How Power Platform scales generative AI across an organization
Power Platform scales AI and the data platform by providing a composable means of both data collection and delivery of insights and AI capability back to the user. Meanwhile, the great, often unsung capability of Power Platform is not the “app”, rather the ability (via Dataverse) of data transacted in a Power Platform solution to hydrate downstream data distribution scenarios such as analytical workloads, enterprise search, and—you guessed it—whatever AI infused workload you dream up. Let’s explore this.
RAG and the fundamentals of AI acting on enterprise data
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 their AI strategy 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. This approach is what we call “Retrieval Augmented Generation” or “RAG”, which you may have heard of. The name is quite literal: Here we are augmenting the generative pre-trained (and now you know what “GPT” stands for) model with data that we have retrieved from the organization’s data estate.
Power Platform in a Modern Data Platform Architecture
I’ve been thinking quite a bit lately about Power Platform as one of the three principal components of the one Microsoft Cloud, alongside Azure and Microsoft 365 of course. This is particularly important in more complex data ecosystem, one of the enterprise management dimensions you’ll find in the Power Platform Adoption Framework. So I want to expand on the “data ecosystem” concept with the idea that modern data platform architecture is a wheel or a cycle (rather than a linear flow), particularly when Power Platform solutions are leveraged (and they should be).