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Artificial Intelligence

How to Prepare Your Product Data to Ensure AI Success

AI has the potential to transform how you manage product information, but if your product data is inconsistent, incomplete, or scattered, even the smartest AI tools will stumble. Discover the risks of applying AI to messy data, what “AI-ready” product information looks like, and the practical steps you need to take in order to assess, clean, and structure your data.

Table of Contents

    Keywords

    Artificial intelligence (AI)
    eCommerce
    PIM

    Have you ever tried cooking with a poorly written recipe? You know the kind – vague measurements, missing steps, ingredients listed out of order. You spend more time second-guessing than actually cooking, and the end result rarely turns out the way it should.

    Working with artificial intelligence (AI) on top of messy product data feels a lot like that.

    AI has incredible potential to transform how businesses manage and scale product information. It can generate product descriptions, power intelligent search, personalize recommendations, and help you go to market faster across every channel. But just like a recipe, it needs clear, complete, and reliable instructions—your product data.

    When your product data is inconsistent, incomplete, or scattered across systems, AI can’t perform at its best. In fact, it may even cause more problems than it solves. That’s why the first step in any AI journey should be getting your product data in order.

    Why Focus On Product Data First?

    Artificial intelligence isn’t magic. It’s pattern recognition at scale. Whether you’re tapping into generative AI to write compelling product descriptions or using predictive models to suggest upsells, all AI systems depend on one critical ingredient: data. And not just any data – clean, structured, and consistent product data.

    AI thrives on well-organized inputs. It needs reliable patterns and clear relationships between data points to draw insights or make predictions. When you feed it high-quality product information, it can identify trends, fill gaps, and even anticipate customer needs. But when that data is messy or incomplete? Things fall apart.

    Let’s say your AI is tasked with generating SEO-friendly product titles. If it pulls from inconsistent naming conventions where one item is called a “crewneck pullover” and another a “long-sleeve fleece” for nearly identical products, it won’t know which terminology to standardize or prioritize. Or imagine a recommendation engine working off missing sizing information; it may suggest irrelevant or ill-fitting products to shoppers, causing frustration and returns.

    That’s why product data should come first, before you automate, optimize, or personalize.

    Think of it like building a smart home. You wouldn’t start wiring your house for voice-activated lighting or automated blinds before making sure the floors are level and the plumbing works. Without a solid foundation, all that smart functionality is compromised. 

    It’s the same with AI: get the basics of product data right, and automation, personalization, efficiency becomes far more effective and reliable.

    Good AI doesn’t replace good data hygiene – it builds on it.

    Risks of Implementing AI with Messy Data

    Companies excited to deploy AI without cleaning up their data often encounter unexpected consequences, including:

    • Inaccurate or misleading product listings: AI-generated descriptions pulled from poor source data can lead to incorrect claims, like saying a jacket is waterproof when it’s not. That’s a fast track to unhappy customers, negative reviews, and costly returns.
    • Faulty recommendations and inaccurate personalization: Product recommendation engines depend on well-structured attributes (size, color, use case, materials). If those fields are missing or incorrect, AI might suggest winter coats to shoppers browsing bikinis.
    • Poor search functionality and discoverability: AI-powered search and filtering tools rely on good taxonomy and attribute tagging. If similar products use different terminology (e.g., “blush pink” vs. “light rose”), they may not appear in the same search results.
    • Amplification of errors at scale: AI accelerates everything, including mistakes. If your product feed contains incorrect dimensions or pricing, and AI uses that feed to populate 10,000 listings across channels, the error now lives in 10,000 places.
    • Regulatory and legal compliance risks: Bad data can lead to non-compliance with product labeling laws, ingredient disclosures, or safety regulations, especially in industries like food, cosmetics, and electronics. AI doesn’t inherently know what’s legal or ethical; it follows your lead. 

    What AI-Ready Product Data Looks Like

    So what exactly does clean, AI-ready product data look like? There are 6 key traits of AI-ready product data:

    1. Structured

    Your data needs to follow a clearly defined format with proper categorization and hierarchy. Think of it like a family tree for your products. Parent products should be connected to their variants (colors, sizes, styles), and attributes should be broken down into specific fields, like material, size, dimensions, or use case. Without structure, AI can’t navigate your data or draw reliable conclusions.

    2. Complete

    AI can’t work with what it can’t see. Incomplete data like missing product titles, specs, or images leads to poor outputs. Ensure that every product listing contains all the necessary fields and content across every category. Completeness is a prerequisite for AI performance.

    3. Consistent

    Standardization is crucial. If one product lists its color as “navy” and another as “midnight blue,” AI might treat them as unrelated even if they’re the same item in different channels. Consistency allows AI to recognize patterns across your product catalog and make smart associations.

    4. Enriched

    Basic specs alone won’t cut it. AI needs context to do its best work, whether that’s generating creative copy, powering search results, or optimizing listings for SEO. That means providing rich text descriptions, high-quality images, videos, customer reviews, usage guidelines, sustainability certifications, and more. The more context AI has, the better it can craft engaging, accurate, and tailored content.

    5. Centralized

    Your product data shouldn’t live in 15 spreadsheets and a handful of legacy systems. To be usable by AI, data must be centralized in a single, trustworthy location, ideally a Product Information Management (PIM) system. Centralization eliminates version control issues, reduces duplication, and makes it easier to audit, update, and govern your data. It also ensures every AI application is drawing from the same source of truth.

    6. Channel-Ready

    Your product data must be flexible and adaptable. AI can help tailor content to each platform, whether that’s your eCommerce site, marketplaces like Amazon, print catalogs, or social media. But only if your data is already segmented and prepped for multichannel use. That means having different title lengths, formats, and tones for different channels, and language or region-specific versions when needed.

    The Next Chapter of Commerce

    6 Questions to Determine if Your Product Data is Ready for AI

    Before you dive into AI, it’s important to pause and assess the foundation you’re building on. Not all data is created equal, and not all data is ready for AI. These six questions will help you evaluate your current state and identify any red flags that could trip up your AI ambitions.

    1. Do you have an established product data hierarchy?

    Clear parent-child relationships (e.g., product families, variants) are essential for AI to understand your catalog structure. Without a clear hierarchy, AI might treat similar products as unrelated or miss opportunities to apply shared attributes. That leads to duplication, messy data, and irrelevant recommendations. A clean, logical structure is essential for training AI models to recognize patterns and apply rules efficiently.

    2. Do you have at least 100 pieces of product data?

    AI needs a critical mass of data to perform well. If you feed it too little, it won’t be able to detect patterns, test hypotheses, or generate meaningful results. Generally speaking, the more high-quality data you have, the smarter your AI becomes. This typically means 100+ well-documented, attribute-rich product records. Each should include structured fields like dimensions, materials, color, brand, and use cases. With a rich dataset, AI can make accurate inferences, generate tailored content, and even predict customer preferences.

    3. Does all of your product data live in a single, centralized source?

    Scattered data is one of the most common, and most frustrating, roadblocks to effective AI. If your product information is siloed across spreadsheets, outdated databases, DAMs (Digital Asset Management systems), or inside someone’s email inbox, AI won’t have access to the full picture. Worse, it may pull from inconsistent or conflicting versions of the truth. By storing your product data in a centralized system like a PIM platform, you create a single source of truth. That makes it easier to maintain, govern, and feed into AI applications. Centralization also reduces duplication, accelerates updates, and ensures that everyone (and every system) is using the same, up-to-date data.

    4. Is your product data consistent and coherent?

    If your data looks different from one product to the next, AI won’t know how to apply logic across your catalog. If one product is listed as “Large” while another says “L,” and another is “LG”, your AI model won’t recognize them as equivalent. That’s a recipe for bad recommendations, broken filters, and mismatched descriptions. Coherent data follows consistent naming conventions, formatting rules, and attribute structures. This uniformity allows AI to detect patterns, group similar products, and apply transformations or enrichments more effectively.

    5. Does your product data have rich text fields?

    Structured fields like specs and dimensions are important, but they’re only half the story. Rich text fields add depth and context. They include product descriptions, usage instructions, brand stories, SEO keywords, or care guidance. These fields provide the raw material for generative AI tools to create engaging content. Without rich text inputs, your AI won’t have enough “language” to work with. You might end up with generic, uninspired product copy or worse, AI-generated content that lacks accuracy or relevance. Rich text not only enhances the shopper experience, but it also helps AI create natural, persuasive, and informative product content at scale.

    6. Is your product data localized for different markets?

    If you sell internationally, localization is non-negotiable. Your product data needs to reflect the languages, cultural preferences, currencies, and regulatory standards of each region you operate in. AI tools can help automate translation, unit conversion, and channel-specific adaptations, but only if your data is set up to accommodate those needs. Localized data ensures that AI can produce relevant, compliant, and personalized content across geographies.

    6 Tips for Creating a Strong Foundation of Product Information

    If your answers to the above questions raised some red flags, don’t worry. Here’s how to get your data in shape:

    1. Audit your existing data and technology: Start by evaluating the current state of your product data. What’s missing? What’s inconsistent? Which systems are involved? This gives you a roadmap for where to focus.
    2. Identify key internal and external stakeholders: Involve product managers, marketing, IT, customer support, and compliance early. Everyone touches product data at some point, and their input will be vital to success.
    3. Establish a clear and consistent taxonomy: Create a standardized naming and classification system for your products. This helps both humans and AI understand how products relate to each other.
    4. Create a single source of truth for product data: Invest in a PIM or other centralized system where all product information lives. This ensures consistency across teams, regions, and sales channels.
    5. Set up processes for translation and localization: Make sure your data can flex to serve multiple languages, units of measurement, and cultural nuances. AI tools can help, but they need clean inputs to get started.
    6. Establish on-going data governance policies: Treat product data like a living asset. Set rules for how it’s created, reviewed, and maintained, and assign owners to ensure accountability over time.

    Smarter AI Starts With Stronger Data

    AI can be a powerful ally in managing, enriching, and scaling your product information. But it’s not a magic wand, it’s a multiplier. If your data is strong, AI will help you move faster, reach further, and deliver better product experiences. If your data is weak, AI will only amplify the gaps.

    That’s why investing in clean, complete, and consistent product data is a strategic move. It lays the groundwork for automation, personalization, and innovation that actually work.

    So before you dive into the latest AI tools, take a moment to look at the foundation you’re building on. Ask the right questions. Fill in the gaps. Organize what you have. With the right data in place, you’ll be ready to unlock the full potential of AI and turn it into a true competitive advantage.

    The Next Chapter of Commerce is Here.

    Discover how AI is transforming shopping, search, and product experiences, and why clean, structured data is the key to staying competitive in the next era of commerce.

    Casey Paxton, Content Marketing Manager

    Akeneo

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