Why AI Prototypes Stall Before Production

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Издатель Jul 10, 2026

The AI chaos pattern is predictable.

Leadership says the organization needs to do something with AI. Departments start generating ideas. Vendors show polished demos. A developer or innovation team builds a prototype. The prototype works well enough to create excitement.

Then reality shows up.

The data is messier than expected. The old .NET system does not expose clean APIs. SQL Server contains undocumented business logic. Security wants to know where the data is going. Legal asks whether the output affects regulated decisions. Infrastructure asks who will monitor it. The business owner wants changes. The project manager asks what the milestone actually means.

Now nobody wants to kill the prototype because people saw it working. But nobody wants to fully fund it because too many risks remain.

That is usually not a model failure. It is a process failure.

The production workflow behind this video was built using the same methodology I apply for enterprise clients — I identified a real production bottleneck, evaluated AI options, and built a .NET-integrated workflow using AI tools to deliver it faster, better, and at lower cost. The thinking that improved my own workflow is the same thinking I bring to yours.

Explore more practical, applied enterprise AI insights at AInDotNet.com.

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