Most enterprises are past the question of whether generative AI is useful and into the harder one: which processes justify the operating cost, and what has to be true before the output can be trusted without a human checking every line.
The organizations getting value are the ones treating AI as a systems problem. They ground models in their own data through retrieval, instrument accuracy with an evaluation harness rather than anecdote, and design the human review gate deliberately — tightening it where the cost of error is high and removing it where measurement shows it is not earning its delay.
The architectural decision that matters most is the one people skip: abstracting the model behind an interface. Model capability and pricing are moving faster than any procurement cycle. Teams that wired one vendor directly into their business logic are now paying for that shortcut twice.