The AI Demo Problem
An impressive demonstration is only the beginning. Operational AI needs the architecture, integrations and workflows around it.
AI has created a new kind of technology trap: the spectacular demo.
Someone uploads a document. AI analyzes it perfectly. Everyone around the table is impressed.
Then someone asks: “How do we use this across 40,000 documents inside our actual workflow?”
That is where the conversation changes.
Where do the documents come from? Who has permission to access them? What happens when the AI is uncertain? Where does the result go? Does another system need to be updated? Who approves exceptions? What gets logged? What happens if the format changes? How does the customer get notified? What happens when the model produces the wrong answer?
The AI itself may be only one part of the solution.
Production systems require architecture around the intelligence: integrations, workflow, permissions, monitoring, data, exception handling and user experience.
That’s the gap between an impressive AI demonstration and an actually operational AI. And it’s where a lot of the hard work actually begins.
When Custom Software Makes Sense
When off-the-shelf software leaves critical work to spreadsheets, email and manual workarounds, the build-versus-buy discussion changes.
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