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We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time for a proper model through which organizations may realistically assess their readiness to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey. This AI Strategy Framework guides organizations as they construct their AI strategy atop five pillars, each with five dimensions to be considered, matured, and regularly evaluated.
When taking on the question of how Power Platform integrates with Azure data services, Point-to-Point, Data Consolidation, Master Data Node, and Data Distribution evolve a similar theme. Specifically, each focuses primarily on transactional data during any given users interaction with it. “Data Distribution” is different, focusing more on data distributed for analytics, enterprise search, integration with third-party or external sources via API, data science workloads, or training or augmenting a large language model (LLM). This blog overviews the Data Distribution pattern.
When comparing architectural models for Power Platform, it’s important to avoid the instinct to choose just one. Instead, the goal is to explore various approaches that enable different scenarios for integrating Power Platform solutions with enterprise data. Each organization should strategically mix and match these approaches, considering factors like performance, flexibility, maintainability, and cost. This strategy allows for creating adaptable patterns within a cloud ecosystem where Power Platform plays a key role.
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.
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.
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.
I have come to understand strategic thinking as more art than science. Strategy is surely informed in part by data, but I find that our society in general and the technology industry specifically too often confuse data and wisdom, that we foolishly (though understandably) seek heuristics or processes so that we can turn the art of strategic thinking into a game of color by numbers so simple that anyone can do it. When it comes to strategic thinking, better that we seek methods of framing our thoughts rather than shortcuts to the answers themselves. So in this piece I have sought to offer practical approaches to injecting strategy into your organization’s Cloud journey, indeed, a selection of the same approaches I take with my clients.
Too many organizations are still struggling to go big with Power Platform not because of limitations in technology, but because of their own outmoded ways of doing business, though I know of no other move that IT decision makers in organizations across the economy and around the world can make that is likely to achieve results of this magnitude by—in effect—doing more with less. But the benefits of adoption are often dampened by three big, non-technical reasons that I see so many organizations failing or underperforming in their scaled, enterprise Power Platform adoption.
The Power Platform Landing Zone is the beginning of the path to overcoming these barriers. A foundation, if you will, the Landing Zone is the initial technical infrastructure plus governance of that infrastructure that allows an organization to begin “landing” workloads in Power Platform. With that in mind, a while back I set out to create a reference architecture for a Power Platform Landing Zone. In other words, if an organization were to build their Power Platform infrastructure properly, it would look a lot like this reference architecture.
Let’s consider a model for how organizations should be prioritizing their work and investments in the Microsoft Cloud. The imperative here could not be greater. Technological advancements are now moving on timelines that in some instances can be measured in weeks. Not months. Not years. But weeks. This both accelerates and is accelerated by the shift to system-based value. In other words, getting the platform ecosystem right in an organization is both necessary to creating the greatest likelihood that the organization can absorb rapid innovation, whilst simultaneously creating the conditions that drive that rapid innovation forward. But too many organizations have misallocated their focus up and down the value chain, prioritizing workload implementation either at the expense of or out of ignorance to architecting strategic foundations, building the platform ecosystem, and creating the conditions for success. That’s a bit esoteric, so let’s visualize this phenomenon as the “strategic pyramid”…