The Everyday AI Desk

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Greg Jefferson

Greg Jefferson

Documenting the reality of everyday AI — the failures, the friction, the patterns most miss. Product manager. The Everyday AI Desk.

Why Your Team Has 10 AI Tools and Zero Results: The Tool Sprawl Problem

Twenty‑eight percent of enterprises now use ten or more different AI applications. Seventy‑six percent have experienced negative outcomes from disconnected AI tools. This isn’t a technology adoption problem. It’s a governance and design failure disguised as progress. If you’re responsible for an AI budget, a

Designing Against Failure, Not for Ideal Conditions

Most systems are designed for a person who doesn't exist. Motivated. Focused. Working with clean data and uninterrupted time. The person in the demo. The person in the product walkthrough. The person who reads the documentation and follows every step. That person shows up maybe 20% of the

Survival vs Optimization: A Different Way to Evaluate AI Tools

You tested the tool. It worked. You rolled it into production. Then someone used it under deadline pressure with incomplete instructions and messy input data, and it produced confidently wrong output that made it into a client deliverable. The tool didn't warn you. It didn't fail

Why I'm Charging for This

Most newsletters flip to paid subscriptions when they hit scale. The logic is simple: build an audience, prove value, then monetize. It's a reasonable model. It just doesn't work for what I'm building here. I'm charging from the beginning because the economic

If You’re Accountable for Results (Not Just Ideas), AI Changes

There's a moment most people hit after the novelty wears off. The tools still work. The outputs still look impressive. But something feels off. The system produces more material than it resolves. Decisions take longer, not less time. You're surrounded by suggestions, summaries, and options, yet

Why Your AI System Worked in the Demo But Broke Under Real Work

If you’ve implemented AI more than once, you’ve likely seen this pattern. The demo worked. The test case looked clean. The output was impressive. Then the system met real work. Usage dropped. Outputs became inconsistent. The workflow required more attention than it saved. Eventually, it stopped being part