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.
Why don’t more engineers embrace Power Platform as enterprise-grade?
Did the original positioning of Power Platform as a “low-code” or “citizen developer” tool do a massive disservice to the enterprise nature of the Power Platform itself? Are big organizations that have signed Power Platform into enterprise agreements with Microsoft looking at downsizing their investment because they don’t understand—and have not put to work—the Power Platform’s capability as an enterprise-scale application and data platform? So, why aren’t more engineers, architects, and IT leaders maximizing it as the enterprise platform that it really is?
Moving IT from “utility company” to “strategic leaders of the business”…
Do innovative technologies need to move out of IT in order to thrive, or do we need to move governance and security out of IT and into facilities management as our “workplaces” have become increasingly tech-centric? There is a difference between achieving an acceptable level of security and governance vs. having a forward-looking strategy that is going to grow the business, return investment to the business, modernize the business, or make the business more productive.
AI Applied CIO roundtable, hosted by HSO International 🇳🇱
Andrew Welch presented a keynote and facilitated a CIO roundtable at the invite-only AI Applied event, hosted by HSO International (a Cloud Lighthouse partner)… Last year was a stand-out year for AI developments. According to Gartner, generative AI technologies reached the peak of the hype cycle. It's now time to step away from the hype and move towards making practical plans to leverage the power of AI in your organization.
How to build and execute a cloud roadmap, and then connect the dots?
How do I build and execute a roadmap and strategy? Not a high-level business strategy that organizations typically receive, but one that actually bridges the gap between business outcome and business value that can be achieved in the cloud. How do I execute this plan, and what are the dots to connect in achieving these outcomes? Why are more organizations not doing so? The Ecosystems Podcast welcomes James Joyce, our first (but not last) ever guest.
Foundations, investment trends, and how CXOs can lead in the age of AI
How do you have AI conversations with CTOs, CIOs, CDOs, and others in technical or business leadership roles? What do I need to be thinking about for my organization, particularly while “every man and his dog” are publishing their own AI content? It’s overwhelming right now.
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.
The road ahead for organizations pursuing their strategic AI aspirations
Organizations have a lot of work to do in pursuing their AI aspirations in a strategic way, and importantly, to correct some of the mistakes they’ve made kicking the (data) can down the road for the last thirty years. This episode dives into the five pillars set out in the recent Microsoft + Cloud Lighthouse white paper, Crafting your Future-Ready Enterprise AI Strategy, as we help organizations become “future ready” for the AI we understand today and the AI capabilities that are coming in the very near future. The hosts discuss the preparation for artificial intelligence in 2024. They highlight the importance of data consolidation, governance, and the use of AI tools for quick wins. The conversation also touches on the practical applications of AI in daily life and the need for AI to be infused into all aspects of work. The hosts explore the excitement and optimization potential of AI, as well as the legal and ethical considerations surrounding its use. They discuss the challenges of demonstrating AI reasoning and the balance between AI advancements and regulations.
Diving in on “Crafting your Future-Ready Enterprise AI Strategy”
The Ecosystems podcast starts 2024 with a big announcement and a discussion of Andrew’s new Cloud Lighthouse + Microsoft white paper, Crafting your Future-Ready Enterprise AI Strategy. The paper addresses the need for organizations to take a strategic approach to implementing artificial intelligence (AI) and highlights the importance of data readiness. We emphasize the distinction between incremental AI, which brings efficiency to existing tasks, and differential AI, which represents groundbreaking advancements. The white paper also discusses the role of low code in scaling AI and the need for consistency in data access and rights, so we dig into that a bit in this episode. The conversation concludes with appreciation for the valuable insights provided in the white paper.
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.
Whitepaper: “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.
Christmas gifts from Microsoft (and one from a listener!)
Complete with twinkling lights, baubles, questionable sweaters, gnomes, and patriotic Christmas stockings: Mark, Andrew, Ana, and Chris celebrate the holidays and the year’s last episode of the Ecosystems podcast. Together we explore AI's practical business applications, our takes on the recent chaos at Open AI, and our thoughts on a great listener question. Microsoft Ignite is just weeks behind us, so we close out the year sharing our favorite Christmas gifts from Microsoft, new or now generally available tech that has us really excited.
UN Low-Code + API Summit 🇪🇸
Wave periods between major innovation in the cloud are growing shorter. We no longer have the luxury of waiting it out, of adopting later. Cloud ecosystems built on strategic foundations create the conditions to absorb successive waves of change. This session explored the history of these wave periods and implications that quickening technological innovation hold for global organizations, overviewed the “strategic pyramid” model for activity and system-based value in the cloud, introduced a reference ecosystem map built with ecosystem-oriented architecture, applied this architecture to a UN organizational context, and linked the architecture to a combination of Azure data, Azure AI, and Power Platform technologies.
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.
Data is central to ecosystems!
Chris, Ana, Mark, Andrew, and Will take on the crucial role of data in technology and business strategies, and in building cloud ecosystems that reflect those strategies. We’re seeing a bit transition in technology where data ought to be prioritized over individual applications within an ecosystem... but many organizations, their technology partners, and individual technology practitioners have been slow to adjust their thinking to this paradigm. They critique common practices in organizations where data is often neglected, leading to inefficiencies and missed opportunities. The episode highlights the need for a data-centric approach in technology development and organizational planning, underscoring the value of data quality, integration, and management.
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 Innovation Day 🇳🇱
It is as yet unknown if artificial intelligence is more akin to a “great invention” 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. 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. In this session we will explore AI strategies that are flexible, able to absorb tomorrow what we don’t fully grasp today, strategies that offer value to the organization beyond specific AI-driven workloads because the nature and value of these workloads will remain unclear for some time. We will understand core concepts behind AI acting on enterprise data, present a framework of five pillars upon which future-ready AI strategies are built, and discuss concepts and approaches to mitigate risk in your organization’s AI strategy.
MS Stage 🇺🇦
It is as yet unknown if artificial intelligence is more akin to a “great invention” 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. 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. In this session we will explore AI strategies that are flexible, able to absorb tomorrow what we don’t fully grasp today, strategies that offer value to the organization beyond specific AI-driven workloads because the nature and value of these workloads will remain unclear for some time. We will understand core concepts behind AI acting on enterprise data, present a framework of five pillars upon which future-ready AI strategies are built, and discuss concepts and approaches to mitigate risk in your organization’s AI strategy.
Architect strategic foundations
Establishing strategic foundations for the business of modern technology is essential for long-term success. Neglecting ongoing planning can lead to problems. Adapting to changing technology landscapes and evolving technology leadership roles are crucial, and aspiring cloud ecosystem architects need a strong foundation, adaptability, and specialized roles in organizations managing technology ecosystems. Join us as we banter on.