Better Data, Smarter Merchandising: How To Get AI Ready

In the rush to unlock AI’s potential, retailers everywhere are moving fast. Smarter forecasting, faster planning, automated decision-making — the impact of AI in merchandising is real, and it’s arriving fast.

But behind every high-performing AI tool is something far less glamorous: clean, well-structured data.

At Syrup, we’ve seen firsthand how a lack of data hygiene can quietly derail even the most advanced AI initiatives. Not because teams aren’t smart or because their systems are broken.

It’s because the data, especially around inventory and transactions, wasn’t originally set up to support machine learning.

The good news? You don’t have to overhaul everything to get AI-ready. You just need to start asking the right questions.

Why AI Demands Cleaner Data

In traditional reporting, imperfect data might slow you down. In AI-driven systems, it can stop you entirely.

AI tools rely on precise, consistent relationships between data points: SKUs tied to accurate metadata, transactions linked across tables, and timestamps that reflect reality. If those links are missing or fuzzy, your AI output will be too.

In other words: if you want high-quality AI recommendations, you need high-quality inputs.

How to Get Ahead — Before You Even Start

If you’re even thinking about bringing AI into your merchandising stack, now is the perfect time to audit your data. Most of it likely lives across spreadsheets, ERPs, and databases — and in many cases, this will be the first time it’s being connected to a live system that’s making real decisions.

Start by reviewing your inventory and transaction datasets with a critical eye. Clarify how values like discounts, returns, and in-transit inventory are treated, and make sure key metadata like product category, tier, and season are complete. Identify which fields are absolutely essential, and ensure that consistent identifiers like SKUs, store numbers, and order IDs are being used across tables.

This isn’t just about cleanup — it’s about clarity and ownership. Understanding who owns what, and ensuring teams speak the same data language, will pay off well beyond onboarding.

The Most Common (and Avoidable) Data Pitfalls

Here are some of the most frequent issues we encounter during onboarding — and questions you can ask right now to stay prepared for AI implementation.

Inventory Data: What to Check

• When are daily stock snapshots taken?
• How is in-transit inventory captured? Is it broken out clearly or blended with available stock?
• Do receipt and transfer dates reflect physical movement or data entry time?
• Are e-commerce transfers treated the same as store transfers?
• Are store IDs consistently referenced across tables? Can you geocode stores if coordinates aren’t provided?
• Is there a consistent SKU structure (e.g., article + size)? And is it used across datasets?
• Are product attributes complete? Think: category, tier, color, fabric, season.

Transaction Data: What to Clarify

• Are returns included — and are they clearly marked?
• Are units and revenue shown as negative for returns?
• How are discounts tracked? At the line level or overall?
• Do values reflect unit prices, gross revenue, or something else?
• Are transactions linked with consistent order and item IDs?
• Do transaction dates represent order, ship, or delivery date?

Even small inconsistencies, like return units not being negative, can lead to major distortions in how AI interprets demand and performance. These aren’t just technical issues — they directly impact how well AI supports your decisions.

What “AI-Ready” Data Actually Looks Like

High-performing data teams share a few traits:

• Clear ownership over each dataset — and confidence in what each field means.
• Consistent identifiers across tables: SKUs, store numbers, orders.
• Robust metadata to help AI segment and learn: product tiering, category, fabric, and more.
• Return logic that’s, well, logical — with accurate dates, values, and links to original orders.
• Validation tools that catch issues before they slow things down.

At Syrup, we’ve built a validation script and checklist to help teams catch issues early — long before onboarding becomes a bottleneck. Clean, consistent data doesn’t just make onboarding faster; it ensures the insights you get are actually accurate and actionable.

Preparing For What’s Next

AI implementation is already reshaping how leading brands operate. The retailers who will win — and stay winning — are the ones who treat data like the asset it is. If you’re ready to explore AI-powered merchandising, make sure your data is ready to meet the moment.

Need help getting there? Syrup has helped dozens of brands like yours validate, refine, and maximize the impact of their data. We’re happy to help you do the same.

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