Does AI make creative and design work better?
No good evidence. Studies so far point to a gain, but there are too few or too weak to rate it.
We show no impact figure until at least two studies qualify.
There is not enough evidence to rate this yet.
Design tools with AI features and general assistants list about $10–$30 a user a month.
What the studies found
No impact figure yet. Fewer than two studies qualify, so under our rules we do not show how big the effect is. The studies we found are listed below, with what each measured.
How sure are we?
- Starting level2 of 5, from 3 studies graded A, B or C.
- Down one: old models0 of 3 studies show they used a model released since May 2025 (the 18-month window); the rest used older models or do not say which.
- Down one: not real workMost studies used lab or benchmark tasks rather than deployed work.
- Down one: mostly not peer-reviewedMost of the weight is on working papers or preprints, together with another problem above.
- Publication biasnot testable with fewer than 10 studies.
- Result0 of 5. There is not enough evidence to rate this yet.
Behind the average
- Designers new to GenAI (Fu et al. (2024)): Idea fluency and flexibility rose after the GenAI session (fluency 14.3 vs 12.5, p < .05); no change for experienced users.
- Native English speakers (Fu et al. (2024)): Reported lower relaxation in the GenAI session (p < .05).
- Product managers (non-designers) (Stewart et al. (2026)): Cumulative time fell 35% (SE 5:17, p < 0.0001), much more than for designers.
- Less inherently creative writers (low DAT score) (Doshi and Hauser (2024)): Novelty +6.3% with one idea and +10.7% with five; usefulness +5.5% and +11.5%.
- Most inherently creative writers (high DAT score) (Doshi and Hauser (2024)): Little effect on creativity.
How, where and why it works
1 of 4. AI offers starting ideas and drafts; individual work rates as more creative, but collective work becomes more alike.
2 of 4. Less naturally creative writers gained most; non-designers sped up most with a design tool.
1 of 4. Most studies are short lab tasks; one used a design tool in realistic product tasks.
0 of 4. No study counts cost.
What to do next
Measure your pilot →If you are trying it yourself
- Use AI for options and first drafts, then push for something it would not suggest.
If you are rolling it out
- Judge work with blind creative review, and watch for sameness across the team.
What it takes
- Most evidence comes from short lab tasks, not months of real creative work.
What can go wrong
- Stories written with AI ideas were more alike each other, reducing variety overall.
- One study was run by the company that makes the design tool tested.
The studies
| Study | Grade | Design | People | AI model (released) | Company role |
|---|---|---|---|---|---|
| Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking Yue Fu, Han Bin, Tony Zhou, Marx Wang, Yixin Chen, Zelia Gomes Da Costa Lai, Jacob O. Wobbrock, Alexis Hiniker, 2024. arXiv 2411.00168 (preprint) | C | RCT, lab or benchmark | 36 designers (94% under 30, 81% women, recruited via Slack/Discord, email lists and campus flyers), 105 eligible designs rated by 5 blind reviewers who were members of the client research group the ads promoted | DALL-E 2 via the ChatGPT web app (ChatGPT Plus) | none |
| Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows Remy Stewart, Olabode Anise, Andrew Hogan, Augustus Griffin, 2026. arXiv 2609.26725 (preprint) | C | RCT, realistic task | 100 professionals (50 product designers, 50 product managers), 25 per role per arm; recruited and moderated by MeasuringU | Figma Make (prompt-to-design LLM tool; underlying model not named) | vendor-run: All four authors are Figma employees testing Figma's own Figma Make |
| Generative AI enhances individual creativity but reduces the collective diversity of novel content Anil R. Doshi, Oliver P. Hauser, 2024. Science Advances 10 (journal) | C | RCT, lab or benchmark | 293 writers (UK Prolific participants); 600 evaluators; 3,519 evaluations | GPT-4 (OpenAI API) (Mar 2023) | none |
Studies we found but do not count
- Generative artificial intelligence, human creativity, and art (Zhou and Lee (2024), grade C). Its posting data run from January 2022 to June 2023, so most of the fieldwork comes before November 2022.
Does the rating hold up?
- Run without company-run studies2 studies: strength 0, verdict no good evidence, headline band gain.
- Run without working papers and preprints1 studies: strength 0, verdict no good evidence, headline band gain.
How up to date is this evidence?
Method →Evidence searched to 2 Nov 2026. Studies cover models released Mar 2023 to Mar 2023. Rating computed 5 Nov 2026 under Module 15 v0.3 (pilot).
How this page was made: AI research agents searched for studies and read each paper; a second pass checked every number on this page against the paper itself, with the quote and where it appears. The rating is computed by fixed rules, not chosen. No person outside AI Adoption has reviewed it yet. Editorial owner: AI Adoption editors. Spot a mistake? Corrections.
Disclosure: AI Adoption uses AI tools, including Claude from Anthropic, in its research and editorial work. No company pays for or has any say in a rating.