Getting Your Data Ready for Agentic Systems and GenAI: A Practical Guide for CPG Manufacturers

The next generation of value creation in the CPG industry will be built on data that's not just clean—but connected, contextualized, and ready to act. In the race toward more predictive and automated decision-making, “agentic” is gaining traction. For CPG manufacturers, acting in an agentic way means empowering systems (not just humans) to sense what’s happening, diagnose root causes, and recommend or even act on the best next steps.

It’s not that CPG companies don’t have data. It’s that their data isn’t structured in a way that autonomous agents, decision orchestration layers, or predictive copilots can understand and act on. You can’t automate what you can’t access or trust.

So where should you start?

Here are five practical steps to begin preparing your data for agentic automation and GenAI-enabled intelligence:

1. Start with the “Golden Records” That Matter Most

Agentic systems thrive on high-quality, authoritative data. Begin by identifying and cleansing the most critical master data objects—your Golden Records—across the commercial value chain:

Products / SKUs (GTIN, pack size, category, claims; enhanced with planning and consumer decision tree hierarchies)
Customers / Retailers (store type, location, segmentation; enhanced with hierarchies that reflect corporate strategy)
Trade Events / Promotions (mechanic, duration, expected lift)
Retail Conditions (store planograms, pricing zones, OSA targets)

These are some of the foundational building blocks that almost every sales, marketing, and forecasting bot will need to reference and connect.

2. Organize Data by Use Case, Not Just by Source

Most CPG data lakes were designed for reporting by function, not action. In an agentic model, data should be organized by business process or use case:

“What data does a pricing agent need to set optimized guardrails?”
“What data does a retail execution agent need to recommend a store visit?”

Re-bundling and structuring data around these agent workflows is critical. Create data products aligned to moments of decision—assortment planning, trade negotiation, OOS root cause, etc.

3. Ensure Semantic Consistency Across Domains

Agents operate across silos. Your product data, customer hierarchies, trade events, and demand forecasts must speak the same language. That means:

Aligning naming conventions and taxonomies
Creating and governing standard attributes (e.g., size class, value tier)
Unifying location hierarchies (store → DC → region)

If a GenAI bot asks “Which high-velocity SKUs are at risk of out-of-stock in Kroger’s urban format stores?”, the system has to know what each of those terms actually refers to.

4. Capture the Metadata and Lineage

Bots don’t just need the what—they need the where and why. That means keeping track of:

Data source lineage
Last refresh date
Business owner or steward
Confidence score or trust rating

Think of this as building the “nutrition label” for each data product. Agents and copilots will increasingly weigh data quality dynamically, and this metadata fuels that judgment.

5. Automate Data Readiness with API and Event Hooks

Once your data is clean and structured, the final step is to make it actionable. That means building in:

APIs to expose and consume data products
Event-driven architecture to signal when something has changed
Bot-readable outputs (JSON, XML, structured tables, not PDFs or PowerPoint)

Think: How would a smart retail execution agent subscribe to planogram violations or OOS alerts and know which action to trigger next?

Final Thought: Think Crawl-Walk-Run

You don’t need to boil the ocean. Start with one use case where data quality is holding back automation—say, trade promotion post-event analysis or POG compliance recommendations. Clean and structure that data, then pilot an agent or GenAI copilot against it.

Each success builds your capability—and your confidence.

The age of agentic commerce is coming fast. The winners will be those who treat their data like a product, not a byproduct—and make it ready for the bots who are about to start doing the work.

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Originallly published on LinkedIN on July 8, 2025; https://www.linkedin.com/feed/update/urn:li:ugcPost:7348338790760923137/