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Where AI fits in packaging decisions
| Packaging decision | How AI can help | What still needs human or physical validation |
|---|---|---|
| Material development | Screen candidate structures for properties such as barrier performance. | Product protection, safety, manufacturability, cost and end-of-life performance. |
| Design and consumer insight | Analyze text, images, surveys and sales data; generate or refine design directions. | Whether the insight is sound and the resulting design is feasible and usable. |
| Shipping operations | Recommend package types and sizes using product and order information. | Damage rates, packing workflows, material use and total operating cost. |
These are decision-support roles, not guarantees. A model can narrow the options or surface patterns, but a packaging team must decide what to test and whether a proposed solution works across its actual products, markets and production conditions.
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How can AI help design sustainable packaging?
Screen material candidates before costly trials
Material discovery models can help researchers identify molecular structures that may have desired properties, such as barriers against oxygen or moisture. Nestlé and IBM Research described a generative AI tool that draws on known materials and uses a chemical language model alongside a regression transformer to relate molecular features to physical-chemical properties. The announced candidates target high-barrier performance against moisture, temperature changes and oxygen. Nestlé described cost, recyclability and functionality as considerations for future application; the announcement presents a development pathway, not proof that a new material is commercially available or has a lower lifecycle impact.
For packaging teams, the practical benefit is prioritization: computational screening may help focus laboratory work on promising candidates. It cannot replace tests of barrier performance, shelf life, food or product safety, production compatibility, or the economics of the complete pack.
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Model degradation and performance together
The European Commission’s E-OILÉ project describes work on biodegradable packaging for single-use oily food and cosmetic products. Its objectives include materials based on biopolyesters and polysaccharides, improved barrier and mechanical performance, and validation of performance, shelf life, safety and sustainability. AI and advanced modelling are part of the planned approach to predict degradation. These are project goals, not evidence that every proposed material has passed validation or will biodegrade in a particular local waste system.
“Bio-based” and “biodegradable” do not by themselves tell a buyer what happens to a pack after use. Assess the material composition, the conditions required for degradation, the available collection or treatment route, and whether the package protects the product for its intended shelf life.
Evaluate the whole package, not one attractive attribute
Compare candidate designs against the same decision criteria:
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- Material and cost: total material use, unit cost and relevant handling or production costs.
- Protection: barrier needs, damage risk and shelf life for the specific product.
- Safety: applicable food or product-contact requirements.
- End of life: whether the pack is recyclable in the markets where it is sold and under the collection conditions available there.
- Customer experience: opening, resealing, storage, portability and clarity of disposal instructions.
- Implementation: data quality, production-scale feasibility and the effort needed to change processes.
How can AI reduce packaging waste and shipping cost?
Right-size shipping packs without increasing damage
Machine-learning systems can use product and shipment data to recommend package formats and sizes. Amazon says its models help select efficient packaging, including for orders containing multiple items, with the aim of reducing empty space while delivering products without damage. This is an operational example reported by the company; it does not establish that AI alone caused every packaging or environmental result.
Amazon’s packaging page reports 5.4 million metric tons of packaging materials avoided in North America and Europe since 2015, and 28 million unique products certified for its Ships in Product Packaging program globally. It also reports that 11% of its packages globally shipped without additional Amazon packaging in 2025. These figures have different scopes and timeframes; none is a standalone measurement of AI’s causal effect.
For other shippers, the same logic suggests measuring more than box size. Track material consumed per shipment, void fill, pack time, damage and returns, and compare those results against a defined baseline. A smaller package that leads to more breakage or replacement shipments may not reduce total cost or waste.
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Use operational data to find material loss
AI can help identify patterns in production or fulfillment data that point to where material is lost, such as recurring trim waste or excessive void fill. The Consumer Goods Forum and Bain & Company’s January 2026 report summary says 40% of companies see AI as helpful for mapping where and why material loss occurs. That is a reported view of potential, not a measured reduction in waste from deployment.
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How can packaging design influence consumer purchase decisions?
AI can analyze survey responses, customer comments, sales information and images to surface preferences or generate design variations for testing. Berry Global’s June 2024 white paper describes natural-language processing, machine learning, image analysis, predictive analytics, automated design generation, material suggestions and trend monitoring as potential applications. The paper also stresses the need for people to frame useful questions and turn insights into feasible designs. These are supplier-described capabilities, not independent proof that AI-generated packaging improves sales.
The consumer question is broader than appearance: “How can packaging design influence consumer purchase decisions?” A package may earn attention, but it also has to work in the hand and at home. Test whether people can identify the product, open and reseal the pack, store or carry it, and understand its disposal instructions. Personalization is most useful when it serves a real customer need rather than adding complexity or material.
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Berry’s white paper cites an Ipsos 2018 survey in which 72% of Americans surveyed agreed that packaging design influences purchasing decisions. This is an older survey figure cited by a supplier in 2024; it is not a current estimate and does not show that AI caused purchase behavior.
A practical example of useful design
PepsiCo Design describes Doritos STAX as a durable, reclosable paperboard canister intended to offer convenience and be widely recyclable in the paper waste stream. The case reports a Kantar ranking among the top 10 2020 fast-moving consumer goods launches and first for category incremental sales within that group. It illustrates a consumer-centered packaging experience, not an AI-enabled design. The example also shows why claims about recyclability need to be understood in the context of material and available waste systems.
What does industry adoption data tell packaging leaders?
The Consumer Goods Forum and Bain & Company’s January 2026 report summary attributes three figures to its analysis: 30% of companies are already using AI across the packaging lifecycle; 70% see the greatest potential for AI in packaging design; and 40% see potential to map where and why material loss occurs. The report summary describes research drawing on expert interviews, case studies and market analysis. These figures represent reported adoption or perceived opportunity, not independently comparable proof of cost savings, environmental improvement or sales impact.
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Industry percentages, a consumer survey and company-reported operational figures answer different questions. Treat each as context for where to investigate, not as a common benchmark or a forecast of results for your own business.
How to pilot AI in a packaging program
- Choose a measurable problem. Define a specific target, such as reducing empty space in a shipment, identifying promising barrier materials, or improving ease of opening. Record the current baseline and relevant constraints.
- Assemble the right data. Check that product specifications, shipment dimensions, damage records, material properties or consumer feedback are accurate and representative of the products and markets in scope.
- Use AI to screen or generate options. Ask the model to identify candidates or patterns; retain traceable assumptions and let packaging specialists challenge the output.
- Test shortlisted options in the real context. Evaluate production feasibility, protection, safety, shelf life, customer use and end-of-life route as applicable. Test shipping changes against damage and returns, not just package dimensions.
- Compare total outcomes to the baseline. Include material, production and shipping costs alongside product loss, customer experience and disposal conditions. Scale only when the measured result holds under relevant operating conditions.
A sound business case separates what the model suggested from what the physical trial and operating data demonstrated. Cost reductions should be reported only against a defined baseline and scope; a promising simulation or design concept is not an achieved saving.
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