Can a Product Information Management (PIM) System Make Your Data AI-Ready?

Yes, a PIM system can help get your data ready for AI—but only if it’s set up the right way.

If you’re managing product info across channels, you already know that bad data means bad results. And if you’re thinking of adding AI—like auto-tagging, personalization, or predictive tools—your product data needs to be spot-on.

This post breaks down what “AI-ready data” actually means, why messy product data kills your AI plans, and how a PIM system fits into fixing it.

What Does “AI-Ready” Data Mean?

AI-ready data is clean, complete, consistent, and structured to match what the AI needs to do. If any part of that is missing, the results from your AI model will be wrong or useless.

Gartner outlines five key steps to make data AI-ready:

1. Assess the data needed for each AI use case. You can’t just throw all your product data into an AI tool and expect magic. You need to know what the AI is supposed to do—recommend products, tag images, write descriptions—and check if the data supports that.

2. Align your data with the AI’s goals. Let’s say your goal is to personalize search results. That means every product needs the right tags, images, and categories. If that info’s missing or inconsistent, AI can’t deliver what you want.

3. Set clear rules for data governance. This includes naming standards, formatting rules, and tracking changes. AI systems rely on patterns. Without strong data governance, the AI can’t recognize patterns well enough to learn or predict accurately.

4. Use metadata to give your data context. Metadata helps AI understand what each piece of data means. It’s how you tell a machine the difference between a color and a size, or between an image and a feature.

5. Make data everyone’s job. If only IT or product teams handle data cleanup, you’ll never scale. You need marketing, content, and sales to be part of the process. That cross-team input helps AI models learn faster and smarter.

Without these steps, AI tools waste time trying to clean or guess data—and that leads to mistakes.

Common Product Data Problems That Hurt AI Outcomes

AI depends on structured, reliable data. When product data is messy or incomplete, AI tools can’t learn correctly or make accurate decisions.

Here are the most common issues that mess up AI results:

1. Missing values. If your product descriptions don’t always include size, color, or materials, the AI can’t group or recommend items correctly.

2. Inconsistent formats. “Red”, “RED”, and “#FF0000” might mean the same thing to people—but not to machines. AI models treat each format as different unless the data is standardized.

3. Duplicate entries. Two versions of the same product can confuse the AI. It might see them as separate products and deliver incorrect suggestions or analytics.

4. Unstructured content. If your product titles are crammed with keywords but no pattern, AI can’t extract useful meaning. Structured data is easier for models to work with.

5. Lack of metadata. AI models need more than just the product image or title. Without tags, category labels, and usage context, the model can’t learn how to connect products.

6. Outdated info. AI training requires current, real-world data. If product details change often but don’t get updated fast enough, the AI works off bad inputs and gives wrong outputs.

Each of these issues reduces the accuracy of your AI’s predictions, recommendations, or automations.

How PIM Systems Solve These Problems

A PIM system helps fix the data issues that stop AI from working well. It brings structure, control, and context to your product data—all of which AI needs to deliver value.

Here’s how PIM lines up with the five AI-readiness steps from Gartner:

1. Data aligned with use case: In a PIM, you define which attributes are required for each product category. If your AI needs color, size, and material to personalize product recommendations, PIM ensures that data is there—before the product is published.

2. Data normalization: PIM tools standardize formats. “Blue” won’t show up as “BLU” or “navy blueish” in different listings. The system enforces data rules, so your AI can trust the inputs.

3. Data governance: PIM systems let you set validation rules, version tracking, and user permissions. This means every change is tracked, and only approved data moves forward—key for AI systems that depend on clean histories.

4. Metadata management: PIM systems store and manage metadata like categories, usage tags, and even SEO terms. This extra layer helps AI models understand context—whether it’s matching a product to a search or choosing the best image.

5. Cross-team collaboration: With a PIM, marketing, product, and eCommerce teams work from the same source. This reduces errors, speeds up updates, and gives AI a steady flow of reliable product information.

By solving these issues at the source, a PIM platform creates the clean, structured, and well-governed data foundation that AI tools need to do their job right.

Can PIM Alone Get You There?

A PIM system solves the data problems—but it doesn’t replace the AI stack. Think of PIM as the prep kitchen. It gets everything clean, sorted, and ready to go. But you still need the right tools to cook.

Here’s what PIM does well:

• Cleans up product attributes

• Standardizes formats and values

• Adds missing metadata

• Makes data accessible across teams

But once the data is ready, you still need AI platforms to do the heavy lifting. That includes:

• Machine learning models to drive personalization

• Predictive tools to forecast demand or returns

• Agentic AI tools that take action (like re-tagging or alerting on gaps)

• Analytics platforms to visualize outcomes

So no, a PIM alone won’t give you full AI capabilities. But without a PIM, your AI tools will spend most of their time cleaning up your mess instead of giving you results.

PIM Is Your First Step to Smarter AI

AI can only work well when the data behind it is complete, consistent, and structured. A PIM system lays that foundation. It organizes your product information, enforces data standards, and adds the context that AI tools need to operate accurately.

Without clean data, AI models deliver flawed results. But with a strong PIM in place, you give AI the best chance to succeed—whether it’s automating product tagging, powering recommendations, or optimizing digital experiences.

Need help setting up a PIM or making your product data AI-ready?
Connect with us today—We help businesses use the right mix of PIM and AI to get real results faster. Whether you’re starting fresh or upgrading what you’ve got, we’ll make sure your data is ready for the next step.

A strong and successful search engine optimization (SEO) strategy is essential in the extremely competitive world of e-commerce today. You can increase the visibility, draw in more visitors, and raise conversion rates with the correct tools and strategies. Product information management (PIM) is a crucial tool for accomplishing these objectives.

What is PIM?

PIM provides a central repository for product information, ensuring that information is accurate, consistent, and up-to-date. This allows businesses to streamline the management of product data, such as descriptions, images, specifications, and other key information related to their products. Having this organized and easily accessible information can be extremely beneficial to businesses looking to improve their customer service, increase sales, and ultimately enhance their SEO performance.

By using PIM, businesses can save time and resources by reducing manual work, increasing accuracy, and eliminating redundant data entry. A PIM system can also help with managing different versions of product descriptions, images, and other data fields in different languages and currencies. This allows businesses to quickly launch products into new markets and keep them updated across multiple channels.

How can PIM help improve your SEO?

Product Information Management (PIM) systems are designed to help businesses store, manage, and distribute product information in an efficient and organized manner. It has become a popular tool for businesses looking to improve their SEO rankings.

PIM can help improve your SEO rankings in several ways:

1. High-quality Content: PIM can help ensure that product information is accurate, complete, and consistent, which can lead to better on-page optimization and search engine visibility.
2. Enhanced Product Descriptions: PIM enables the creation of detailed and optimized product descriptions, which can help improve the relevance and quality of content for search engines.
3. Better Keyword Targeting: PIM can provide insights into which keywords are most relevant for each product, enabling e-commerce websites to better target those keywords in their product pages and other content.
4. Improved Taxonomy: Taxonomy helps to improve the customer experience by making it easier for customers to find what they are looking for, and to compare products based on relevant attributes. In addition, a well-structured taxonomy can also help to improve search engine optimization (SEO) by increasing the relevance of search results, which can drive more traffic to a company’s website.
5. Cross-Channel Distribution – PIM systems also make it easy to distribute your product information across multiple channels. This helps increase the visibility of your product pages and will help improve your SEO rankings.
6. Faster and More Efficient SEO Updates – PIM can also help make SEO updates faster and more efficient. With PIM, you can quickly and easily make changes to your product information, which can then be automatically updated across all of your sales channels. This saves time and reduces the risk of errors, making it easier to optimize your product pages for search engines. With PIM, you can keep your website up-to-date with the latest product information and take advantage of new SEO opportunities as they arise.
7. Asset Management – Asset management in a Product Information Management (PIM) system refers to the process of organizing and managing digital assets, such as images, videos, and other multimedia files, associated with a product. This includes storing, categorizing, and versioning these assets to ensure that they are easily accessible and up-to-date. We can also attach metadata to digital assets to help improve the search.

This can lead to improved organic search traffic and more conversions for your business but business always questions how do I know the optimization we were doing in PIM is helping us, One way to identify is utilizing Digital Self analytics.

inriver’s digital self-analytics tool, Evaluate, significantly enhances SEO optimization in several ways:

1. Content Compliance: Evaluate ensures that your product information is accurate and consistent across all channels, which is crucial for SEO. Accurate data helps search engines understand your products better, improving visibility.
2. Keyword Optimization: The tool tracks keyword performance and helps you optimize product listings for better search rankings. This includes monitoring keyword search and share-of-shelf.
3. Real-Time Insights: Evaluate provides real-time insights into how your products are performing on the digital shelf. This includes monitoring product search rankings, competitor pricing, and stock levels, allowing you to make data-driven decisions to improve SEO.
4. Engagement Intelligence: By analyzing customer interactions and engagement with your product listings, Evaluate helps you understand what works and what doesn’t. This information is vital for refining your SEO strategy to attract more traffic and improve conversions.
5. Automated Monitoring: The tool uses smart automation to constantly monitor your products, providing actionable insights that help you stay ahead of the competition and ensure your product information is always optimized for search engines.

Using inriver Evaluate, you can take control of your digital shelf, drive revenue growth, and enhance your SEO efforts with precise, actionable data.

By following these recommendations, you can make sure that you get the most out of your PIM system and improve your SEO performance. PIM can help you stay ahead of the competition in the e-commerce space. So if you’re looking to improve your SEO performance and reach more customers, it’s time to invest in PIM. For more information on this, contact our experts today.

For organizations to remain competitive in today’s fast-paced digital environment, product information management (PIM) needs to be precise and effective. inriver latest innovation, the Expression Engine, aims to transform how consumers manage and manipulate product data. It allows users to configure rules that automatically apply to product data, similar to using formulas in a spreadsheet. If you’re an inriver PIM user and haven’t yet enabled this powerful feature, here’s what you’re missing out on:

1. Enhanced Data Transformation Capabilities

The Expression Engine allows users to configure enrichment rules, enabling the automation of complex calculations, data string generation, and logical rule application. This significantly reduces manual effort and boosts efficiency. For example, you can create SEO friendly description by concatenating key attributes. This automation significantly reduces the time and effort required to prepare data for various channels.

2. Improved Speed to Market

By automating data transformations, the Expression Engine helps businesses accelerate their time to market. As product information is processed and updated automatically, you can quickly adapt to new market demands and ensure that your product data is always current and accurate. This is particularly beneficial for companies managing large product catalogs across multiple channels.

3. Increased Data Quality and Visibility

The dynamic functionality of the Expression Engine guarantees that any updates to your PIM data are instantly mirrored across all connected fields. This minimizes the likelihood of human error and helps maintain consistent and dependable data. Furthermore, users can view the formulas used in each field, enhancing transparency and simplifying the process of troubleshooting any issues.

4. Cost and Resource Efficiency

Traditionally, businesses often turned to custom code or manual processes to manage complex data transformations. However, the Expression Engine streamlines this process by eliminating the need for these costly and time-consuming methods. With its intuitive interface for configuring expressions, businesses can reduce their reliance on IT support, resulting in lower costs and a more efficient way to maintain their PIM systems.

5. Seamless Integration and Extensibility

The Expression Engine utilizes familiar tools and interfaces, like Microsoft IntelliSense for code completion and color coding, making it simple for users to create and manage expressions. This smooth integration allows you to fully leverage the PIM’s features without requiring advanced technical expertise.

6. Future-Proofing Your PIM Strategy

The Expression Engine’s adaptable and scalable architecture ensures that your PIM system can evolve alongside your business needs. By enabling this feature, you’re investing in a future-proof solution that will keep providing value as your product information requirements evolve.

Achieving PIM Efficiency with Expression Engine

A few example use cases of Expression Engine in PIM –

• Retail: In the retail industry, the Expression Engine can automatically calculate and populate fields like product attributes (e.g., size, color, and material) based on predefined rules. For example, if a retailer defines a rule that certain product types should always include specific care instructions or packaging details, the Expression Engine can automate the inclusion of these values, ensuring consistency across all product listings.
• Manufacturing: For manufacturers using PIM, the Expression Engine can generate and manage product codes and specifications dynamically, based on product attributes such as part numbers, materials, or dimensions. This ensures that each product is accurately categorized and described without needing to manually update multiple fields for every new product variation, reducing errors and enhancing efficiency.
• E-commerce: In the e-commerce sector, the Expression Engine can help create SEO-optimized product descriptions by automatically combining product attributes such as category, key features, size, and material. This automated approach ensures that every product page is consistent, relevant, and keyword-rich without requiring manual entry, thereby saving time and improving search engine visibility.

Conclusion

Activating the Expression Engine in your inriver PIM system offers numerous benefits that can greatly improve your product information management. You’ll see enhancements in data quality, quicker time to market, cost savings, and increased efficiency. Don’t miss the chance to optimize your PIM strategy—enable the Expression Engine today and witness the positive impact on your business.

If you’re ready to get started, contact us to discover how we can assist you on your PIM implementation journey and learn more about how enabling the Expression Engine can streamline your enrichment process.

In today’s digital landscape, efficiently managing product information is vital for businesses to enhance customer satisfaction and drive sales growth. A robust Product Information Management (PIM) system with excellent integration features, like inriver, will streamline your PIM strategy. By utilizing the integration frameworks and APIs provided by inriver, businesses can ensure relevant, accurate, and consistent product information across all channels. This article explores key inriver integration techniques that have the potential to transform your PIM approach.

The importance of PIM Integration –

Automating PIM processes leads to significant improvements in efficiency, accuracy, and scalability. By eliminating manual data entry, automated integration reduces errors and ensures that information remains consistent and current across all systems. This not only saves time and cuts labor costs but also enhances business agility and customer satisfaction. With automated integration, companies can swiftly adapt to market changes, make informed decisions, and provide timely, personalized information to their customers.

Exploring inriver Integration Options –

There are several ways to automate the integration between systems that are used to send or receive data –

Leveraging APIs (application programming interface)-

• inriver REST APIs – These can be utilized to build integrations in any programming language and customize interfaces within inriver, including creating enriched PDF/Preview templates.
• inriver Remoting APIs – These require C# programming knowledge and are used with hosted solutions. The Remoting API services consist of six major components:
  • Channel Service – Methods related to channels. e.g. Channel Structure, Publish/Unpublish a channel, Retrieve entities and links from a channel etc.
  • Data Service – One of the most widely used Service for creating, updating, deleting and finding entities and links in the system.
  • Model Service – Contains methods for building and maintaining PIM data model.
  • Print Service – Used for developing the inriver print plugin.
  • User Service – Provide methods for maintaining uses, role, permissions and restrictions.
  • Utility Service – Contains various method including Connector states, HTML Templates, Languages, and Notifications.

Remoting Services

• Content API  – A set of APIs designed to facilitate the onboarding and distribution of large volume of product data.
  • Content Onboarding API – help standardize the data onboarding process by dividing them into five key steps – Landing Area, Field Mapping, Staging area, PIM validations and Import.
  • Content Delivery API – used for distribution of product data to various channels and platforms, ensures that product data is uniform across all channels.

Integration Framework (IIF) – The Integration Framework is a foundation for building adapters and outbound integrations in inriver. It transforms customer’s unique data model into a standard integration model. It supports custom entity types, delta functionality and provide standard functions to deliver product data.

High level integration framework flow

The following table highlights the key aspects when considering integration within inriver –

These integration techniques can significantly enhance your PIM strategy, ensuring your product data remains accurate, consistent, and up to date across all channels. At Perficient, we engage in comprehensive discussions throughout our elaboration process and continue to validate during implementation phase. We help finalize best practices tailored to each customer’s unique needs, recognizing that one approach may work better for one client than another. Get in touch to explore how we can support you on your PIM implementation journey, whether you’re starting fresh or facing challenges with an existing system.

I recently got a chance to work on Optimizely’s Product Information Management (PIM) system and to familiarize myself with the platform. Optimizely PIM is a cloud-based product information management system that provides a centralized hub to manage all your product information. Like other traditional PIM platforms, it provides a single place to collect, manage, and enrich product data and distribute it across your commerce channels.

Some key features of Optimizely PIM include data import, workflow, data governance, and asset management. However, some core differences make Optimizely PIM different from other PIM systems:

• Optimizely PIM is tightly integrated with Optimizely (Episerver) B2B Commerce Cloud and works as an add-on rather than an independent tool.
• A business user needs to do pre-setup and configuration at Optimizely B2B Commerce Cloud before beginning with Optimizely PIM.
• Optimizely B2B Commerce Cloud comes with a pre-built integration module to bring approved data over Episerver PIM.
• A traditional PIM helps to syndicate enriched product content to multiple places, but at present, Optimizely PIM syndicates to Optimizely B2B Commerce Cloud. The product team is enhancing this further to allow for the data to be pulled out of PIM for external use.
• At this time, there are no extension points for customization.

Optimizely PIM comes with a defined set of entities based on Optimiely B2B Commerce Cloud needs. Below are the core entities and relations between Optimizely B2B Commerce Cloud and Optimizely PIM.

How Attribution Works in Optimizely PIM

An entity is a container with any kind of property. Properties, also known as attributes, are data elements that define your products. These properties are grouped and organized and then pulled into product templates.

Product templates are used to define the major data elements required or recommended to populate for items within a family of products. This helps enforce data governance before publishing products to the Optimizely B2B Commerce Cloud. The following diagram shows the Optimizely PIM attribution structure:

One of the key features that I like about Optimizely PIM is that, as you create properties, you also get the option to map them with Optimizely B2B Commerce Cloud fields. The properties can be mapped to virtually any product-related object or field, such as attributes, specifications, related products, and even custom properties. Still, it also imposes restrictions based on control types and validations, such as minimum or maximum length.

A Typical Workflow in an Optimizely PIM Implementation

A product content workflow plays an important role in any PIM implementation because it is how a product moves through processes, whether you are adding a new product, removing a product, or updating existing product information.

Optimizely PIM moves the enrichment process forward as products are imported. When product import is complete and the product has all of the required data based on the product template’s data governance, a user can then submit the products for approval. Only admin or manager users can approve products to be submitted to Optimizely B2B Commerce Cloud. Once products are approved, they are pulled into Optimizely B2B Commerce Cloud using the “published approved” products job, which also flags products published in PIM. “Refresh published” products job will be used to sync the secondary (Stage/Sandbox) environment.

Things to Consider Before Using Optimizely PIM

Optimizely PIM is a newer PIM Platform, and it’s important that the following is considered and taken care of before you start to use it:

• Identify the key member who will be working on Optimizely PIM
• Have those members complete the Optimizely PIM Training (Episerver Academy)
• Identify base product information from the data source
• Gather your product data and assets
• Perform the data profiling and analysis to identify any data inconsistencies
• Work with a PIM expert to create a robust data model and establish data governance.

Final Thoughts on Optimizely PIM

As mentioned before, Optimizely PIM is natively built to integrate with Optimizely B2B Commerce Cloud. It allows to quickly set up a catalog and ensures that product information is up-to-date, consistent, and helps deliver the effective product experience in Optimizely B2B Commerce Cloud. For more information on Optimizely PIM and product information management, contact our PIM experts today, and check out our partner page to learn more about our work with Optimizely.

Every organization manages data internally that provides support in running the operations and provides enriched content to an external audience such as buyers or distributors. To manage the organizational data effectively, it is recommended to conduct data analysis to assess the current state, make informed decisions to streamline that data and help its growth.

Data analysis is the process of collecting and evaluating data using analytical or statistical tools, which can help you make informed decisions for your business. There is a large range of data analysis tools and software available in the market, such as business analysis tools, statistical analysis tools, data visualization tools, ETL tools, spreadsheet applications, and general programming languages. Spreadsheets are one of the most traditional data analysis forms and a go-to tool for many businesses as they are simple and do not require considerable training.

Spreadsheets are Original, Effective, and Useful

Functions and formulas can be utilized in Microsoft Excel to analyze any kind of data set. It can be overwhelming to try to match the right formula with the right kind of data analysis. Still, our data analytics team has provided some of the most commonly used functions to help you solve a number of your data analysis issues:

1. =VLOOKUP – This function can help you find things in a table or range by row. This will be what you want to look up, where you want to look for it, the column number in the range containing the value to return, and return an approximate or exact match – indicated as 1/TRUE, or 0/FALSE).

2. =SUMPRODUCT – This function returns the sum of the products of corresponding ranges or arrays. It includes the first array argument, the value provided to obtain the function’s result, the components you want to multiply and then add, and array arguments 2 to 255 whose components you want to multiply and then add.

3. =COUNTA – This function counts the number of cells that are not empty in a range. Here, the first argument represents the values you want to count, and additional arguments represent the values you want to count, up to a maximum of 255 arguments.

4. =SUMIF – This function adds the value that meets a single criterion. Use SUMIFs if you need to consider multiple criteria. The range of cells that you want to be evaluated by criteria, either in the form of a number, expression, a cell reference, text, or a function, defines which cells will be added or the actual cells to add, and if you want to add cells other than those specified in the range argument.

5. =MID – This function returns a specific number of characters from a text string starting at the position you specify. The text string contains the characters you want to extract, the position of the first character you want to extract in the text, and specifies the number of characters you want MID to return.

6. =FIND – This function locates one text string within a second text string and returns the number of the first text string’s starting position from the first character of the second text string. This can help you with the text you want to find, the text containing the text you want to find, or specify the character to start the search.

7. =IFERROR– This function returns a value you specify if a formula evaluates to an error. Otherwise, it returns the result of the formula. This function checks the argument for an error or the value to return if the formula evaluates to an error. The following error types are evaluated: #N/A, #VALUE!, #REF!, #DIV/0!, #NUM!, #NAME?, or #NULL!.

Finding the right tools to utilize for data analysis can be tricky and hard to understand, but resources like these can help you along the way. Stay tuned for our next topic, as we explain Open Source and free data analytics tools. For more information on data analysis, contact our experts today.

The FieldSets method

The FieldSets method allows users to create individual FieldSets to organize products or items at the entity level. This enables users to search for items and view only those that match specific fields, effectively reducing the number of fields displayed by categorizing them. This feature does not require custom extensions, and entities cannot be duplicated. For larger entities with numerous categories and fields, it’s important to consider and benchmark performance.

The Specification method

The Specification method enables the creation of Parent and Child templates to facilitate product data inheritance and field copying. By managing entities hierarchically, data can be updated in the parent template and propagated to all associated entities. Child templates can be created and modified under the parent template while retaining inherited fields. Although the concept of categories remains unchanged, this feature requires custom extensions. Additionally, search capabilities differ under this model, and larger entities will benefit from enhanced field performance.

By adopting one of these methods, businesses can streamline their product data management processes, reduce conflicts between different areas, and continue to sell products successfully. Efficient product data management is not just a technical necessity but a strategic advantage that can drive business success in a competitive market.