Most enterprise AI investments today focus on models, compute and tooling. The assumption is that intelligence is the binding constraint and that a more capable model will produce better outcomes across every dimension that matters. This is a reasonable starting point, but it is also where most initiatives go wrong. The models organizations are deploying were trained on public data at scale. None of your internal systems, customer schema, pricing logic or support taxonomy appeared in that training. When a model encounters your internal data, it processes it as best it can, but without the grounding that comes from having been trained on it. Early AI initiatives are struggling not because the models are weak, but because the context they need to operate reliably inside your organization is something they have never seen before.

Full opinion : Your AI isn’t broken, your data context is; you need solid data engineering to bridge the gap between a smart model and a reliable, real-world business agent.