AI is here, and is already transforming the way we search, shop, and engage with brands and products. From intent-based search and real-time personalization to virtual shopping assistants and autonomous buying agents, AI is redefining every step of the B2C and B2B buyer’s journey. But here’s the hard truth: AI is only as good as the data it is fed. The only way to navigate the AI transformation and have a top-line impact is to prioritize clean, structured, and consistent product information.
is the projected value of AI use in eCommerce by 2030.
is the average revenue increase seen by companies leveraging AI.
of B2B organizations either already use or are planning to use AI-powered technology.
of consumers want genAI integrated into their shopping experience.
Enhance the product experience to increase conversion and decrease returns
AI is only as powerful as the data behind it. No matter how advanced an AI model is, it can’t deliver accurate recommendations, relevant search results, or seamless shopping experiences if it’s working with messy, incomplete, or inconsistent product information. But when your product data is clean, structured, and well-managed, AI can help customers find exactly what they need, when they need it.
Before you can unlock the full potential of AI, you need to ensure your product data is ready to support it. AI thrives on structure, consistency, and quality—but all too often, product information is scattered, incomplete, or riddled with inconsistencies that limit the effectiveness of AI-driven solutions. From auditing your existing data to eliminating silos and implementing strong governance practices, getting your data AI-ready ensures you can scale your efforts, drive better business outcomes, and deliver seamless, compelling product experiences across every channel.
Before implementing any AI-driven strategies, businesses need to take a long, hard look at the data they already have. This means conducting a thorough audit to identify any inconsistencies, errors, or gaps in product information, checking for duplicate entries, incomplete data fields, outdated information, and variations in data formats.
To enable AI to effectively process and understand product data, it needs to be organized and structured in a consistent way. This means standardizing product attributes (e.g., color, size, material) and metadata (e.g., product category, brand, SKU). By establishing clear guidelines for how product information is captured and stored, businesses can ensure that AI algorithms can easily access and interpret the data.
In many organizations, product information is scattered across multiple systems and departments. This can lead to data silos, inconsistencies, and inefficiencies. To overcome this challenge, businesses should aim to create a single source of truth for product information—a centralized repository where all product data is stored and managed.
Maintaining clean data is everyone’s responsibility. Businesses should provide training to all employees who handle product information, emphasizing the importance of data quality and the impact it has on AI-driven initiatives. By fostering a data-centric culture, businesses can ensure that everyone understands their role in maintaining accurate and reliable product data.
Maintaining high-quality product data is an ongoing process, not a one-time task. This is why businesses need to implement data governance policies that define roles, responsibilities, and procedures for managing product information. Data governance ensures that data remains accurate, complete, and up-to-date over time.
Here is the good news! You can now leverage Akeneo’s Data Architect Agent to generate flexible, accurate, and customizable data models with ease, eliminating the need for lengthy workshops, endless revisions, and numerous spreadsheets.
Unlike competitors requiring extensive manual modeling, DAA delivers 100% implementation success rates while reducing total costs by 50-70% and turning 3-6 month modeling phases into days.
Don’t just imagine the possibilities. Experience them. Request a personalized demo and discover how our AI-powered solutions like the Data Architect Agent can help you streamline operations, improve product experiences, and drive smarter commerce at scale.
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