When building AI-powered business solutions, it is not enough to prepare data for a single model or agent. You also need to ensure that data can be accessed, understood, and safely used by other AI systems that may run alongside it. This is essential for multi‑agent designs, pipelines, and environments where tools such as Copilot Studio, Microsoft 365 Copilot, Dynamics 365, and Foundry all rely on shared information.
Below, we explore how to organise your data so that it can serve multiple AI systems reliably and securely.
Understand the Requirements of the Other AI Systems
The starting point is simple. Before moving or restructuring anything, confirm what the consuming AI systems actually need. For example, do they require structured datasets, raw files, embedded knowledge sources, or indexed content? Requirements differ widely between systems like Copilot Studio, Azure SQL Database, Dataverse, or SharePoint-based knowledge ingestion.
This review naturally connects with understanding grounding data requirements, especially around data accuracy, availability, and timeliness.
Ensure Data Lives in the Right Place
Different AI systems may only support specific types of storage. Common locations include:
- OneDrive,
- SharePoint, and
- AI‑optimised databases such as Azure SQL Database or Fabric SQL.
Additionally, some data may need to be kept in Fabric or Azure storage for non-database scenarios. The key is consistency so that all systems can access the same data without duplication or manual transfers.
If automated movement is required, consider using flows or pipelines to keep data synchronised. This can also help address scenarios where file count limitations apply, such as Copilot Studio Lite allowing only 20 knowledge sources. In such cases, you may need to merge files or adjust where they are stored.
This overlaps with broader considerations around data processing #see topic 31#.
Manage Permissions and Access Controls
Even when two AI systems can technically access the same data, they should only do so if permitted. That means managing:
- credentials,
- role‑based access control (RBAC), and
- restrictions for sensitive or confidential content.
Sometimes, entire datasets should not be accessible to a particular AI system. In other cases, access must be constrained by role, task, or environment.
This theme also applies directly to data residency and compliance #see topic 72# and access control on grounding data #see topic 73#.
Be Aware of System Limitations
AI systems often have limitations on:
- accepted file types,
- the number of files,
- the speed or frequency of indexing, and
- processing constraints.
For example, some systems cannot read all graphical file types, and others (such as Copilot Studio Lite) restrict the number of files or knowledge sources. Understanding these nuances prevents ingestion failures and ensures consistent performance across your solution.
Check That Data Is Representative
When preparing data for use across multiple AI systems, representation matters. If training or grounding data does not reflect real‑world scenarios, it can lead to incorrect outputs. For instance, images used to train a vision model must avoid patterns that unintentionally bias the model, such as always showing apples on plates and oranges in trees. Otherwise, unusual combinations may confuse the system.
This emphasises how organising data also connects to designing multi‑agent solutions #see topic 6#, since shared data must serve consistent, correct outcomes across agents.
What’s Next?
Organising business solution data so that it works seamlessly across AI systems is essential for reliable AI deployments. By selecting the right storage location, controlling permissions, respecting system limitations, and ensuring representative data, you create a foundation that supports scalable and secure AI‑powered solutions.
If you’d like a structured explanation of all these AB‑100 “Agentic AI Business Solutions Architect” requirements, our AB‑100 video course guides you through each topic – or go back to the topics in the AB‑100 exam.
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