"A growing body of research, including a widely cited MIT study, shows that around 95% of enterprise generative AI initiatives fail to deliver measurable business impact, despite widespread adoption. This is not because the models don't work: it's because they were inserted into organizations as tools, not as systems."
"Large language models are, by design, stateless: each interaction starts from scratch unless we artificially reconstruct context. Companies are the opposite. They are stateful systems: they accumulate decisions, track relationships, evolve over time, and depend on continuity."
"Enterprise AI cannot be session-based. It has to remember. We optimized AI to answer questions. But companies need systems that change outcomes."
Generative AI initiatives in enterprises often fail to deliver measurable business impact because they are treated as tools instead of integrated systems. Large language models are stateless, while companies are stateful, leading to a mismatch. This structural issue prevents effective integration into ongoing processes. AI needs to evolve from merely answering questions to changing outcomes, requiring systems that can track results, adapt, and improve over time.
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