We hit a wall: corrupt sales data in a SOX-compliant pipeline. Friday night firefighting, late-night debugging, missing documentation, and urgent pressure. Could AI do this job?

A vendor released an API breaking change. The result? Wrong numbers in accounting reports.

It wasn't just bad data. It was bad mapping, vague urgency, and unclear expectations.

1. Trace every transformation and show intent
2. Understand domain-specific rules (like accounting and SOX compliance)
3. Predict side effects of schema changes
4. Know when to alert a human
5. Coordinate handoffs between people and systems

1. Declarative data flows with semantic metadata
2. Schema validation and contract testing for all integrations
3. Structured logs and code annotations for machine understanding
4. Culture of documentation and observability
5. Org-wide investment in AI-readiness

AI doesn't make these problems go away. It helps if you design systems with clarity, intent, and clean handoffs. Most systems aren't built that way. Yet.

Until then, humans are still the glue.

30 years in software. From aerospace to accounting. I help businesses scale and stay sane when integrations go sideways.

Let's chat → kaizen.io