# Why AI projects fail after the demo

> AI projects rarely fail at the demo. They fail months later in production, for reasons that were visible on day one: no evaluations, no owner, and releases that can't be undone.

Source: https://simasrazinskas.com/blog/ai-project-failure-modes  
Published: 2026-06-30  
Updated: 2026-10-07  
Tags: Production, Evaluation, Deployment

The demo is rarely the risk.
AI projects fail later, in production, because evaluation, ownership and safe releases were left for later, and later arrived with users watching.

## The model call is treated as the system

The model is one box in the diagram.
What decides success surrounds it: where the data comes from, queues and retries, logs, review screens, cost limits and deployment.
A budget that covers only the box ships only the box.

## Evaluation starts after the arguments

Without evaluations, every prompt change is an opinion and every regression stays invisible until a user reports it.
An evaluation set is cheapest to write on the day the prototype first works, from real cases.

For the support agent I built, the evaluation set is a reviewed catalog of real tickets with their captured data.
An independent model judges drafts against expected outcomes the drafting model never sees.
Promoting a prompt change calls for two full passing runs on matching inputs; skipping that shows as a warning, never as a pass.
When operators edit a draft, the edit can become a new case, so the catalog grows from real work.

## Nobody owns the failure

A production AI workflow needs a named owner for wrong answers, provider outages, duplicate work and escalations.
If the answer to "who gets alerted?" is a shrug, it isn't ready.

## Releases can't be undone

A prompt, model or configuration change is a release like any other, and it can be wrong.
Teams that test model quality carefully still push those changes with no health check and no rollback path.

A health gate proves a new version healthy before it takes traffic, and rolls back on its own when it isn't.
Configuration changes behind an AI feature, such as a new prompt schema or a provider-routing update, are migrations and need the same discipline as a database schema change.
Prompts stored as immutable versions make rollback a pointer change.

## Fast delivery depends on all of this

Most of my code is written by AI coding agents.
That only works because tests, evaluations, review and a deploy that can roll back decide what ships.
With those in place, shipping several times a day is routine; without them, every release is a gamble however carefully it was read.

## Start with one process

A one-hour call costs €80. Afterwards you get a written plan, whether or not we work together.

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