Let’s build the future, together.
This blog provides technical guidance on building and optimizing Microsoft technologies. Whether you're an IT professional, a tech enthusiast, or a business leader, the practical steps will equip you with the knowledge to leverage the full potential of Microsoft solutions within a broad technology ecosystem, and navigate the transformative tech landscape.
Strategic AI Workload Prioritization
Our goal in Workload Prioritization (also known as “workload road mapping” or “app rationalization”, depending on which circle you’re running in) is to create a prioritized roadmap of specific workloads to be modernized with an infusion of AI or built anew to solve an emerging problem or a challenge whose solution may have been out of reach without AI. This prioritization is an indispensable part of an organization's ongoing AI journey. Prioritization results in a workload roadmap, a backlog of workloads that are candidates for development with AI capabilities. It allows the organization to project AI’s business value over time, and is a core driver of return on investment (ROI).
AI Won’t Take Your Job, but It May Drastically Change It
There are many speculations that AI will soon take our jobs. It makes sense that there would be; my feed is filled with news about technological advancements and we see it everywhere in our daily lives. I now find it completely normal for Amazon to summarize product reviews based on my preferences, or to see an AI-automated search engine surfacing key points when researching online. AI lives alongside us, and it will become more and more seamlessly integrated into everything we do. One thing is clear though: AI gives us helpful information, but it does not complete the end goal that we're intending to achieve.
Kickstarting AI: The Essential Blend of Pro-Code and Low-Code
You may think my title is incorrect, that no one should WANT to use AI, but rather people should seek AI to solve real-world problems. I agree. But when do you get to know the tech and the problems it may solve? Where do you start if you do not know anything about low-code solutions? It's not just GitHub Copilot - AI is much more democratized than that, through other Microsoft Copilots. So, here's where we begin: Start engaging with your colleagues, you may learn something from them.
Future-Ready AI Ecosystem Architecture
Ecosystem architects seek first to build a cloud ecosystem, that is, a collection of interconnected technical services that are flexible or “composable,” re-usable, and highly scalable. The ecosystem then expands, contracts, and is adapted over time to accommodate the workloads deployed within it.
AI Strategy and Vision
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, our Strategy and Vision pillar sets forth five dimensions which begins with vision, extends to creating the actionable roadmap and architecture necessary to actualize that vision, and finally establishes the programmatic elements necessary to drive that vision to fruition. These dimensions help organizations formulate and take action on their big ideas.
Center for Enablement: Ecosystem Design Authority
The final dimension in our Strategy and Vision pillar is organizational, putting in place the team or organizational unit required to drive our AI strategy forward. The Center for Enablement (CFE or C4E) concept is rather a departure from IT organizational concepts of old, though, representing a shift from controlling processes to enabling people. The Ecosystem Design Authority (EDA) offers a sound model through which the C4E can facilitate the success of the organization’s AI strategy across technical domains that it may not directly control.