AI won’t fix your data problems. Data engineering will
Artificial Intelligence (AI) may feel like a model problem on the surface, but as this article in CIO.com argues, most enterprise failures actually come down to something more fundamental: data engineering. The author, Carter Page, EVP of research and development at Astronomer, explains that organizations are investing heavily in models, compute, and tooling — assuming better intelligence will automatically lead to better outcomes. But the real issue is that AI systems often lack the business context needed to operate reliably inside an enterprise. The problem starts with fragmentation. Customer, billing, product, and usage data are typically spread across multiple systems, each with different definitions and timing. Humans can navigate these inconsistencies through experience and judgment. AI agents, however, act on whatever data they receive — which means incomplete or inconsistent context leads to quietly incorrect decisions at scale. Page argues that this shifts data engineering from a supporting role to a core operational one. It’s no longer just about building pipelines for analytics dashboards, but about creating trusted, real-time context that AI systems can safely act on. That includes entity resolution, data freshness controls, and strong lineage tracking so organizations can understand where data comes from and how reliable it is. It also highlights a second challenge: orchestration. As companies deploy more autonomous agents, they need infrastructure to manage scheduling, permissions, cost controls, human approvals, and auditability. In other words, AI agents require the same operational discipline as any critical enterprise system. Moreover, AI doesn’t fail because models aren’t smart enough — it fails when the underlying data and operational systems aren’t designed for decision-making. Strong data engineering and orchestration are what turn AI from a promising tool into a reliable business system.
