Product packaging as a source of data: How to design with AI in mind

As technology evolves, product packaging has a new set of challenges to meet. It still needs to protect the product, stand out on the shelf, and communicate clearly with shoppers – but increasingly, it also needs to provide unambiguous visual information to algorithms. This article is designed to help brand managers, designers, and marketing agencies look at packaging through the lens of image recognition systems and optimize their designs to make them more AI-friendly.

NOTE: This is a specialist article aimed primarily at professionals involved in the product lifecycle, particularly the design of primary and secondary packaging displayed on retail shelves. You’ll find more examples and practical insights in our free e-book.

Designing packaging for products that end up on retail shelves has always required balancing multiple considerations: the product itself, brand identity, shelf visibility, consumer perception, manufacturing, logistics, sales, and regulatory requirements. Every decision about format, materials, typography, or information hierarchy involves trade-offs between these areas. As computer vision and image recognition become increasingly common in retail, another consideration is gaining importance: how packaging performs when analyzed automatically by image recognition systems.

Packaging is increasingly becoming an object of algorithmic analysis at different stages of its lifecycle. AI technologies can support design evaluation, quality control during manufacturing, analysis of in-store execution, as well as sorting and recycling processes. As automation advances, it becomes increasingly important for packaging to communicate clearly not only with people, but also with cameras, robots, and other systems that need to identify it, classify it, or use it as a source of data.

Artificial intelligence is now being used at various stages of the packaging lifecycle – from design and the detection of defective products during manufacturing to the analysis of in-store execution, waste sorting, and directing materials into the appropriate recycling streams. In this context, the way packaging communicates information not only to people, but also to image recognition systems, is becoming increasingly important.

Dr Monika Ratajczyk, Assistant Professor at the Institute of Management and Quality Sciences, Maria Curie-Skłodowska University (UMCS); market researcher specializing in packaging and consumer behavior

Evolution, not revolution

From a design perspective, this is neither a revolution nor an entirely new set of rules. Most of the factors that support reliable automated recognition have their counterparts in well-established design principles: thoughtful use of color, strong contrast, a clear information hierarchy, and distinctive visual cues.

What is new is the context – and the way it changes design priorities. A system such as eLeader Shelf Recognition AI, which analyzes in-store displays, does not work from a packshot or production artwork alone (or, to be precise, those assets are not enough to achieve the highest levels of product recognition accuracy). It works with photographs taken in actual stores. System “sees” a product surrounded by other packaging, under unpredictable lighting conditions, often partially obscured and displayed with varying degrees of consistency. It cannot move or rotate the product, nor can it look deeper into the shelf. As a result, packaging also needs to be considered in terms of whether it remains visually distinctive enough when presented in less-than-ideal conditions.

What does the system actually recognize?

An image recognition system can only interpret product packaging based on what is visible in the image. It has no knowledge of the detailed design brief, the brand guidelines, or the creative intent behind a particular design choice. To an image recognition model, packaging is a collection of attributes that allow it to match a visible object to a specific SKU: a distinctive graphic layout, color palette, shape, logo, variant identifier, or other visual cues.

What matters most, therefore, is the clarity of the features that distinguish one SKU from other variants. If a distinguishing feature is too subtle, lacks sufficient contrast, or is positioned where it can easily be obscured, it may no longer serve as a reliable identifier in a photograph. Ultimately, the system can only work with the information that is actually visible in the image and use it to identify the product unambiguously.

As AI systems for analyzing in-store product displays become more widespread, visual communication needs to be designed consciously so that it is emotionally engaging and appealing to people while remaining clear to technology. These two “ways of seeing” overlap in many areas and rely on similar elements, such as color, contrast, and typography. But they are not identical. Something that makes communication harder for people – such as information overload – is not necessarily a problem for AI. Conversely, algorithms do not understand context, emotion, or meaning.

Joanna Roszkowska, Studio Dn / neurodesign and packaging design expert

Brand consistency vs. variant differentiation

One of the more interesting design challenges in AI-friendly packaging is striking the right balance between consistency across a product range (such as Bebiko, Tymbark, or Cherrissimo) and making individual SKUs clearly distinguishable from one another (for example, Apple, Mango & Banana vs. Summer Fruit Salad).

A consistent layout, shared color palette, and coherent visual language help organize the portfolio and strengthen brand identity. At the same time, an image recognition system needs sufficiently distinctive and reliable features to tell variants apart – even when the image is captured under less-than-ideal conditions.

The right approach depends on the brand guidelines and product category. Variants can be differentiated through color, illustration, typography, iconography, structural design, or a combination of these elements. The key question is whether that differentiation remains clear outside the controlled environment of a design presentation: on the shelf, from a greater distance, under poor lighting, and alongside other products from the same range.

Packaging needs to be evaluated in its real-world shelf context

A product rarely appears on the shelf as an isolated object. Trays, cartons, shrink wrap, multipacks, and the way products are arranged can all affect which parts of the packaging remain visible. From the perspective of automated image analysis, primary and secondary packaging therefore form a single visual system.

In practice, this means that even a well-designed front panel can lose some of its identifying features once the product is placed in its intended shelf-ready packaging or display format. Sometimes a small adjustment to the position of a variant identifier, the extent of side printing, or the construction of the secondary packaging is enough to maintain clear identification without changing the core design concept.

Packaging materials also affect how much information is visible

Gloss, metallic finishes, transparency, and material flexibility are all part of the packaging design language and serve their own aesthetic and functional purposes. At the same time, they interact with light, camera angles, and product positioning. Glare can temporarily obscure part of a label, while a fold or crease can distort the proportions or legibility of a key design element.

From an AI perspective, what matters is not the use of a particular material in itself, but whether the product’s key identifying features remain recognizable under the conditions naturally encountered in stores. The same test can also be valuable from a consumer perspective: information that is difficult to see in a photograph may also be difficult for a shopper to pick up during a quick glance at the shelf.

AI-friendly as an additional functional test

The term “AI-friendly” is best approached pragmatically. It does not describe a particular aesthetic or a separate design methodology. Instead, think of it as an additional functional test: can the product still be identified unambiguously when it enters an environment where images are analyzed automatically?

This type of test can be particularly useful during a rebrand, when expanding a product portfolio, changing pack sizes, designing promotional editions, or introducing new secondary packaging. These are the moments when the set of features used to identify a product changes – and any unintended consequences may only become apparent once the new packaging reaches stores.

Why does this matter for the business?

SKU recognition is only the first step in a much broader analytical process. If the system confuses one variant with another or fails to identify a product, that error can carry through to reports on availability, share of shelf, listings, promotions, or pricing compliance. Packaging design is, of course, not solely responsible for the quality of this process. The AI model, the accuracy and freshness of the reference database, the way photographs are taken, and the conditions on the shelf all play a role. Packaging is, however, one of the factors that can affect the reliability of the final results – results that may ultimately inform strategic decisions and field activities.

As image recognition tools become increasingly common, it therefore makes sense to add a few simple scenarios to the standard packaging design validation process: test the product in its intended secondary packaging, place it alongside similar variants, check it under different lighting conditions, and photograph it from the perspective of a shopper in the aisle. The goal is not to design packaging for an algorithm. It is to identify, before launch, situations in which product recognition could become ambiguous. This can provide a meaningful competitive advantage for manufacturers, who benefit from higher-quality data, as well as for designers, who can offer packaging that is ready for the latest generation of retail technologies.