8 posts by Brad Micklea
AWS bet early on cloud computing and maintained focus, leading to their success. Today, enterprises face similar challenges with AI, divided between cautious approaches and building in-house AI teams. This article outlines a path to tool selection, project prioritization, and organizational structure that will lead to an enduring competitive edge.
In AI projects the biggest (and most solvable) source of friction are the handoffs between data scientists, application developers, testers, and infrastructure engineers as the project moves from development to production. This friction exists at every company size, in every industry, and every vertical. Gartner’s research shows that AI/ML projects are rarely deployed in under 9 months despite the use of ready-to-go large language models (LLMs) like Llama, Mistral, and Falcon.
