How to Use Generative AI in PIM Systems: A Practical Guide

Artificial intelligence is no longer a topic of the future - it's the present that's changing how we manage product information. I won't try to convince you to use AI in PIM systems - that discussion is behind us. Instead, I'll show specific examples and inspirations for how you can use AI to improve your daily work with product data.

In this article, we'll focus mainly on using large language models (LLMs). While machine learning on your own data can yield great results, it requires access to high-quality training data that most companies simply don't have. Therefore, we'll primarily look at solutions based on generative models that are readily available and can significantly improve work with PIM systems.

Text data generation

Generating Multiple Descriptions for Different Uses

Problem

The same product requires different descriptions depending on where it's published. A technical description on a website differs from those on marketplaces, in PDF materials, or on social media. Each channel has its own requirements for length, style, and content. Manually creating all these versions for a large number of products is impractical.

AI solution

AI generates different versions of descriptions based on technical parameters and a reference description, adapting them to specific channel requirements. You can define templates that consider the specifics of each platform (e.g., Amazon requires shorter texts emphasizing benefits, while websites can contain more technical details), and AI automatically creates appropriate versions, maintaining consistency of information while varying their presentation.

Scaling content across multiple languages

Problem

A company enters new markets, but translating product descriptions into 5 new languages exceeds the team's capabilities.

AI solution

LLMs process product content almost instantly, meaning the entire product catalog can be ready for international expansion within hours, not weeks. The cost is multiple times lower than traditional translations, and the quality - thanks to built-in technical terminology verification mechanisms - matches the work of the best specialized translators. The system not only translates but adapts style and content to each market's specifics - for German audiences, it emphasizes technical parameters, in Italy highlights aesthetic values, and in Scandinavian countries accentuates sustainable development.

In addition to cultural adaptation, LLMs automatically convert units of measurement, adjust formatting, and optimize content for local e-commerce platforms while maintaining complete accuracy of technical data. The automated process allows for instant reaction to catalog changes - new products can be available in all markets on the same day, with minimal team effort.

Different descriptions for different audiences

Problem

The same product must be described differently for different customer segments - professionals expect detailed specifications, while individual customers want to understand basic benefits and applications.

AI solution

LLMs create personalized versions of descriptions from the same source data in seconds, eliminating the need for manual writing of multiple text versions. For professionals, it automatically generates detailed technical specifications, information about advanced features and configuration options, maintaining professional terminology. At the same time, for individual customers, it creates accessible descriptions focused on everyday applications and benefits, using understandable language and practical examples. Each description version maintains the same factual basis, differing only in the level of detail and way information is presented.

Seasonal product content

Problem

Manual updating of product descriptions for seasonality is practically impossible with large catalogs - changing the context for thousands of products several times a year requires too many resources.

AI solution

LLMs dynamically adapt product content to the current season, using the same basic data but changing context and emphasized features. The process is fully automated and can be scheduled - the system automatically detects which products require seasonal adaptation and generates new description versions. For example, the same jacket in winter is presented in terms of thermal properties and frost protection, in spring as light rain protection, and in autumn as a versatile solution for variable weather. Updates can be implemented gradually or en masse, with the ability to automatically schedule changes in advance.

Managing parameters and technical data

Identifying Missing Attribute Values

Problem

Products often contain incomplete data, leading to catalog errors, filtering problems, and difficulties in sales processes. Manually checking each product for missing attributes is time-consuming and unfeasible with large datasets. These gaps can result from inconsistent data entry standards, differences in information sources, or simple human errors. As a result, PIM system users must rely on manual processes to fill in gaps, reducing the efficiency of product information management.

AI solution

The AI model analyzes product descriptions and their existing attributes, comparing them with other products in the same category. If a particular attribute appears in most similar products but is missing in a new entry, the system automatically detects this gap and marks the product as requiring completion. Additionally, AI can process description text and suggest attribute values based on it – for example, if the description contains information about the type of bulb, but the "bulb type" field is empty, the system will propose filling it in.

In addition to identifying missing information, AI can classify gaps by importance, flagging those that have the greatest impact on data quality. PIM system users receive suggestions for completing attributes in the form of reports or automatic notifications, allowing for quick and efficient data completion without the need for manual catalog searching. This improves the completeness and consistency of product data, making product information management more efficient.

Automatic completion of technical data gaps

Problem

Products often have incomplete technical specifications, and manually filling them is time-consuming and error-prone. In many cases, missing values are available in technical documentation, user manuals, or on manufacturer websites, but manually searching for and entering them into the PIM system requires significant effort.

AI solution

AI not only detects missing values but can also automatically fill them based on analysis of technical documentation, manufacturer specifications, and available online sources. Using data extraction tools like getName.ai, AI scans existing descriptions, manufacturer websites, or online documents and identifies key information, assigning it to appropriate product attributes. If the system finds a confirmed value, it can automatically add it to the database, minimizing the need for manual editing.

Unlike gap identification, which only signals the problem, automatic completion allows for direct data enrichment without user intervention. AI can also apply validation mechanisms, comparing found values with other sources to ensure consistency. This makes the PIM system more autonomous and the product data management process faster and less prone to errors.

Automatic error correction in data

Problem

Typos, errors in technical data, and incorrect values often appear in product catalogs, leading to inconsistencies and hindering proper search and filtering. Incorrectly entered units, random characters, or transposed digits can result in incorrect specifications that mislead customers. Manual detection of such errors is time-consuming and inefficient, especially in large databases where minor discrepancies can easily go unnoticed. Examples include incorrect values such as a TV weighing 500 kg or a light bulb with 5000W power.

AI solution

The language model (LLM) analyzes attribute values, detecting unusual entries based on context and relationships between data. Thanks to natural language understanding, AI can recognize typos, incorrect units of measurement, and inconsistencies in descriptions. It can also suggest correct values based on analysis of similar products and their descriptions. If the system encounters a suspicious value, it marks it for verification by the user or suggests a correction, e.g., correcting "500kg" to "50kg" in the context of typical TV weights.

Unlike static validation rules, the LLM model can recognize errors in the context of the entire product description, not just individual values. It can analyze relationships between attributes – if a bulb's power significantly deviates from standard values in that category, AI will suggest its correction. This allows PIM administrators to eliminate errors more quickly, improving data quality without the need for manual verification of each value.

Digital Asset Management (DAM)

Generating metadata for images

Problem

Lack of appropriate tags and descriptions for thousands of product images makes their effective cataloging, searching, and reuse difficult. In PIM systems with DAM (Digital Asset Management) modules, images are a key element of product data management, but manually describing them is time-consuming and prone to inconsistencies.

AI solution

The language model (LLM) supported by image analysis can automatically generate detailed metadata for images, describing their key visual features. AI analyzes the image and assigns appropriate tags regarding color, style, shot type, background, and other relevant elements. For example, a product image can be tagged with "white shirt", "mannequin shot", "neutral background", facilitating later search and categorization.

Unlike manual image description, AI ensures consistency in tagging and enables mass processing of thousands of files in a short time. Additionally, the model can adapt metadata to specific company needs, e.g., according to SEO guidelines or requirements of different e-commerce platforms. Automatic generation of visual descriptions significantly improves digital asset management and streamlines product material publication processes.

Image compliance verification

Problem

Product images should faithfully reflect product specifications, but in large catalogs, inconsistencies often occur. As a result, customers receive incorrect information, leading to increased returns and decreased brand trust.

AI solution

Advanced AI models can analyze both product description content and its images to detect discrepancies. The system recognizes visual features such as color, pattern, material type, or shape, and compares them with information contained in the description. If a product marked as a "red wool sweater" shows a blue cotton sweatshirt in the image, AI reports a potential error for verification.

Besides identifying obvious inconsistencies, AI can also detect less obvious cases, such as missing elements in the image or product version mismatch. Automatic control significantly improves visual data management and reduces the risk of errors in product catalogs.

Generating different fashion image views

Problem

In the fashion industry, it's crucial to present clothes in various contexts – on a model, flat lay, in lifestyle arrangements, or dynamic shots. Organizing professional photo shoots for each product variant is an expensive and time-consuming process requiring a team of stylists, photographers, and models. As a result, many brands limit the number of available images, which can negatively impact sales conversion.

AI solution

Modern SaaS tools using generative AI enable automatic creation of alternative clothing views based on a single image. These models can realistically represent clothes on models of different body types, change backgrounds, and generate dynamic visualizations in motion. This allows brands to quickly obtain professional-looking materials without organizing additional photo shoots.

Such solutions allow adapting images to different sales channels and personalizing product presentation depending on the audience. AI not only speeds up visual content production but also opens new possibilities in digital fitting rooms and interactive product catalogs, which represents the future of online fashion presentation.

Automatic product Classification Based on Image Analysis and Metadata

Problem

In PIM systems with DAM modules, products are often added to the database without full classification, and image files themselves are not clearly linked to appropriate categories. Lack of automatic image assignment to proper products or product groups causes chaos in the catalog and complicates visual material management.

AI solution

The AI model analyzes image content, file name, and available metadata to automatically determine which category a given product should be assigned to. Through recognition of visual features such as shape, color, product type, or style, AI can classify an image even when there's no clear description. Additionally, the system can use information contained in file names and EXIF metadata to increase classification precision.

Automatic category assignment facilitates asset organization in DAM and ensures consistency in product classification. This means administrators don't have to manually review hundreds of images, and visual materials are immediately ready for use in product catalogs and marketing campaigns.

Master data management

Automatic categorization of new products

Problem

Manually assigning categories to new products is not only time-consuming but also prone to errors and inconsistencies. In large product catalogs, different people may apply different classification criteria, leading to problems with filtering, searching, and data analysis.

AI solution

AI analyzes the name, description, and available product attributes to automatically assign it to the most appropriate category in the PIM structure. AI uses understanding of context and industry terminology, allowing for precise matching. If a product has unusual features, the system can suggest several possible categories along with justification, making it easier for the user to make the final decision.

Automatic classification eliminates subjectivity and increases data consistency across the entire catalog. Additionally, AI can adapt to changes in category structure, making the system more flexible in long-term product data management.

Product duplicate management

Problem

The same products often enter the catalog multiple times, differing in name, code, or minor specification details. This causes chaos in the database, complicates offer management, and can lead to errors in orders.

AI solution

LLM analyzes not just names but also technical specifications, descriptions, and product images to detect potential duplicates. The system identifies similar entries even when they differ in formatting or contain ambiguous designations. It can also suggest which product variant should be considered primary and which entries should be merged or removed.

AI significantly speeds up the deduplication process, reducing the number of erroneous records in the PIM system. This keeps the catalog organized, and searching and managing data becomes more efficient.

Process and workflow automation

Integration of data from multiple suppliers

Problem

Each supplier uses their own format and terminology for the same product attributes, leading to inconsistencies in the PIM database. Manual normalization and mapping of this data is tedious, error-prone, and makes scaling operations difficult.

AI solution

The language model analyzes provided files and automatically maps different terms for the same attribute to the standard format used in the PIM system. For example, if one supplier uses "capacity", another "volume", and yet another "volume", AI recognizes their identity and assigns them to a uniform attribute. It can also suggest corrections for values, eliminating duplicate units of measurement and conversions between systems (e.g., liters vs. milliliters).

Automatic attribute normalization speeds up the data integration process and allows for consistent product information management without the need for manual intervention in each new supplier file. See more at getName.ai.

Approval workflow automation

Problem

The process of approving new products often encounters delays because there's a lack of clear rules about who should take the next steps. Products may require evaluation by different departments, and manual responsibility assignment causes inefficiency.

AI solution

The AI model analyzes product characteristics, categories, and business requirements to automatically assign tasks to appropriate teams. For example, food products are directed to the food safety department, electronics to the technical compliance department, and clothing to the visual compliance team. AI can also identify products requiring approval based on specific criteria, such as product value, need to meet industry standards, or lack of required documents. Automatic task distribution eliminates ambiguities in the approval process, speeds up workflow, and reduces the risk of bottlenecks in the product launch process.

Personalization and customer experience

Generating answers to technical questions

Problem

The customer service team often has to manually search through various documentation sources, user manuals, and previous tickets to find answers to technical questions. This process is time-consuming and can lead to inconsistent answers, especially with a large number of products.

AI solution

The language model using RAG (Retrieval-Augmented Generation) technique analyzes available technical materials, user manuals, and history of similar queries to generate precise answers in real-time. Thanks to the mechanism of retrieving information from external sources, AI can recognize the context of the question and provide accurate answers, providing specific technical values, documentation fragments, or suggested problem solutions.

Product comparison generation

Problem

Customers often ask about differences between similar products, but manually creating comparisons requires time and knowledge of specifications. Lack of clear comparisons makes it difficult for users to make purchasing decisions.

AI solution

The AI model automatically analyzes product attributes and generates readable comparison tables and summaries of key differences. It can also highlight advantages of one product over another depending on customer needs – e.g., for laptops, AI can focus on processor performance for gamers, and on weight and battery life for travelers. Automatic comparisons eliminate the need for manual specification comparison and help users make informed decisions.

Accessory recommendation personalization

Problem

Standard accessory recommendations, such as "frequently bought together", don't consider the context of product use. As a result, customers receive general suggestions that don't always match their actual needs.

AI solution

The language model analyzes the way the product is used and the purchase context to generate more accurate recommendations. If a customer is buying a camera, AI can suggest accessories tailored to specific use – e.g., for sports photography, it recommends fast memory cards and stabilizers, while for wedding photography, flash units and additional portrait lenses. This approach increases recommendation accuracy and improves the shopping experience, leading to better product-to-need matching.

Will PIM systems finally become "intelligent"?

Until recently, PIM systems were synonymous with simplicity and crudeness to me. They served as data warehouses – rigid, passive, and completely devoid of intelligence. Their main task was to store product attributes, descriptions, and multimedia, but they lacked cleverness and the ability to analyze context.

However, this is beginning to change. With the development of large language models (LLMs) and new technologies, a question arises: can PIM systems become more than just a primitive database? Can they start to "understand" products, optimize content, and support users in information management? Will users shift from performing tasks in the PIM system to simply issuing task commands?

Dynamic knowledge systems instead of static databases

Today, PIM is a structural database where users must manually manage categories, attributes, and relationships between products. However, in the future, these systems may become dynamic knowledge networks that independently analyze data and suggest optimal solutions.

• Will PIM systems be able to automatically recognize connections between products and suggest missing information
• Will product descriptions be generated in real-time, adapting to the audience and sales channel?
• Instead of defining content strategy independently, will users be able to simply ask PIM for the best way to present a product?

Automatic adaptation to market changes and regulations

Companies must regularly adapt their product catalogs to changing regulations and legal requirements. Today this requires manual updates, but AI can significantly simplify this process.

• Will the PIM system monitor regulatory changes and automatically suggest updates compliant with new regulations
• Will AI be able to dynamically optimize product descriptions for SEO and various e-commerce platform requirements
• Will intelligent PIM systems detect market trends on their own and suggest changes to product catalogs?

Intelligent management of productdata and content

Already today, AI in PIM systems allows for automatic description generation, adaptation to different sales channels, and detection of missing data based on context analysis. Language models can process technical content, suggest attribute values, and dynamically personalize descriptions for different audiences.

But what's next? In which direction will these technologies develop?

• Will PIM systems be able to analyze sales data and optimize product content for conversion based on that?
• Will AI not only generate descriptions but also test different variants and automatically implement those that work best?
• Instead of creating content manually, will PIM users become content curators, approving and optimizing AI suggestions?

Personalization and automation already play a key role, but the future may bring even greater AI autonomy in product information management. Will we reach a point where PIM will operate almost independently, with humans only correcting its decisions?

New technologies - OpenAI's "operator" function

It's also worth considering the potential impact of the virtual worker function in the form of OpenAI's Operator tool. Operator is an AI agent that can independently perform tasks in a web browser, such as filling out forms or placing orders, mimicking human interactions with the graphical interface.

Can such advanced tools be integrated with PIM systems, enabling automation of more complex product information management processes? For example, could Operator independently update product data on various e-commerce platforms, adapting descriptions and attributes to the specific requirements of each?

While these are still speculations, the development of such technologies may significantly influence the future of product information management. It's possible that PIMs will not only store and organize data but also actively manage it, automatically responding to changes in the market environment.

What's next?

There's still no certainty about which direction PIM system manufacturers will take. Will AI remain just a tool supporting administrators, or will it take over most operational tasks?

One thing is certain – PIMs won't be the same as before. Intelligence and automation are beginning to play an increasingly important role. The question is no longer whether, but how deeply AI will influence the way we manage product data.

When not to use AI in PIM systems?

Despite the growing popularity and effectiveness of AI solutions, there are situations where their implementation in PIM systems may bring more problems than benefits. Before investing in AI tools, it's worth honestly assessing whether your organization really needs them and whether it's ready for their implementation. Below are key scenarios where it's worth considering a more traditional approach or waiting to implement AI until the scale of operations or organizational maturity justifies it:

Small scale operations

• If you manage a catalog smaller than 1000 products, AI implementation costs may outweigh potential benefits
• When product updates happen rarely (e.g., once per quarter)
• If you work only in one language and one market

Products requiring very precise description

• Medical equipment where incorrect information could have health consequences
• Products with legally regulated descriptions (e.g., medications, dietary supplements)
• Technical components where specification precision is critical for safety

High legal risk

• When description errors could lead to lawsuits (e.g., allergen ingredients in food)
• Products subject to strict legal regulations
• When full transparency of content creation process is required

Unique, Hand-crafted products

• Artisanal products where each piece is different
• Artworks requiring expert description
• Collectible items with unique history

Lack of verification capability

• When you don't have experts who can verify generated content
• Lack of quality control procedures for AI-generated content
• Limited resources for monitoring and correcting errors

Sensitive business data

• When using external AI models requires sharing confidential information
• No possibility of locally hosting AI solutions
• Risk of data leakage through APIs

Remember that AI should be a supporting tool, not completely replacing human expertise. The best results are achieved by combining automation with appropriate supervision and verification by specialists.