In traditional software, errors are usually obvious. A service throws an exception.A request fails.A user sees a broken screen. In AI systems, the most dangerous errors don’t crash anything. They look like success. That’s why error handling matters more in AI than it ever did in traditional software—and why teams that reuse old assumptions quietly…

If AI were actually failing at the rate people claim, production systems across finance, healthcare, logistics, and government would already be collapsing. They aren’t. What is failing—quietly, repeatedly, and expensively—is engineering discipline applied to AI systems. This distinction matters, because blaming “AI” is comfortable.Blaming engineering discipline is uncomfortable.And uncomfortable truths are exactly what production systems…

Modernizing your business systems using AI core applications allows you to inject intelligence directly into your existing .NET software. You do not need a complete rewrite or a team of Python experts. You can transform legacy data into predictive insights using the C# skills your team already has by leveraging tools like ML.NET and Azure…

When AI systems fail, the first question is always the same: What happened? Without logging, that question has no answer. AI systems operating without proper logging aren’t just harder to debug — they are business liabilities. They expose organizations to legal risk, operational blind spots, runaway costs, and irrecoverable trust loss. This isn’t an engineering…

And How Engineering Prevents It AI prototypes almost always work. That’s the problem. Demos succeed in controlled environments, with curated data, friendly prompts, and no real operational pressure. Production systems, on the other hand, are messy, adversarial, cost-constrained, audited, and unforgiving. When AI prototypes collapse in production, it’s rarely because the model “wasn’t smart enough.”It’s…

What the 2025 McKinsey AI Report Confirms — and What Enterprises Are Still Missing Disclaimer: This article is an 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. The Hard Truth McKinsey Confirmed According…

AI Isn’t Special. It Isn’t a Strategy. And It Isn’t a Shortcut. Disclaimer: This article is an independent analysis and commentary on enterprise AI adoption trends and publicly available research, including the 2025 McKinsey AI Report. It is not affiliated with or endorsed by McKinsey & Company. All opinions are my own. AI Is Not…

Most organizations struggle with AI not because the technology is weak, but because expectations are wrong. A common assumption is that AI should do the work—design the system, automate the process, optimize operations, and deliver results. When that doesn’t happen, leaders conclude that AI is unreliable or immature. It’s a lot of work to use…