By now, it should be clear that this money should and can be used in a better way to enable enterprisewide adoption using scalable development of generalized components, as opposed to tailored point solutions that are limited to a single-use case.

As models and ML code become more and more democratized, an unfortunate consequence of this is that machine learning is an infrastructure problem.

This calls for a paradigm shift: From a model-centric approach to a production-centric approach.

Instead of throwing money at the modeling problem, start investing in infrastructure and orchestration. This requires a shift in mindset more than technological prowess.

Enterprises should take a step back and see the big picture of the AI journey, and start thinking of a systematic way to utilize many AI models in a single, robust framework.