AI is changing custom packaging mainly by helping teams explore visual concepts, adapt brand-approved assets, and preview designs in 3D. Those uses can speed creative work, but they are not the same as generating a production-ready package or printing a unique design for every customer. Published examples show promising company workflows—not an industry-wide adoption rate or proven sales lift from personalized physical packaging.
What does AI do in custom packaging?
“Custom packaging” can mean several different things: a new package concept, market-specific variations on a brand design, or a package tailored to an individual customer. The documented examples are strongest in the first two areas. They show designers using generative tools to explore ideas and creative teams adapting branded assets; they do not establish that one-to-one printed packaging is common or that it improves sales.
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It also helps to separate generative design from digital visualization. Generative tools can help create or vary visual concepts. 3D software can let a team inspect a digital representation of a package before making a physical mockup. These steps can support design decisions, but neither alone demonstrates that a package is ready to manufacture.
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Concept exploration
Adobe’s June 2024 account of Mattel’s Barbie packaging work describes staff packaging designer Sal Velazquez using Firefly to explore concepts and express visual directions. Velazquez said the tool could portray a designer’s vision on “digital paper.” That is a designer’s description of concept work, not a controlled measure of quality, savings, or production performance. The account does not say that generated artwork was automatically ready for print.
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Brand-governed variations
In May 2025, The Coca-Cola Company announced Project Fizzion, a design-intelligence system co-developed with Adobe and using Firefly generative AI services. Coca-Cola said it was in a pilot phase at the time. The announcement describes a system that learns design intent and encodes it in a “StyleID,” applying brand rules as creative teams make variations for different formats, platforms, and markets.
This is a brand-asset workflow: it aims to help teams adapt creative material while keeping designers 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 productivity result or a packaging-production statistic. It does not describe tailoring a physical package to an individual shopper.
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Packaging-specific content production
Adobe’s 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 particular case. It is not evidence that all packaging teams will achieve the same speed, or that the figure measures customer response or manufacturing time.
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Digital visualization can help teams examine packaging concepts before they make physical mockups. In a 2022 case study, Adobe described Coca-Cola teams using Substance 3D Stager to visualize bottles, labels, and packaging, including 3D renders for brand ideation, design options, and virtual photo shoots. Adobe reported that teams created dozens of 3D designs in days rather than weeks and saved more than $200,000 in photography costs. Both figures are from Adobe’s account of this specific workflow; they are not independently audited, and the work was digital visualization, not generative AI.
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- Custom printing uses customer-supplied artwork.
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- The design is printed on the exterior; the interior remains white.
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- Produced to order in the United States using submitted artwork.
The case describes reducing reliance on physical mockups for early exploration and feedback. It does not establish that teams can skip print proofs, structural or manufacturing checks, or other production validation. Treat a convincing digital render as a way to review appearance—not as proof that a package will print, fit, protect its contents, or perform as intended.
How do the documented AI packaging workflows differ?
| Workflow | What it helps with | What the cited example establishes | What it does not establish |
|---|---|---|---|
| Generative concept exploration | Exploring visual directions for packaging | Adobe’s June 2024 account describes Barbie designers using Firefly for concept work. | That generated designs are production-ready or improve outcomes by a measured amount. |
| Brand-governed asset variation | Adapting creative assets across formats, platforms, and markets | Coca-Cola announced Project Fizzion as a pilot-phase system using Firefly and brand rules in May 2025. | That the pilot is a standard industry workflow or creates customer-specific printed packages. |
| 3D visualization | Reviewing digital package concepts and sharing feedback before some physical mockups | Adobe’s 2022 Coca-Cola case study describes 3D visualization of bottles, labels, and packaging. | That the workflow uses generative AI or replaces production validation. |
| Digital customer recommendations | Personalizing product discovery in an online store | Adobe’s Coca-Cola Store case study reports results for behavioral recommendations. | That recommendation results come from, or prove the impact of, personalized physical packaging. |
Does personalized packaging increase sales?
The cited evidence does not answer that question for packages printed with individual customer-specific designs. Adobe’s Coca-Cola Store case study reports a 117% increase in clicks and a 36% increase in revenue from one-to-one product recommendations, along with a 17% click-through rate for “Frequently Bought Together” recommendations. These are digital commerce metrics for recommendations based on shopper behavior and affinities—not results from personalized packaging.
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Personalized messages or designs on physical packages may be a conceivable use of customer data, but the available examples do not quantify its effect on purchases or customer experience. Digital recommendations and printed package customization should therefore be evaluated as separate strategies.
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What should a team verify before using AI for packaging?
- Define the job: Decide whether the goal is to explore concepts, make consistent campaign variations, or preview a package in 3D. The cited examples address different tasks.
- Keep brand decisions reviewable: A system that applies brand rules can support variation, but the Fizzion announcement describes a pilot and does not establish an autonomous approval process.
- Review the actual production deliverables: The concept and visualization examples do not establish that generated artwork or a 3D render is a validated manufacturing file. Have the relevant production and packaging specialists assess deliverables before use.
- Measure the result that matters: If the goal is faster creative work, track that workflow; if the goal is sales, measure the relevant customer and commerce outcomes. Do not treat case-study figures from another task as proof.
- Read case-study numbers in context: The cited results come from company or vendor accounts of particular workflows. They are useful examples, not independent benchmarks for the packaging industry.
How widespread is AI in packaging?
The cited examples establish that particular teams at Mattel, Coca-Cola, and Newell have used or piloted these approaches. They do not provide an independently established industry adoption rate. Nor do they establish quantified customer outcomes from one-to-one personalization printed directly on packages. The most supportable conclusion is that AI is being applied to parts of packaging-related creative work, while the extent and measured impact of broader adoption remain unclear.
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