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AI is already part of real design and creative workflows—but in these 15 documented deployments, it is used mainly to generate options, adapt assets, visualize products, or support production. People still direct the work, make selections, refine outputs, and approve what is released. Most examples are described in vendor-published accounts, so they show how particular teams used AI, not proof that it improves every creative workflow.
15 examples of AI in design and creative work
The cases below span campaign development, brand systems, fashion, 3D production, film, and public activations. Their evidence varies: some accounts describe a workflow, while others report a specific output or performance figure. The percentages and output totals are attributed to the companies that published them.
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| Deployment | Workflow and output | AI’s role and human involvement | What the published account establishes |
|---|---|---|---|
| 1. IBM’s “Trust What You Create” campaign | Campaign visuals about AI risks, including a virtual fishbowl treatment for the Las Vegas Sphere during Adobe Summit 2024. | IBM used Adobe Firefly to make the message playful, inviting, and educational. Global executive creative officer Denise Zurilgen described the campaign’s intent; the account does not frame AI as the campaign’s decision-maker. | Adobe’s campaign account documents the use of Firefly and the Sphere activation. It is a campaign example, not a measured test of audience response. |
| 2. Studio Rx rebrand | Brand imagery for a healthcare marketing studio’s rebrand. | The studio used Firefly to create and refine assets, with its creative team directing the work and retaining its style. | Adobe reported that Studio Rx produced more than 20 assets in 10 days. Executive creative director Matte Hall said the finished images retained the artist’s personality, creative control, and style; this is a vendor-published case result. |
| 3. Paramount+ campaign for IF | Personalized digital drawings based on fan-submitted character descriptions. | Designers used Firefly to turn the descriptions into images while maintaining a consistent sketchbook look. | Adobe reported more than 70 images for the campaign. The account demonstrates personalized campaign production, not an independent assessment of image quality. |
| 4. Deloitte Digital’s Orb Foundry | Branded 3D orbs for Deloitte Digital’s visual identity. | A prompt-based tool generated orb designs; the resulting look was reviewed for brand approval. | Adobe describes custom output produced in seconds and approved for the brand. The account does not provide an independently measured time comparison. |
| 5. Adobe MAX 2024 identity | Abstract designs adapted into color and texture treatments for physical and digital surfaces, installations, and animations. | Adobe Brand Studio used Firefly to explore and apply visual treatments across formats. | Adobe’s account describes an adaptation workflow spanning event materials and motion content; it does not report a quantified productivity result. |
| 6. Coach handbag digital twins | Handbag concepts and digital twins based on Coach imagery. | Tapestry trained a Firefly Custom Model on proprietary Coach imagery. Adobe describes a test prompt for a shearling Tabby bag that generated an image resembling an existing product variation. | The case illustrates brand-specific image generation and product visualization. The cited account does not establish that the generated image itself became a manufactured product. |
| 7. Kate Spade fashion ideation | On-brand fashion concepts and variations, product twins, and self-serve content. | Tapestry says designers used Firefly Custom Models trained on Kate Spade patterns, textures, and archival details; Substance 3D extended concepts into product twins, and Adobe Express supported self-serve content. | Senior director of digital product creation J.J. Camara summarized the role as: “AI isn’t driving. It’s helping us navigate — getting us there faster.” This is Tapestry’s description of its process, not a general performance guarantee. |
| 8. Monks 3D texture production | Creation and revision of textured 3D assets using Substance 3D and related Adobe tools. | AI-enabled and 3D tools supported asset creation and versioning within a production workflow. | Adobe’s 2025 customer story reports 70% faster 3D asset creation and 96% faster versioning at Monks. These are vendor-reported, case-specific results and should not be generalized to other studios or projects. |
| 9. Pixel Arena digital outfits | Customizable 3D outfits for a Google and NBA multiplayer fan game. | Monks designers worked from prepared models and used Substance Painter to add fabric, texture, stitches, and color. | Adobe’s account describes how designers built game-ready visual variety. It does not report a measured efficiency outcome for this project. Associate motion director Andrea Arice said Substance 3D’s texturing and material-creation features made it “an indispensable tool” in the team’s toolkit. |
| 10. Momo Wang’s Sundance short | An animated dramedy produced through a mix of traditional craft and generative workflows. | Wang’s small team used Firefly and Creative Cloud tools within a broader production process. | Adobe presents the short as an example of generative tools being used in small-team production; the account does not isolate AI’s effect on production time or quality. |
| 11. Taryn O’Neill’s Sundance short | A hybrid live-action and generative-AI drama, from visual development through compositing and editing. | The team used Firefly Boards for visual development and collaboration, then Photoshop and Premiere for compositing and editing. O’Neill said Firefly supported concepting and world-building, while Photoshop helped refine and composite images, including AI visuals blended with live action. | Adobe’s account says the team retained creative control over characters, pacing, and emotional direction. This describes the production team’s experience rather than an independent evaluation. |
| 12. Canva Magic Studio | AI writing, design generation, and format adaptation inside Canva’s visual communication platform. | Canva combined OpenAI technology with its own design engine. | OpenAI’s 2023 customer story reported 5 billion Magic Studio uses at publication. This is a platform-reported usage total, not a measure of output quality, unique users, or business impact. |
| 13. Adobe Firefly Times Square takeover | A synchronized campaign across Times Square screens. | Firefly-generated images and videos formed part of a large-scale public display. | Adobe’s 2026 account reports 392 unique images and videos, 3.8 hours of content, more than 3 million rendered frames, and 20 terabytes of data. These figures describe the activation’s scale, not an independent productivity metric. |
| 14. Too Faced mascara campaign | A campaign for Ribbon Wrapped Lash Mascara, followed by use of Firefly for packaging previsualization and early storyboards. | The team used Firefly Video Model and Premiere Pro in the campaign workflow. | Adobe says the campaign was completed in 16 days and describes Firefly as becoming part of the creative process for subsequent work. The timeline is a case-specific vendor report, not a benchmark. |
| 15. Adobe and Dentsu’s Delhi out-of-home campaign | “India Go Big” visuals adapted for billboards, bus shelters, and other placements. | Distributed teams used Firefly Boards to exchange references and explore directions, then refined and adapted visuals in Photoshop. | Adobe’s account documents collaboration and placement adaptation. It does not provide a measured comparison with a non-AI workflow. |
Where AI fits in a creative workflow
Across the cases, AI appears at several different stages rather than replacing an entire creative process. In early exploration, teams generate concepts, variations, or visual directions. In production, they create campaign images, branded assets, or 3D textures. In adaptation, they extend a visual system to new formats, placements, or audiences. In finishing, people still refine, composite, edit, and approve the material.
The input matters too. Some workflows begin with prompts; others use fan descriptions, brand archives, proprietary product imagery, prepared 3D models, or live-action footage. Those inputs shape what a system can generate and what designers need to check. A brand-trained model, for example, is being used to explore within a visual archive—not to prove that every output is accurate, usable, or ready for production.
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What the examples say about creative control
Human direction is explicit in many of these accounts: teams choose the task, constrain the visual style, select and revise outputs, and approve finished work. That pattern is useful when evaluating a deployment, but the examples are mostly described by Adobe or OpenAI, often in customer stories. They are not independent evidence that every team retains the same degree of control or that AI improves the result.
- Look for a defined task. The clearest cases identify a bounded use such as generating variations, visualizing a product, texturing a 3D model, or adapting a campaign asset.
- Check the source material. A prompt, archival pattern library, product image collection, and live-action shot each create different constraints and review needs.
- Identify the approval point. A generated concept, a brand-approved asset, and a finished public campaign are different stages; an account should not imply that one automatically guarantees the next.
- Read performance numbers narrowly. A case-study percentage or output count belongs to the named project and publisher, unless comparable independent evidence is available.
What adoption figures do—and do not—show
Adobe’s 2026 research offers context about creative work in the United States, but it does not settle AI’s overall effect on employment. Adobe reported an 8% increase in US creative professional job postings between September 2025 and April 2026. Its research also included a survey of 1,433 creative professionals and creators, plus an 11-country study of 3,300 people training to enter creative work. The measured job-posting period and survey populations should not be treated as a universal labor-market forecast.
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In the same report, 84.8% of surveyed US creative professionals felt positively about AI, with particular positivity about brainstorming. Adobe also reported specific task use: 57.6% of US photo professionals said they used generative AI substantially to remove distracting background elements, 50.0% for image compositing, and 45.1% for color and tone retouching; 48.6% of US video creators reported substantial use for visual effects. These are Adobe-reported survey findings with stated US populations, not rates that can be assumed for all creative workers or regions.
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A useful case study should make clear what the people did, what the tool did, what went into the system, and what the finished output was. Claims about speed or scale need their own context: who reported the figure, which workflow it measured, and whether it compares against a defined baseline. A high volume of generated assets, for example, shows output volume; it does not by itself show better design, lower cost, or greater audience impact.
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These deployments establish that creative teams are using AI for specific parts of real work—from exploratory concepts to public-facing campaigns. They do not establish that one tool is best, that every output is suitable for release, or that the same workflow will deliver the same result elsewhere.
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