Merchandise planning teams across retail are exploring how AI can improve forecasting, allocation, and planning decisions. The potential is clear: faster insights, better pattern recognition, and more responsive execution. But before AI can deliver meaningful value, organizations need to understand whether their data is ready to support it.
AI readiness is often discussed in broad terms, but for merchandise planning teams, it comes down to a few concrete areas. These include data availability, the ability to process and connect information, and the organizational practices that ensure data can be used consistently over time. Among these, data readiness is often the most immediate and impactful place to start.
This blog focuses on how merchandise planning teams can assess and strengthen their data foundations to support AI-driven decision-making — without waiting for perfect conditions.
The data challenge in modern merchandising
Merchandise planning teams have access to more data than ever before. Point-of-sale transactions, inventory records, web analytics, customer feedback, and operational systems all contribute to a rich information landscape.
In theory, this abundance of data should lead to better decisions. In practice, the challenge is not volume, but structure. Data that was collected for operational or reporting purposes may not be organized in a way that supports advanced analytics or machine learning.
AI systems depend on clear, consistent relationships between data points. When those relationships are incomplete, ambiguous, or inconsistently defined, AI outputs become harder to trust. Preparing data for AI is less about adding new sources and more about ensuring existing data can be interpreted reliably.
3 data priorities for merchandising teams
Merchandise planning teams preparing for AI adoption tend to focus on three foundational areas. Addressing these does not require a full system overhaul, but it does require clarity and coordination.
1. Strengthen data hygiene practices
Data hygiene refers to how data is maintained, updated, and governed over time. For many teams, this is an area that has evolved organically rather than by design.
Key questions to consider include:
Who owns each core dataset, and what does ownership mean in practice?
Which datasets are routinely reviewed for accuracy and completeness?
How are outdated or unused records handled?
Are there clear processes for updating and archiving data?
Establishing basic standards and accountability helps reduce ambiguity and makes downstream analysis more reliable. Even incremental improvements in hygiene can have a meaningful impact.
2. Improve consistency in naming and definitions
AI systems are sensitive to inconsistency. Differences in how products, categories, attributes, or locations are named across systems can lead to confusion and inaccurate results.
Merchandise planning teams should review:
Whether product identifiers and attributes are consistent across systems
How naming conventions have changed over time
Whether buying, planning, and allocation teams use the same definitions
Consistency does not require perfection, but it does require agreement. Shared definitions help ensure AI models interpret data the same way teams do.
3. Consolidate and connect data sources
Data often lives across multiple systems and repositories, each with its own structure and access rules. While full centralization may not be practical, improving how data is connected and standardized can significantly improve AI effectiveness.
Areas to assess include:
How many systems serve as sources of record for key data
Whether consistent identifiers link records across systems
How data is shared and updated between platforms
Whether a common data model or structure is applied
Better-connected data reduces gaps and helps address challenges like data sparsity, where limited history for individual products can affect forecast accuracy.
What AI-ready data looks like in practice
Retail organizations that succeed with AI typically share a few characteristics:
Clear ownership and documentation for critical datasets
Consistent identifiers across inventory, transaction, and product data
Complete and reliable product metadata
Well-defined logic for returns, discounts, and adjustments
Validation processes that surface issues early
AI-ready data is not about eliminating every exception. It is about creating enough clarity and consistency for models to learn from patterns rather than noise.
Preparing for what comes next
Getting data ready for AI is an ongoing process, not a one-time project. Merchandise planning teams that approach it incrementally — starting with hygiene, consistency, and connectivity — are better positioned to apply AI effectively as capabilities evolve.
Perfection is not the goal. Progress is. Most retailers already have data that can support meaningful AI use today, as long as teams are thoughtful about how it is prepared and maintained.
With a clear foundation in place, AI can become a practical tool for improving merchandising decisions, not an abstract ambition.
