Author: Keith Baldwin

A low-cost, low-risk approach for AI experimentation using Microsoft-native tools Prototyping is where most AI projects live or die.The wrong tools, the wrong scope, or the wrong mindset can turn promising ideas into budget black holes. Fortunately, if your organization already uses Microsoft tools, there’s a clear, low-risk path forward. In this guide, we’ll show […]

If you’re building AI systems in the Microsoft ecosystem, should you use ML.NET or Semantic Kernel?These two tools support radically different use cases—and knowing when to use each can save you time, reduce complexity, and lead to better business outcomes. This guide breaks down the key differences between ML.NET and Semantic Kernel, explains when to […]

In most Microsoft-based environments, software development has followed a well-defined formula for decades: gather requirements, write code, run QA, deploy. And it’s worked. Teams are established. Roles are clear. Quality Assurance (QA) ensures the code meets the requirements. The legal department steps in when there are contracts or compliance checkboxes. And if the app crashes, […]

In traditional software systems, logging and exception handling are often considered back-end hygiene—a developer’s concern. But in AI systems, especially those deployed across enterprise and government environments using Microsoft technologies, logging and exception handling aren’t just technical details. They’re essential pillars of observability, traceability, and accountability. This article explains why robust logging and exception handling […]

How to Choose the Right Tools, Algorithms, and Architecture for Real-World AI Forecasting in Microsoft Environments 🔍 Why Forecasting Matters More Now Forecasting has always been part science, part art—and often ignored. As someone who studied operations research and statistics decades ago, I expected to use those techniques everywhere. But for 40 years, most customers […]

AI implementation isn’t plug-and-play—it’s more like remodeling your house while you’re still living in it. 🔍 Why This Matters As more organizations adopt artificial intelligence (AI) to streamline operations, many overlook a hard truth: the success of an AI project hinges not just on the technology, but on the readiness of key roles—especially project managers […]

Introduction: Has the U.S. Fallen Behind in Innovation? The global innovation race is intensifying—and many believe the United States is no longer in the lead. A recent article titled “Why China Has Already Won” highlights a growing concern: that China’s long-term industrial strategy, infrastructure investment, and focus on self-reliance have placed it ahead of the […]

Discover what executives need to know about AI assistants in 2025—how to align them with business goals, reduce risk, and deploy cost-effectively using Microsoft tools like Copilot, Azure AI, and .NET. 🧠 Why AI Assistants Are an Executive-Level Priority AI assistants are no longer just experimental tech—they’re rapidly becoming core infrastructure for communication, operations, and […]

In today’s AI-saturated tech space, the terms chatbot and AI assistant get thrown around like they mean the same thing. They don’t. Call a chatbot an assistant and you risk overselling its intelligence. Call an AI assistant a chatbot and you downplay its value. This isn’t semantics—this is about software architecture, development cost, system complexity, […]

🧠 Predicting the Future (No Crystal Ball Required) Sometimes, the ability to “predict the future” doesn’t come from data models or insider info—it comes from looking at the same information differently. I often follow investment and finance news—not because I’m day trading—but because the financial press often reveals what’s about to happen in real business […]