Retailers are moving quickly to adopt AI for merchandise planning. Smarter demand forecasting, faster scenario modeling, and more automated decision support are no longer future concepts — they’re becoming part of everyday operations.
But behind every successful AI initiative is something far less visible: data that is clean, consistent, and structured for analytical use.
Many AI efforts stall not because the technology falls short, but because the underlying data was never designed to support advanced analytics or machine learning. Inventory and transaction data, in particular, often reflect years of operational workarounds rather than a clear foundation. Becoming AI-ready is less about rebuilding systems and more about strengthening the data foundations that support accurate, trustworthy decision-making.
Why AI places higher demands on data quality
In traditional reporting, imperfect data can often be managed. Analysts adjust assumptions, explain anomalies, or work around inconsistencies. AI systems don’t have that flexibility. AI relies on clear and consistent relationships between data points: products linked to accurate attributes, transactions connected across systems, and timestamps that reflect what actually happened — not when something was entered. When those relationships are incomplete or ambiguous, the outputs that depend on them become unreliable.
In short, better AI outcomes depend on better inputs. Without that foundation, even the most advanced models will struggle to deliver usable insight. This foundational work begins with a critical look at the core data that powers merchandising decisions.
Assessing your data: Key questions for AI readiness
For many retailers, AI represents the first time inventory and transaction data is being used in a live, decision-making context rather than retrospective analysis. That shift makes data readiness especially important. A practical place to start is by reviewing core datasets with a critical lens, clarifying how key values are defined and ensuring consistency across systems.
Inventory data considerations:
- When are stock snapshots taken, and do they reflect actual availability?
- Is in-transit inventory tracked separately or blended with on-hand stock?
- Do receipt and transfer dates represent physical movement or system updates?
- Are store and SKU identifiers used consistently across datasets?
- Is product metadata — such as category, tier, season, or size — complete and standardized?
Transaction data considerations:
- Are returns clearly identified and represented with correct units and values?
- How are discounts recorded, and at what level of detail?
- Do transaction dates reflect order, shipment, or delivery?
- Are transactions linked using consistent order and item identifiers?
Even small inconsistencies can affect how AI interprets demand and performance. In practice, these issues influence forecast accuracy, allocation decisions, and downstream financial results.
What "AI-ready" data looks like in practice
By systematically addressing these data considerations, retailers can build a data ecosystem that shares the common characteristics of AI leaders. Organizations that succeed with AI tend to share a few common traits:
- Clear ownership and documentation for each dataset
- Consistent identifiers across inventory, transaction, and product tables
- Robust metadata that supports segmentation and pattern recognition
- Well-defined return and discount logic
- Validation processes that catch issues before they affect downstream planning
AI-ready data is defined by its reliability, transparency, and ability to support planning decisions across teams and systems.
Preparing for what comes next
AI is reshaping how merchandising and planning decisions are made. Retailers that see the most value are those that treat data as a strategic asset, not just a technical requirement. Anaplan’s retail planning solutions streamline the process from data assessment to data integration, so you are ready for what’s next.
Strong AI outcomes begin with strong data foundations. Getting AI-ready starts with asking the right questions about how your data is structured today — and making targeted improvements that support better decisions tomorrow.
