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AI is changing custom packaging work in three distinct ways: helping designers explore concepts, helping brand teams create consistent variations, and supporting personalized digital customer experiences. Those uses are not interchangeable. Company examples show generative tools being used for packaging concepts and brand assets, while the cited personalization results concern online product recommendations—not individually printed packages.
How AI is changing packaging design
Generative AI can help turn a designer’s direction into visual concepts to discuss and refine. In an account published on 27 June 2024, Adobe described Mattel’s Barbie packaging designers using Firefly to explore packaging concepts. Barbie Staff Packaging Designer Sal Velazquez said the tool could help portray a vision on digital paper. That is a designer’s description of concept exploration, not a controlled test of design quality, production readiness, cost savings, or print results.
In practice, this makes generative tools useful earlier in the creative process: exploring visual directions, communicating an idea, and giving a team options to assess. The cited example does not establish that generated artwork can go straight to a printer or that AI can independently resolve structural, material, regulatory, or production requirements.
Three different AI-related packaging workflows
Packaging discussions often group several technologies together. The examples below show why it is useful to separate them:
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| Workflow | What it does | What the documented example establishes |
|---|---|---|
| Generative concept exploration | Creates visual directions from a designer’s prompt or intent. | Adobe’s June 2024 account describes Barbie packaging designers using Firefly to explore concepts. It does not report a controlled performance comparison or prove generated designs were production-ready. |
| Brand-governed asset variation | Applies brand rules to creative assets and helps teams make variations for different formats, campaigns, or markets. | The Coca-Cola Company announced Project Fizzion with Adobe in May 2025 as a pilot-phase design intelligence system using Firefly. Its announcement describes encoding creative intent in a “StyleID” and keeping designers in control. It does not establish the system’s present availability or independent performance. |
| 3D visualization and mockups | Lets teams inspect virtual product and packaging designs and share feedback before some physical mockup work. | Adobe’s 2022 Coca-Cola case study describes Substance 3D visualization for bottles, labels, and packaging. It is a digital design workflow, not generative AI, and does not show that manufacturing validation can be skipped. |
| Personalized digital recommendations | Uses customer behavior or affinities to tailor online product suggestions or communications. | Adobe’s Coca-Cola Store case study reports digital recommendation results. It is not evidence about individually personalized printed packaging. |
How brands can create variations while keeping control
Localization can require adapting brand assets across markets, formats, and channels. In its 15 May 2025 announcement, The Coca-Cola Company described Project Fizzion as a design intelligence system co-developed with Adobe and using Firefly generative AI services. The announcement said the system was embedded in Adobe Creative Cloud and in pilot phase at that time. It described learning from designers’ work and encoding creative intent so teams could produce brand-consistent variations, with designers remaining in control.
Coca-Cola said the system could help creative teams produce content “up to 10 times faster.” That is a company-reported claim in the pilot announcement—not an independently verified result, a general productivity benchmark, or a measured packaging-production outcome. The announcement describes campaign and brand-asset variation; it does not show one-to-one customer-specific packaging.
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What 3D packaging mockups can—and cannot—replace
Adobe’s case study, published 9 February 2022, describes Coca-Cola teams using Substance 3D Stager to visualize bottles, labels, and packaging. Teams used 3D renders for ideation, visual options, and virtual photo shoots, and could review designs without printing wraps for cans or commissioning physical mockups for those early exploration tasks.
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Adobe reported that the Coca-Cola teams created dozens of 3D designs in days rather than weeks and saved more than $200,000 in photography costs. These are vendor-reported, case-specific figures from 2022, not independently audited results or proof that generative AI produced the savings. Digital visualization can help with design review; the case does not establish that physical print proofs, structural engineering, material checks, or production validation are unnecessary.
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- Custom printing uses customer-supplied artwork.
- Includes 25 boxes measuring 6 x 4 x 2 inches.
- The design is printed on the exterior; the interior remains white.
- White corrugated cardboard with an uncoated matte surface.
- Produced to order in the United States using submitted artwork.
What the reported results do—and do not—show
- Paper Mate packaging: Adobe’s 12 June 2025 announcement says Newell used Firefly Custom Models for Paper Mate packaging and reports content production was 75% faster. This is a vendor-reported result for that case, not an industry benchmark or a measure of customer response.
- Coca-Cola Store recommendations: Adobe’s ecommerce case study reports a 117% increase in clicks and a 36% increase in revenue from one-to-one product recommendations; it also reports a 17% click-through rate for “Frequently Bought Together” recommendations. These results concern digital shopping recommendations, not custom-printed packages.
- Project Fizzion: Coca-Cola’s “up to 10 times faster” figure is a company claim in the system’s pilot announcement. It should not be read as an independently verified, packaging-specific result.
- 3D design work: Adobe’s Coca-Cola case study reports dozens of designs created in days rather than weeks and more than $200,000 saved in photography costs. The account describes 3D visualization, not a generative AI evaluation.
These examples come from company or vendor accounts. They illustrate specific workflows, but do not establish how widely AI is used across the packaging industry or provide independently verified industry-wide performance figures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI personalize the package each customer receives?
The cited examples do not establish quantified customer outcomes from one-to-one personalization printed directly on physical packages. Coca-Cola’s reported recommendation figures are from its online store, where product suggestions were tailored using shopper behavior and affinities. They cannot be used to claim that personalized packaging increases clicks or revenue.
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AI-assisted design and brand-asset systems may help teams make more variations, but that is different from selecting a unique package for each buyer based on customer data. Treat claims about individualized printed packaging and sales impact as unproven by these examples unless a source measures that specific use.
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What to check before using AI in a packaging workflow
- Set the task: Decide whether the goal is concept exploration, brand-consistent campaign variation, 3D review, or digital customer recommendations. A result from one workflow does not prove another will work.
- Keep brand decisions accountable: The Project Fizzion announcement describes designers remaining in control. Establish who approves brand consistency and final creative choices.
- Separate visualization from production approval: A digital render can support review, but the cited 3D case does not establish that it replaces package engineering, print proofs, or manufacturing checks.
- Ask what a performance figure measures: Check who reported it, when, for which team and workflow, and whether it is independently verified. A faster content-production claim is not automatically a faster package launch or better customer result.
- Measure the intended outcome: If the question is whether personalized packaging affects sales, measure the physical-package experience itself rather than applying ecommerce recommendation results to it.
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