
Why AI Pilots Stall at 80% and Never Ship
The demo worked. The pilot went well. Then it sat for eight months. The five reasons AI projects die between promising and production.
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Custom software, web applications and AI builds: scoping, buying, choosing vendors and maintaining what gets built once it is live.

The demo worked. The pilot went well. Then it sat for eight months. The five reasons AI projects die between promising and production.

APIs decide whether connecting two systems takes two days or two months. A non-technical explanation, and the questions to ask before you buy software.

Document extraction is the least glamorous AI use case and often the highest return. What works, what does not, and how to size the savings.

MVP has been stretched to mean anything. What it originally meant, why smaller is genuinely better, and how to decide what makes the first release.

Language models do not know when they are wrong. Why that is structural rather than a bug, and the engineering controls that actually reduce it.

Twelve questions that surface how a software firm actually works — on ownership, testing, handover, and what happens when things go wrong.

Five questions that separate a real AI engineering team from a thin wrapper with a markup — and the answers you should expect.

Realistic timelines by project size, what runs in parallel and what cannot, and the four things that add months without adding features.

A chatbot answers. An agent acts. The distinction decides how much you should trust it, what it can break, and what guardrails it needs.

Everyone budgets for customer-facing software. The tools your own staff use every day are usually cheaper to build and pay back faster.

Spreadsheets scale further than people expect, then fail suddenly. Nine signs you have crossed the line, and what to do about it.

If a custom build uses the same underlying model as the subscription you already pay for, what exactly are you buying? Six concrete differences.