Artificial intelligence adoption goes beyond trying a model or buying access to a tool. It requires a useful workflow, appropriate data, trustworthy behavior, integration with existing systems, training, governance, support, and evidence that the system produces better outcomes than the current alternative.
Adoption can move more slowly than technical capability because habits, switching costs, procurement, regulation, public trust, and product friction change at different speeds. A model may be capable enough before the surrounding product and organization are ready to use it reliably.
