A demo proves what is possible. Demos are not business as usual, they are designed to persuade and convince. What organizations need is a potential new version of operational repeatability.
Just like with new business ideas, AI must survive contact with real operational work, which includes messy data, edge cases, existing systems, employee habits, customer expectations, security rules, budgets, compliance, policies and accountability. A model that performs impressively once is far from becoming a business capability.
The progression would be:
- Demo
- Validated use case
- Workflow integration
- Controls
- User adoption
- Measurement
- Iteration
- Sustained valuable business outcome
Prerequisites include:
- Tasks must be clearly defined and worth improving.
- Performance must meet realistic use cases, not just curated examples.
- The AI capability must fit into actual workflows with appropriate data, tools, and permissions.
- Clear roles for review, exception handling, and accountability.
- Employee trust must be addressed.
- Cost, speed, privacy, security, and reliability must be acceptable.
The organization must prioritize outcomes over usage.
The gap between demos and implementation is huge. They key is whether the organization is willing to redesign itself around the capabilities highlighted in the demo.
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