"AI Strategy Framework" guides your organization's journey in the Age of AI
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.
Copilot positioned to become the new UI of AI
With organizations today using 200-300 applications, what will be the impact on users when a profusion of AI solutions are added to the pile? As vendor AI offerings continue to expand, imagine the confusion. Employees required to access multiple agents with multiple UIs, stored sporadically across their organization: an agent for HR relations, sales, service, and for vendor applications, the likes of Workday, SAP, Salesforce. A labyrinth of applications. Now imagine this simplified. Imagine all the isolated agents and their data integrated into a single UI, a single place of reference, giving clarity, accessibility and enabling high adoption. As announced by Satya Nadella, Copilot is positioning to become the new UI for AI.
Bot vs human: which will reign in consumer engagement?
In the dawn of ‘agentic’ AI, that is to say, autonomous bots capable of mimicking humans and independent decision-making, what will be the implications for our every lives? Perhaps an end to dreaded call centre dispute resolutions, instead replaced by bots tackling negotiations perfectly due to having instant access to undisputable contracts and policies, outmatching human agents. For e-commerce, AI assistants capable of re-ordering groceries online, exploiting the best discounts, fastest delivery, and lowest shipping costs, totally disrupting traditional e-commerce loyalty. Future AI has the potential to make daily life incredibly efficient and transform consumer engagement models in ways not yet fully realised.
The skeptical approach to security and AI
Staggeringly, if cybercrime were a country, it would have the 3rd largest GDP. With attacks happening every second, it’s never been more important to approach data security and AI with a zero-trust mindset: practicing insider risk-management, auto-classifying data with Purview, and “red teaming” AI outputs. This critical thinking should apply to future advancements also, as we predict a shift towards observability whereby AI handles tasks and humans merely monitor them. Plus, as AI begins to mimic personas and styles, the risk of deep fakes increases, unbeknownst to users unless questioned. Staggeringly, if cybercrime were a country, it would have the 3rd largest GDP. With attacks happening every second, it’s never been more important to approach data security and AI with a zero-trust mindset: practicing insider risk-management, auto-classifying data with Purview, and “red teaming” AI outputs. This critical thinking should apply to future advancements also, as we predict a shift towards observability whereby AI handles tasks and humans merely monitor them. Plus, as AI begins to mimic personas and styles, the risk of deep fakes increases, unbeknownst to users unless questioned.
Embracing responsible AI with chaos engineering and governance
As AI systems become more integrated into our daily lives, it’s never been more critical to ensure they operate ethically. There are significant risks if not governed properly through informed practices, making responsible app development not just a necessity, but a cornerstone for building trustworthy AI systems that adhere to ethical standards and regulatory requirements. When an organization upholds this commitment, it not only mitigates potential harms but also fosters trust among users and stakeholders, thereby establishing the foundations for long-term success.
White Paper: “Crafting your Future-Ready Enterprise AI Strategy, e2”
It has become obvious how difficult so many organizations are finding it to actually craft and execute their AI strategy, in part because of the (often) decades they’ve spent kicking their proverbial data can down the road, in part because it turns out that enterprise-grade AI really does require the adoption of ecosystem-oriented architecture to truly scale, but largely because many organizations have no idea where to start. Many lack the wherewithal to really assess where they stand on day one, and to identify areas where they must mature to get to day 100 (and beyond).
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 to broaden the thesis, so in this second edition we offer a model through which organizations may realistically assess their current maturity to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey.
White Paper: “Ecosystem-Oriented Architecture in the Public Sector”
This whitepaper offers CIOs, enterprise architects, and other public sector technologists a comprehensive introduction underscoring the need to adopt ecosystem-oriented architecture (EOA) to build scalable, resilient, and flexible cloud ecosystems that can absorb successive waves of technological change.
Five strategies to integrate Power Platform in your data platform architecture
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.
White Paper: “Scaling your Enterprise Cloud with Power Platform”
Power Platform has been woven into the fabric of enterprise IT for far longer than most people realize, and, as such, is often the backbone of mission-critical “Tier 1” workloads. This white paper is the technology leaders’ guide to strategic Power Platform in a modern cloud ecosystem, diving deep on some of the most important methods of scaling your entire enterprise cloud—data, AI, applications included—with Power Platform: Using Power Platform with your enterprise data, lowering your long-term costs, securing and governing your data to reduce risk, infusing AI into daily work, then scaling AI across your org, and scaling your cloud and other tech investments with specialized capabilities.
White Paper: “Power Platform in a Modern Data Platform Architecture”
For all the talk about Power Platform as a ‘’low-code’’ tool (and this is the last time I will use the word), for all the attention given to how supposedly easily it allows non-technical users to create simple apps, Power Platform’s greatest value lies not in the app, but in the data the app collects or serves back to its users. Power Platform isn’t an app phenomenon. It’s a data phenomenon. This white paper takes on the question of how Power Platform integrates with Azure data services including Microsoft Fabric, outlining five patterns that organizations ought to mix and match to extract Power Platform’s greatest value.
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.