Is your product data AI-ready?

In 2025, the global AI market stands at an impressive $747.91 billion, and it’s on track to nearly quadruple, reaching $2.74 trillion by 2032.

AI has evolved beyond just speeding up workflows and is now used to transform ecommerce experiences for customers. More than 75% of online customers appreciate a personalized and consistent brand experience across the web while they shop.

Take Netflix’s 301.6 million global subscribers. AI analyzes their viewing history and preferences to suggest movies or shows. That’s personalization at scale. Ecommerce is now following the same path, with AI continuously enriching product data to create smarter, faster, and more engaging shopping journeys.

And now, the stakes are getting higher. Large enterprises are beginning to expand their target audience beyond human buyers to include AI agents—autonomous systems that can discover, evaluate, and even purchase on a customer’s behalf. This shift, often called agentic commerce, positions your product data as the new storefront in a world of automated, multimodal discovery.

👉 So, what are the most impactful AI use cases in ecommerce and what kind of product data makes them work? Let’s explore!

AI’s expanding role in commerce and the data you need…

AI is evolving beyond chatbots or product recommendations. It now drives complete discovery, shopping, and purchasing journeys. From automated translations and image tagging to emerging content agents fueling agentic commerce, AI is transforming how product content is created, enhanced, and delivered.

Here are some of the most impactful new applications:

1. Visual search and multimodal discovery

Shoppers can now snap a picture or upload a screenshot to find similar products instantly. Google Lens already processes 20 billion visual searches per month. In fact, today, 20% of all Google Lens searches are shopping-related.

2. Conversational and voice commerce

By 2028, voice shopping is projected to reach $45 billion, and around 43% of online shoppers already use voice assistants to explore products. Similarly, conversational AI inside apps like WhatsApp, Instagram, and Messenger is fueling a $290 billion conversational commerce market by 2025.

3. Dynamic pricing and promotions

AI models are making it easier to monitor demand, competitor prices, and seasonal shifts in real-time. Today, 39% of retailers already use AI-driven repricing tools, giving them a competitive edge during sales events.

4. Hyper-personalization

AI-driven personalization is evolving past basic product recommendations to reconfiguring entire storefronts in real time for each individual shopper. Today, 71% of consumers expect brands to deliver experiences tailored to their needs.

5. Generative AI and content creation

From automated product descriptions to AI-assisted creative campaigns, generative AI is changing how retailers scale. Top business leaders say AI boosts productivity and ROI in marketing and commerce.

👉 But here’s the catch: none of these AI applications will work as promised without one thing: a high-quality product feed.

AI’s golden rule: Garbage in, garbage out, and why product data quality matters

Despite the surge in global AI investments, only a few businesses are currently able to scale AI successfully. The biggest roadblock? Inconsistent, incomplete, or siloed product data that prevents AI from making accurate connections and powering the experiences shoppers expect.

The reality is simple: AI is only as good as the data you feed it. Without high-quality product data, even the most advanced models will misfire.

For example, in a catalog with one million SKUs, even a 1% error rate in product attributes could misclassify 10,000 products, risking visibility on key platforms like Google or Meta.

That’s why many enterprises pouring money into AI still struggle to see ROI. Studies consistently find a gap between AI ambition and measurable outcomes.

The common failure modes include:

• Data silos and fragmentation: Product attributes live in PIMs, ERPs, marketplace feeds, spreadsheets, and vendor portals. When those sources disagree, AI models learn noise instead of patterns.
• Inconsistent formatting and taxonomy drift: Dates, units, and category labels must be normalized; otherwise, the AI model sees many dialects instead of one language.
• Missing richness: AI thrives on context: images, descriptive copy, usage scenarios, and attribute completeness (size, color, compatibility, materials) all improve results.
• Bias and representativeness: If the training data underrepresents certain product types, audiences, or regions, models will make skewed recommendations, which could be a risk for fairness, inclusivity, and performance.
• Lack of governance: Without approval workflows, human-in-the-loop checks, and traceability, automated content can produce errors or non-compliant claims.

💡 Pro tip: Invest in tools and workflows that raise your signal-to-noise ratio. Better governance and automation don’t just improve short-term performance; they make your AI efforts sustainable long-term.

Your AI-ready product data checklist: Ensure smooth implementation

Before AI can enhance discovery, personalization, and automation, your product data must be healthy, consistent, and context-rich. Use this checklist to prepare your feeds for AI:

• Consolidate and integrate your data sources

Product data is often scattered across CRMs, internal databases, marketplaces, ecommerce sites, and sales channels. Without integration, this leads to silos, data gaps, and errors that impact decision-making.

By centralizing everything into one reliable source of truth, you can build a complete and accurate product feed. This also helps avoid discrepancies like false product inventory counts when pulling data from multiple systems. A streamlined approach ensures teams always work with consistent, up-to-date information.

• Validate your product data

After consolidating your sources, validate data quality to catch errors, duplicates, and missing fields. Rich attributes, like size, color, or expiration date, strengthen AI’s ability to analyze and personalize content.

For instance, if you want to use AI to personalize a product description for a specific target audience, the more product attributes you can plug in, the more direct impact on how robust the generated description is.

• Structure your product data

Artificial intelligence detects patterns in data and then makes predictions based on these patterns. So, inconsistent data formats make it difficult for AI models to make accurate predictions and provide correct outcomes. To structure your data effectively:

/ Standardize formats: Align dates, numbers, and categorical entries across all sources.

/ Use labels and metadata: Properly annotate data to help AI understand context and relationships between attributes.

• Highlight sustainability and transparency attributes

As regulations like the EU’s digital product passport come into effect, product data must capture lifecycle details — such as materials, recyclability, or carbon footprint. Beyond compliance, this builds consumer trust and allows AI to generate product content that aligns with ethical and sustainability values.

• Ensure contextual and comparative clarity

Help AI systems serve customers better by enhancing product information with natural language context (“lightweight laptop under $1,000 for students”) and clear differentiators (“10% longer battery life than competitors”). This reduces AI misinterpretation and builds trust in the output.

This clarity is critical as commerce shifts toward agentic workflows, where AI systems don’t just surface products—they curate, compare, and even buy. A static “once-and-done” cleanup can’t keep pace with regular changes in SKUs, channels, and compliance rules.

Once your product data foundation is in place, the next challenge is making sure your entire enterprise is ready to adopt AI in a sustainable, scalable way. That’s where a strategic, big-picture approach comes in.

5 steps to make your enterprise AI-ready

For large, global companies with huge data sets, it’s crucial to implement AI strategically. The following steps can help ensure your AI goals are on track.

1. Clarify the objective of the AI use case

Start by revisiting the strategic goals of your enterprise and build a business value map. Analyze the inefficiencies and restraints in your business operations and identify opportunities to improve. After drawing lines between your current pain points and potential solutions, draft the scope for AI in your enterprise. By not overlooking this step, you can move forward with confidence that your AI initiatives align with your fundamental business goals.

2. Conduct a product data health check

Once you have defined the AI implementation objectives for your enterprise, evaluate your company’s product database, how data is currently stored, who is responsible for managing it, how information is shared, where it is shared, and what purpose it serves. Understanding the current state of your product data is a necessary step on the path to improving its quality to ensure successful AI outputs.

3. Build your AI team

Identify people within your company, or lean on your outsourced partners, who have the skills and knowledge required to understand data management, statistical analysis, and the principles of machine learning. This is essential to make informed decisions when developing and deploying AI models effectively.

4. Add governance, ethics, and sustainability to your data strategy

AI doesn’t just need accurate data; it needs responsible data. Update your product data health checklist to include:

• Ethical data sourcing and usage (avoid bias, respect privacy)
• Sustainability attributes (materials, lifecycle, impact) to meet regulatory requirements like the EU’s Digital Product Passport
• Governance workflows to ensure transparency, compliance, and trustworthiness in AI outputs
  

Bridging the gap: How Productsup’s partnership with Protokol simplifies DPPs

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5. Pilot and scale responsibly

Start small by piloting AI use cases in controlled environments. Measure performance, identify risks, and refine. Once you see clear business value, scale across more channels or product categories with confidence.

While smart technologies have had an extensive impact on the global ecommerce sector, such as generative AI, which is now valued at $5.92 trillion, realizing their effects comes down to having good data and transitioning your data management practices to align with your AI goals.

Is your product data ready to see the ROI of AI? Learn more about how Productsup’s platform is equipped to take your raw, complex data and enhance it with AI to deliver flawless product content journeys.

Explore our AI resources:

• [Overview] AI features within the Productsup platform
• [Guide] The state of smart commerce: AI’s impact on buying and selling
• [Blog] Regular expressions and Twigs