The model can be available and the deployment can still fail.
That is the part many AI roadmaps underweight.
I have seen platform decisions look complete on a slide and incomplete in the actual workflow. The API worked. The demo worked. The team still could not ship the system.
AWS putting $1B behind forward-deployed AI engineers — people who embed inside customer teams to push agents into production — is a strong signal about where the real work is. It says the hard part is not the model. Customers need help wiring AI into real tools, permissions, data, processes, metrics, and review gates. That work is unglamorous, but it is where pilots become production. It is also where many AI strategies quietly die: not because the capability was missing, but because nobody engineered the path from capability to working system.
Buying capability is easier than absorbing it into the organization. The model is a purchase; the working system is a build.
The last mile of AI is not a handoff to the model. It is an engineering and operating-model problem.