What's become interesting to observe is how organizations progress once they start working with AI in a hands-on way. The moment they begin training their own agentic processes or decision systems, the learning curve transforms. We see teams start realizing where intelligence fits, where it doesn't, and what it actually takes to get reliable outcomes.
There's also a widening divergence emerging. Companies that engage with AI as something to be trained and integrated are accumulating a different kind of understanding. One group develops a feel for how these systems behave under actual conditions. The other stays in an evaluation loop, waiting for the technology to become "ready."
The distinction becomes clearer. Experimenting with AI means testing tools in isolation. Running intelligent systems means shaping capabilities that evolve alongside the business.
We're still early in this shift, but the pattern holds. Once teams begin working with AI as an operational capability, something that requires training, evaluation, and context, the entire conversation changes. That's where most of our learning has come from, and it continues to reshape how we think about what it means to build Intelligence inside an organization.
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