Real uses, not pipe dreams.
Plain-language writing about AI that actually works — grounded in real projects, honest about limits, and free of hype. One concrete example and one clear takeaway per post.
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Turning a gigabyte of messy government data into something useful
NHTSA publishes over a gigabyte of raw vehicle-complaint text. No human can read it — but the patterns inside it are real. Here's how Python text extraction surfaced part-failure insights the dataset never states outright.
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No token tax: when a plain script beats calling AI every time
Calling an AI model costs a little money every single time — and gives you a slightly different answer every time. For truly repetitive work, a deterministic script is cheaper, faster, and more reliable. Here's how to tell which one you need.
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Excel first: the fastest way for a non-technical person to feel AI is real
Forget chatbots and demos. The moment AI becomes real for most people is the moment it fixes the spreadsheet they've been fighting for years. That's why every training I run starts in Excel.
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Asking precisely: the one skill that separates useful AI from disappointing AI
The difference between "AI is useless" and "AI just saved me four hours" is almost never the AI. It's the question. Precision in asking is a learnable skill — and it's the centerpiece of everything I teach.
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Shadow IT vs. sanctioned tools: build things without making IT your enemy
The worst way to bring AI into your workplace is behind IT's back. The best way turns them into your strongest ally. The difference is a conversation most people skip.
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What AI can't do (yet): an honest list
Anyone selling you AI should be able to tell you where it fails. Here's my working list — the real limits I run into building with these tools every day, and what they mean for your business.
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