Learn how to optimize Microsoft 365 Copilot Connectors by applying data modeling best practices and intentional search configuration.
In our previous blogs, we explored how Microsoft’s AI tools can support enterprise reporting and the foundational readiness needed to use them successfully. Today, we’re diving deeper into Microsoft 365 Copilot and how thoughtful connector configuration can provide better results. As we’ll see, preparing data for Copilot is not simply a configuration task; it is fundamentally a data modeling exercise.
When organizations implement Microsoft 365 Copilot Connectors, the focus is often on establishing the connection and indexing the data. What receives far less attention is how the data is configured once it reaches the search index. While evaluating Microsoft’s AI tools for enterprise reporting, one lesson surfaced repeatedly: successful AI experiences depend less on prompting and more on how the underlying data is structured. Copilot Connector configuration is a perfect example. What appears to be an administrative task is actually a series of data modeling decisions that directly influence what information Copilot can find and how effectively it can return it.
1. Use Business Terminology, Not Database Terminology
Copilot is attempting to bridge natural language questions with indexed data. The easier that translation becomes, the better the user experience. If your source view contains technical abbreviations, consider exposing business-friendly alternatives.
Database Field | Business-Friendly Name | Why It Matters |
CustNbr | Customer Number | Users understand the business term and may include it in prompts. |
PrdCatCd | Product Category | Aligns with how users naturally describe products. |
NetRevYTD | Year-to-Date Revenue | Converts a technical metric into a recognizable business concept. |
Think about field names as part of the user experience. If users would not naturally use a field name in conversation, it may not be the best name to expose to Copilot.
2. Be Intentional About Searchable Fields
One of the first decisions you’ll face is determining which attributes should participate in search. Rather than making every field searchable, focus on the attributes users are most likely to reference when asking questions. For example, the following are excellent candidates because users naturally search using these concepts:
- Customer Name
- Product Name
- Product Category
By contrast, the following are rarely useful search terms and may provide more value as retrievable fields:
- Internal IDs
- Technical keys
- Audit fields
This exercise can be surprisingly valuable because it forces you to think about how users actually interact with the data instead of simply exposing every available column.
3. Configure Attributes Based on User Behavior
Not every field needs every attribute assigned. The most effective configurations are driven by the questions users are asking rather than by technical availability.
Field | Searchable | Retrievable | Queryable | Why This Configuration Makes Sense |
Customer Name | ✅ | ✅ | ✅ | Users frequently search for customers by name and use this information to narrow results. |
Year-to-Date Revenue | ❌ | ✅ | Optional | Revenue is valuable context in responses but rarely serves as a search term. |
Internal Record Identifier | ❌ | Optional | Optional | Most users do not know or reference internal record IDs when asking business questions. |
Just because a field can be marked as searchable does not mean it should be.
4. Start With User Questions, Not Database Columns
One of the most useful design exercises is identifying the questions users are likely to ask before building the connector.
User Question | Supporting Fields |
Tell me about Acme Corporation. | Customer Name, Sales Region, Year-to-Date Revenue |
Which customers are assigned to Sarah Johnson? | Customer Name, Account Manager |
Show me products in the outdoor equipment category. | Product Name, Product Category |
Working backward from expected questions often reveals that many columns considered important by a technical team are irrelevant to the actual user experience.
Final Thought
Throughout our AI reporting evaluations, we found that organizations achieve the best results when they focus on data readiness, governance, and intentional design before introducing AI. Microsoft 365 Copilot Connectors are no different. The field names you expose, the attributes you assign, and the information you make searchable all influence the quality of the answers users receive. That’s why we view connector configuration as more than an administrative exercise. It’s a data modeling exercise and often one of the most important design decisions you’ll make when preparing enterprise data for AI.
Considering Microsoft 365 Copilot or other AI-enabled reporting solutions? Our team has evaluated multiple approaches within the Microsoft ecosystem and can help identify the right fit based on your data, governance requirements, and business objectives.
