Byline: Keith Belanger Byline: Keith Belanger Byline: Keith Belanger AI’s first disruption to data engineering was unmistakable. Expectations for data volume, speed, quality, and governance skyrocketed almost overnight, putting enormous pressure on data workflows designed for the analytical data era. The second disruption is happening more quietly. As teams work to deliver AI-ready data at enterprise scale, AI is increasingly playing a role in automating enforcement of DataOps standards and controls. deliver AI-ready data deliver AI-ready data When Scale Outpaces Human Attention Data reliability has traditionally depended on someone noticing that something feels off: an alert fires, a dashboard looks wrong, or a downstream team flags an issue. Then, an engineer starts digging. I’ve seen this approach work when systems are small and change is slow, but it’s very brittle in the face of growth. As organizations ask data teams to oversee systems that change constantly, react instantly, and behave consistently, human vigilance stops being a solution and starts becoming a liability. When you reach enterprise scale, pipelines don’t fail one at a time anymore. Small changes ripple, and dependencies compound. By the time someone notices a problem, the impact has often already spread. This is exactly what DataOps was designed for: scalable reliability that comes from systems and processes, not individual heroism. Moving AI Upstream Since the first AI models emerged, data teams have seen AI as a data consumer at the very end of the pipeline. But we’re well into the AI era now. The same technology that’s increasing pressure for high-volume, high-speed, highly governed data can now help build and operate the systems that deliver that data. AI can support data operations in a few distinct ways. It can help keep documentation in sync as pipelines evolve. It can propose tests based on how systems have failed or drifted in the past. It can surface anomalies humans would miss or notice too late. And it can evaluate readiness signals across quality, lineage, and governance continuously, not just during reviews. This Isn’t About Replacing Engineers Every wave of automation makes people afraid that their jobs will be optimized away. In data engineering, this framing misses what actually breaks in data systems. It assumes people can manually enforce standards, validate every change, and remember every dependency. But in reality, they can’t—not at the scale of enterprise AI. data engineering data engineering AI is well-suited to take on rote analytical tasks people struggle with, including scanning for patterns, checking consistency, and applying rules continuously. When organizations automate tasks that shouldn’t have been human work in the first place, data experts gain the freedom to do what they’re uniquely good at: designing data products, weighing tradeoffs, and improving systems over time. Where AI Fits Into Governance AI governance conversations tend to focus on what happens after models get deployed. But failures typically originate upstream, in data systems that end up feeding the AI bad data. AI-assisted DataOps can detect problems earlier and stop bad data from reaching production in the first place. Certain questions should always serve as gates for data delivery: Should this change deploy? Has a data product regressed compared to its past behavior? Does this pipeline still meet policy and quality expectations? Should this change deploy? Has a data product regressed compared to its past behavior? Does this pipeline still meet policy and quality expectations? Humans can’t answer these questions continuously at AI-scale, but AI can. With AI, teams can stop relying on periodic reviews or post-hoc audits and start systematizing governance checks. governance checks governance checks An AI-Augmented DataOps Model in Practice An AI-augmented DataOps model doesn’t look like a fully autonomous system. It looks like layered support built into the operating model. layered support layered support Humans define intent, standards, and acceptable risk. Automation enforces consistency and repeatability. AI adds analysis, recommendations, and early warning, helping teams see issues sooner and reason about them more clearly. AI improves trust by reducing blind spots while keeping accountability where it belongs. When AI participates in running data operations, a few shifts happen: Issues are caught earlier, when they’re cheaper to fix. Reviews focus more on intent and impact, not mechanical checks. Documentation stays closer to reality. Teams spend less time reacting and more time improving. Issues are caught earlier, when they’re cheaper to fix. Reviews focus more on intent and impact, not mechanical checks. Documentation stays closer to reality. Teams spend less time reacting and more time improving. None of this is flashy, but that’s the point. The goal is data systems that predictably deliver AI-ready data at speed, even as change and demand accelerate. Rethink the Role of AI in Data Engineering The future of data engineering will be defined by how reliably its data operations deliver AI-ready data products. That means treating AI as a participant in enforcing discipline, consistency, and trust across the data lifecycle. data products data products This moment calls for recognizing AI as more than a downstream AI consumer. It should be brought into the fold as a partner in the operational work that makes data AI-ready in the first place. It’s time to bring AI into data operations as a partner that can enforce discipline, surface risk earlier, and keep systems reliable at scale.
