Workshop contents
Session 2 of 4
Connecting AI to Enterprise Data
Helping AI understand the business behind the data
Suggested direction. This session outline is a starting point, not final workshop content. The examples and depth should be adjusted to the participating organization's data systems, data quality, priorities and current AI maturity.
From Data to Context
AI becomes more useful when it can connect numbers to business meaning. This session shows how raw operational data becomes the context AI needs before it can support a serious business decision.
| Stage | Plain meaning | Why it matters for AI |
|---|---|---|
| Raw records | Orders, shipments, customers, products and costs | AI can only analyze the facts it can access. |
| Business meaning | Definitions, targets, service promises and exceptions | The same number can mean different things in different businesses. |
| Connected systems | ERP, warehouse, transport, finance and customer data working together | AI needs the full picture, not one isolated spreadsheet. |
| Trusted context | Clean, current and explainable information | Leaders need to know where the answer came from before they act on it. |
What This Session Covers
The focus is the information layer behind enterprise AI: where the data comes from, what it means, and how it connects to daily work.
- Where important business data lives across the company
- How tables, documents, messages and system notes each give AI different context
- Why clean, complete and current data matters for decision quality
- How workflows connect AI output to real operational work
- What must be in place before AI moves from a demo into daily use
The Question This Session Answers
Key questionHow does operational data become usable context for AI?
How the Pieces Connect
| Business need | Information AI needs | Better output |
|---|---|---|
| Reduce late deliveries | Order dates, promised dates, actual delivery dates and carrier details | A clear view of where delays happen most often |
| Protect profit | Order margin, shipping cost and customer value | Recommendations that do not solve service by destroying margin |
| Prioritize action | Targets, budget limits and customer commitments | A ranked plan that reflects how the company actually works |
What You Leave With
Participants leave with a practical way to judge whether AI has enough business context to be trusted. The lesson is simple: enterprise AI depends on connected, trusted and usable information, not isolated prompts or standalone tools.
Shared example. We use the same sanitized DataCo supply chain extract across the workshop so each session builds on the same business situation.