Third, optimize for cost-efficient inference, which is both a matter of choosing the right infrastructure and the right model size for the job. (Don’t use a 175-billion-parameter behemoth if a 3-billion-parameter model fine-tuned on your data performs almost as well.) The four big cloud providers are investing heavily to make this a reality.
Fourth, as exciting as it may be to really get humming with AI, don’t forget governance and guardrails. If anything, inference makes these concerns more urgent because AI is now touching live data and customer-facing processes. Put in place the “boring” stuff: data access controls (Which parts of your database can the model see?), prompt filtering and output monitoring (to catch mistakes or inappropriate responses), and policies on human oversight.
A healthy dose of AI pragmatism
The signals are clear: When budget plans, cloud road maps, and C-suite conversations all point toward inference, it’s time to align your business strategy. In practice, that means treating AI not as magic pixie dust or a moonshot R&D experiment, but as a powerful tool in the enterprise toolbox, one that needs to be deployed, optimized, governed, and scaled like any other mission-critical capability.
