Mapping your Cloud Ecosystem: The Ecosystem Map
"Ecosystem Map" is both one of the 25 dimensions of the AI Strategy Framework and a foundational concept in ecosystem-oriented architecture (EOA), which makes this article doubly important reading for strategic thinkers on both fronts. The “map” metaphor is instructive here. It is used to distinguish an ecosystem map from the various forms of architectural diagrams, nearly all of which tend to include more technical minutiae than a typical ecosystem map. Whereas an architectural diagram provides specific parameters for specific technical solutions, an ecosystem map presents a higher-level, more visionary view of an organization’s cloud ecosystem. This analogy is fundamental to understanding and practicing EOA.
Reflecting on 2024: AI and the ‘Shiny New Object’ Effect
Reflecting on 2024, it’s clear that AI has landed and officially taken on the ‘shiny object effect’. With organizations rushing to utilize the technology, there’s been a tendency to layer AI atop an ecosystem without establishing a strategy for the fundamentals, such as data platform, data consolidation and modernization. A hope for 2025 is the normalization of AI to avoid this, and the progression of autonomous agents that learn and mimic human communication preferences, advancing beyond basic chatbots to more immersive and context-aware AI agents.
Additionally, this past year has made it apparent that not all organization’s are creating conditions for hiring across boarders, and allowing top talent to work on key projects regardless of their geographic location. A shift in this would provide multiple benefits: simplifying projects whilst widening the talent pool, supporting the freedom of dedicated workers, and releasing brain power and capital in areas that will benefit from economic growth.
Data Integration: The Key to Operational Success
In this episode, Ana, Andrew, and Chris discuss various themes including the technical difficulties faced during remote recordings, the influence of technology on elections, the challenges of cybersecurity, and the complexities of data integration and security. They also explore how algorithms shape political messaging and the implications of these technologies on society. In this conversation, the speakers discuss various themes including the impact of social media on public perception, insights gained from major conferences like Ignite, the evolution of podcasting in the tech space, and the importance of ecosystem-oriented architecture in organizations. They emphasize the need for a clear vision and cultural readiness for successful transitions in technology and business strategies.
Principles of Ecosystem-Oriented Architecture
We were working with eight to ten years between major disruptions from the dawn of the consumer internet. But these “wave periods”, that is, the time between the crest of two waves, have shortened to three to five years since the rise of the public cloud. It makes sense: As the evolution of computing technology and capacity picks up steam, it similarly accelerates. Innovation begets innovation. Generative AI was only made possible by the incredible computing power and connectivity available in the cloud. Now, AI is further accelerating this pace of change, shortening the time we have available before new waves crash upon the shore. 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 is much narrower.
Announcements from Microsoft Ignite 2024
Microsoft Ignite 2024 featured over 200 AI sessions. Key announcementsincluded Azure SQL transactional databases for app development, Co-pilot Studio in Power Apps for mini agents, deeper RAG capabilities with Azure AI Foundry, new managed security and operations in Power Platform, and updated governance and licensing for Co-pilot Studio.This podcast episode also tackles the rise of shadow AI, emphasizing the need for governance as 78% of workers use unsanctioned tools. Beyond the tech, it reflects on the joy of hands-on collaboration, like data modelling and brainstorming on whiteboards, proving that going back to basics can still drive innovation. What’s your top Ignite takeaway?
Trust in AI: Reducing Fears with a Strategic Vision
It is hugely important that there is trust in AI org-wide, for the successful adoption of AI. This needs human involvement and intentionality as part of a long-term AI strategy and clearly defined enablement program. Podcast listener, Rachel Roberts, comments that the need for a clear vision and planning a path towards the benefits of AI, instead of letting organic doubt take over, will build trust and reduce fears of uncertainty. She believes that being intentional can help mitigate doubts and foster trust in AI and each other. Having a strategic vision is critical and must be collectively agreed upon and championed. Ensuring that change management and training are fundamental to the effort is also essential due to the complexities of technology implementation and adoption. By combining these approaches, users can adapt, and organizations can maximize the impact of AI within their business.
Ethical Agents and the Path to Digital Equity
European regulations are becoming more stringent, aiming to ensure AI safety, ethics, and transparency. It's therefore crucial to ground AI solutions in good data and adhere to responsible AI (RAI) standards to avoid significant fines for non-compliance. The impact of robotics on the labor market are bringing opportunities, creating task automation through rules-based agents, and evolving fully autonomous agents ready for a shift towards human oversight of agent activities. Embracing an abundance mindset can unlock these new opportunities in the evolving landscape, though there’s a necessity for digital equity, ethics and preparing data systems for future AI integration.
Executive Vision and Actionable Roadmap for your AI Strategy
There’s an incredibly important transition in the broad information technology space that is often lost in the furor and excitement over generative AI. Simply “wanting AI” doesn’t cut it. So, the AI Strategy Framework begins with the Strategy and Vision pillar that sets forth five dimensions beginning with vision, extending to creating the actionable roadmap and architecture necessary to actualize that vision, and finally establishing the programmatic elements necessary to drive that vision to fruition. These dimensions help organizations formulate and take action on their big ideas.
"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.
Whitepaper: “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.
Whitepaper: “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.
Whitepaper: “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.
Whitepaper: “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.