THE STATE OF AI
Scaled production comes with variables that are easy to miss during experimentation.
Policy constraints may shape what can be generated. Model behaviours and capabilities evolve over time. Service throughput may vary under demand. None of these points are uniquely ‘good’ or ‘bad’. They are operational facts.
Treating AI as a production dependency helps. Teams can define standards, document preferred workflows, and establish fall back options for time sensitive delivery. Clear guard rails make adoption easier because they give teams confidence about what is acceptable and what requires extra review.
Given the pace of change, it is sensible to plan for evolution rather than permanence. The way of working now should be expected to change over the next 12 to 24 months, as capabilities mature and as teams build new strengths. Organisations that do best tend to build adaptable operating models, supported by ongoing testing, learning, and iteration.