Beyond the Demo: Why AI POCs Stall Before Production
A working demo can create excitement, but it does not guarantee that AI is ready for production. In this practical presentation, Stuart Houston of CNOOC explores why AI proof of concepts often stall before becoming trusted business tools, and what organisations need to consider if they want AI to move beyond technical success.
The session begins with a clear reminder: start with the business problem, not the technology. Stuart frames the challenge around controlled documents, where responsibilities sprawl, manual reviews, governance visibility and inconsistent interpretation can make review processes slow, difficult to scale and hard to manage consistently.
The presentation shows how an AI proof of concept can solve the right first problem by supporting extraction, role matching, searchable review, comparison and SME validation. However, the key message is that a successful POC is not the finish line. Production use requires explainable outputs, defined ownership, controlled deployment, measurable business value and a sustainable operating model.
Responsible AI, in this context, is less about the model itself and more about the controls around it. Human review, traceability, validation, governance and measurable value are all positioned as essential foundations for building confidence and moving AI towards controlled use.
The closing lesson is simple but important: the challenge is not building AI that works, but building something the business is willing and able to rely on. View the full presentation for a focused look at how CNOOC approaches the journey from AI proof of concept to usable, governed and trusted production workflows.
