How do we avoid ending up with a graveyard of AI proofs-of-concept that never scale?

The AI proof-of-concept graveyard is the most expensive real estate in enterprise technology. Millions of dollars in pilot projects that impressed the board, generated enthusiasm, and then quietly died when someone asked 'how do we put this in production?'

The pattern is always the same. The pilot runs on curated test data. It uses a simple integration. The output is reviewed by one person who knows exactly what to expect. It works beautifully. Then someone tries to connect it to real systems, with real data, at real volume. And it breaks. Not because the AI is wrong, but because nobody designed for the messy reality of production.

The fix is to design for production from day one. Use real data, not test data. Plan the integration architecture before you start building. Define success in production metrics (cost per transaction, error rate, throughput), not pilot metrics (it ran, it produced output). Build governance and exception handling into the first version, not as a retrofit.

SynthesisArc does not build proofs of concept. We build production deployments. The INSIGHTS assessment identifies the right workflow. PRISM deploys against real data with real integration. The 90-day guarantee means if it does not deliver measurable results in production, you do not pay. That is how you stay out of the graveyard.