"The real gap in enterprise AI isn't who has access to models. It's who has learned how to build retrieval, evaluation, memory, and governance into boring, repeatable systems."
"The real divide in enterprise AI isn't just between companies moving fast and companies moving slow. It's between teams treating AI as a prompt-driven demo and teams learning, often painfully, that production AI is mostly a data and software engineering problem."
"What do I mean by 'engineering capability'? I definitely don't mean model access. Most everyone has that—or soon will. No, I mean the practical disciplines that turn a model into a system: data modeling, retrieval, evaluation, permissions, observability, and memory."
Enterprise AI adoption varies significantly across companies and teams, revealing a divide in engineering capability. Successful implementation requires practical disciplines like data modeling, retrieval, evaluation, and memory management. Workshops focused on foundational elements rather than advanced applications, emphasizing the importance of these 'boring' components in ensuring the success of enterprise AI projects. The current phase of enterprise AI is characterized by a need for foundational skills rather than advanced agent-based systems.
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