Author: Keith Baldwin

Why AI Pilots Die (and How to Escape the Pilot Graveyard)

Disclaimer: This article provides independent analysis and commentary on the 2025 McKinsey AI Report. McKinsey & Company does not endorse, sponsor, or affiliate with AInDotNet. AI pilots are everywhere right now.Chatbots. Copilots. Agent prototypes. Workflow automations. Executives love them.Teams build them quickly.Vendors use them to promise transformation. And then… nothing happens. The pilot never reaches […]

AI Adoption Is High, But Scaling Is Failing: Why Most Companies Are Stuck — and How to Fix It

Disclaimer: This article is an independent analysis and commentary on the 2025 McKinsey AI Report. McKinsey & Company does not endorse, sponsor, or have any affiliation with AInDotNet or the viewpoints expressed here. AI Adoption Is High, But Scaling Is Failing Over the last two years, AI adoption has exploded. Depending on the survey, 80–90% […]

Why I Started AInDotNet — And How the McKinsey 2025 AI Report Highlights the Exact Problems I Set Out to Solve

Disclaimer: This article contains independent analysis and commentary on the publicly available 2025 McKinsey AI Report. McKinsey & Company does not endorse, sponsor, or have any affiliation with AInDotNet or the viewpoints expressed here. Introduction When McKinsey released its 2025 AI report, I read it with a mix of déjà vu and quiet confirmation. Not […]

The AI-Enabled .NET Enterprise Blueprint

A practical architecture for building intelligent, future-ready enterprise applications The rise of AI isn’t just changing how developers write code — it’s redefining the very structure of enterprise software. As code becomes faster to generate, frameworks become interchangeable, and AI reasoning becomes part of daily operations, enterprises need a modern blueprint that blends: This is […]

The Future of Enterprise Software: From Codebases to Knowledge Systems

Why AI is pushing enterprises beyond traditional applications and into intelligent, reasoning-driven systems For decades, enterprise applications have been nothing more than structured CRUD machines — systems that store, retrieve, display, and update data. Even the most “sophisticated” platforms have largely been elaborate interfaces around databases and business workflows. But that era is ending. AI […]

How Many Professionals Actually Know How to Use LLMs? A Data-Driven Look at AI Adoption on LinkedIn

Artificial intelligence tools like ChatGPT, Claude, and Microsoft Copilot are everywhere—but how many professionals actually know how to use Large Language Models (LLMs) effectively to get work done, reduce workload, and improve results? While some voices online claim “LLMs are flawed” or “LLMs are good for nothing,” usage patterns tell a very different story. This […]

Functionality First, Optimize Second: A Pragmatic AI-Era Strategy for Modern .NET Development

For decades, developers were trained to obsess over optimization — crafting micro-efficient loops, shaving milliseconds from SQL queries, and squeezing every ounce of performance out of infrastructure. But in the AI-accelerated era of software development, that mindset can quietly sabotage enterprise progress. Today, the teams who win aren’t the ones who write the fastest code […]

Human-in-the-Loop: Designing Enterprise AI Systems That Stay Accountable

AI is transforming how modern enterprises operate—but without human oversight, the results can become unpredictable, biased, or outright dangerous. As organizations embed AI deeper into workflows, the question is no longer “Can AI automate this?” but “How do we ensure the AI behaves responsibly?” That’s where Human-in-the-Loop (HITL) design becomes essential. In enterprise software—especially inside […]

Building Intelligent Business Services in .NET: Turning Your Applications Into Smart Decision-Makers

For decades, enterprise applications have moved data, displayed screens, and executed workflows — but they haven’t thought. That era is ending. With AI now baked into the .NET ecosystem, business services can evolve beyond procedural logic into intelligent, adaptive components that enhance decisions throughout the enterprise. This shift doesn’t start in your UI or database.It […]

AI in the Software Development Lifecycle: From Planning to Deployment, AI Accelerates Every Phase of Development

For decades, the software development lifecycle (SDLC) has been a slow, linear, and highly manual process. Requirements take weeks to document. Developers spend months writing boilerplate code. Testers chase bugs across environments. DevOps teams stitch together pipelines and deployment scripts. But the rise of AI — Copilot, ChatGPT, Azure AI, ML.NET, and automated DevOps systems […]

Designing AI-Ready Architectures in the .NET Ecosystem

Architect once — plug in AI anywhere. The Next Evolution of .NET Architecture Modern .NET development isn’t just about scalability, reliability, and clean layering anymore — it’s about preparing for intelligence.AI is no longer a separate system that you bolt on later. It’s becoming a native layer of capability that needs to live comfortably inside […]

From Business Rules to C#: Turning Policies into Logic

Every rule, constraint, and workflow can map cleanly to a .NET construct. Why This Matters When businesses talk about “digital transformation,” they often overlook a critical truth:transformation doesn’t happen in code—it happens in logic. Every policy, exception, and decision that defines how your organization operates can be modeled and executed directly in software. In traditional […]