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.
Technology considerations when scaling AI across an organization
Join me—Andrew Welch—with HSO’s "Dynamics Matters” podcast host Michael Lonnon for part five of our AI strategy miniseries as we return to the centrality of data in this episode, talking Microsoft Fabric and the future roadmap for Microsoft’s data + AI technologies, the importance of ecosystem-oriented architecture (EOA) to scaling artificial intelligence, the need for organizations to change how they budget technology initiatives, and how to organize an IT team for the age of AI as the IT Tower of Babel rears its ugly head once again.
White Paper: “Crafting Your Future-Ready Enterprise AI Strategy”
It is yet unknown if artificial intelligence is more akin to a “great inventions” of the 19th and 20th centuries, or if it will ultimately represent another more incremental evolution of existing capabilities. The former—as seems more likely given the immense investments being made today—will present significant challenges to nearly every organization that, having become accustomed to incremental change, is suddenly faced with a “great inventions” caliber paradigm shift that AI seems to portend. This white paper explores foundational concepts for AI technology and your AI strategy, five pillars for your AI strategy—data consolidation, data readiness, incremental AI, differential AI, scaling AI—and trends that are likely to shape AI in organizations going forward.
Human and organizational considerations in “Scaling AI”
In time, most organizations will turn their attention from future readiness and establishing themselves with AI to focus instead on scaling (and sustaining) their investment in AI and the data platform upon which it depends. Put another way, one-time consolidation and readiness of data combined with a few AI-driven workloads does not a future-ready organization make. You see, there are non-technical organizational and human considerations that should be taken as you scale AI across the organization. This significant element of people-centric scaling and change management is required here, in other words, to scale AI by baking it into the way people work.
Differential (Aspirational) AI
We’re on to what I think popular culture would consider the “fun” bit, or at least the bit that dominates the imagination when it comes to artificial intelligence’s supposedly boundless possibilities. Whereas Incremental AI includes the scenarios that improve upon solo human performance of activities that would have been performed anyway, “Differential AI” broadly encapsulates workloads that would not have likely been performed by humans alone, scenarios that are valuable to the organization because they allow you to jump out ahead of your competition, to offer your customers something that you’d not have otherwise been able to provide. I toyed with the idea of calling this “Secret Sauce AI” or “Moonshot AI” to underscore the point.
Incremental AI for the people!
What I will call “Incremental AI” is a broad, conceptual category of workloads in which AI is applied to bring speed, efficiency, scale, accuracy, quality, etc. to activities that a human would have otherwise performed. Microsoft’s Copilot capabilities (for the most part) squarely fall into this bucket as they help their end user to find information more granularly, identify highest-potential sales targets more accurately, create content more quickly, book appointments more efficiently, write code more effectively, etc.
AI = Data, good and ready
Join me—Andrew Welch—with HSO’s "Dynamics Matters” podcast host Michael Lonnon for part one of our AI strategy miniseries as we geek out about data consolidation and AI-addressable data storage, Microsoft Fabric, data hygiene, data readiness and the “cloud landing zone”, data distribution and more as you craft + execute your future-ready AI strategy.
Building future-ready cloud ecosystems for the age of AI
Discussing AI in recent months I have often thought about the fable of the boiled frog, whereby a frog placed in boiling water jumps out, but a frog placed in warm water that is gradually heated lacks awareness of his impending demise until it is too late. Or, as I continue to remind the CIOs with whom I work closely, the grace period for organizations to get their act together and position themselves for the next wave is growing much shorter, the margin for error much more narrow.