of speeding up coding, grouping, and pattern finding; the responsibility for interpretation remains with the researcher and team.
Practical minimum for the design team
You don’t have to build your own Figma index to see if AI works. You can start with a short cycle on one recurring scenario – for example, preparing a screen version, design review or handoff.
- Figure the starting point. How long does the task take now, how many edits does it take and where ambiguity most often arises.
- Select one measurable effect. Do not “implement AI” but, for example, reduce the time for the first option without increasing the number of criticisms.
- Collect high-quality feedback. Ask designers and developers if it has become easier to communicate a solution, not just if the layout has appeared faster.
- ** Check the quality of the result.** Check the design with the implemented interface, states, availability and mobile version. A fast but untestable screen is not considered an improvement.
- Repeat the measurement in the same way. Only the same scenarios and criteria can be compared.
For the team, this turns the conversation about AI from a general “seems to be faster” impression into a testable hypothesis. And this protects against the common mistake of optimizing image production instead of quality.
Main conclusion
AI enhances design not when it generates more screens, but when it helps the team come to the right decision faster and maintain quality control. Therefore, it is useful to measure not only the speed of generation, but also the consistency between roles, the number of changes, the quality of implementation and confidence in the chosen direction.
Figma does not replace its own data, but asks the right question: has the way the team works together changed, and has it made the user and product better?
