Trustrorthy AI
Knowledge Base
How generative AI acts on enterprise data
Let’s establish a basic understanding of how AI uses and acts on enterprise data. We will define 'enterprise data' as data that is proprietary to a specific organization, kept and (I certainly hope!) secured inside the boundary of the organization’s data estate.
You may be familiar with the term “RAG”, an acronym for “retrieval augmented generation”. While this is not the sole means through which AI acts on organizational data - and new and evolving patterns now emerge regularly - RAG represents a good baseline for the general concept through which nearly all AI workloads essentially augment an existing model with an organization’s proprietary data.
Figure 2: Use these five pillars to evaluate an organization’s maturity, risks, and opportunities in AI at any point in time. Then build your strategy to mitigate those risks, seize those opportunities, and mature the organization’s use of AI over time.
In the top-right of the diagram we’re looking at various data sources sitting in a modern data platform (Azure SQL, OneLake, and Blob Storage are shown top to bottom for representative purposes). Blob Storage is a highly efficient way to store unstructured data, that is, files, images, videos, documents, etc. In this simple scenario we’ll say that unstructured data is drawn from Blob.
These data sources are indexed by Azure AI Search (formerly called “Cognitive Search”), which also provides an enterprise-wide single search capability. Moving to the far left we see an application user experience (UX) e.g., a mobile, tablet, or web app that provides an end user the ability to interact with our workload.
The application sitting beneath the UX queries the knowledge contained in Cognitive Search’s index (as derived from the data sources on the right). It then passes that prompt and knowledge to Azure AI services to generate an appropriate response to be fed back to the user.
CIOs and enterprise architects need not be experts in the technical mechanics of AI to formulate and execute an effective AI strategy. That said, it is critical that leaders driving this strategy must understand this basic concept of how institutional AI - that is to say, AI workloads specific to your organization - both requires and acts on enterprise data.
Without that data, it’s just AI, unspecific to the organization it is serving.