The documents your AI reads were never treated as data
The most valuable input to most AI features never passed through the data platform. Contracts, pricing sheets, and support tickets have no data owner and no rule for which version is current, a governance gap that sits next to the data platform rather than inside it.
The data contract your vendor never signed
Internal data contracts rely on shared governance and shared authority. When the data producer is outside your organisation, neither exists — external data quality is a negotiation problem, not a governance one.
Your data engineers are advancing on a ladder designed for someone else
Most data engineering career ladders are software engineering career ladders with the title changed. The divergence between the two disciplines — what distinguishes senior from staff, and staff from data architect — is the part most companies have never described.
Your data access model assumes a human is asking
AI agents enumerate available data before deciding what to query. Your data access model was designed for a data consumer who arrives with a formed question.
The business question data lineage was built to answer
The answer to "what will break if we migrate this data source?" is already computed in the data lineage graph. It is rarely the first place anyone looks.
Your data product has contributors but no owner
Most data platforms have engineers accountable for whether data arrives. Almost none have anyone accountable for whether it is correct, trusted, and fit for the decision it is supposed to support — and that missing accountability is the root cause most data quality investigations never reach.
Data observability without incident response is a log nobody reads
Most data teams have bought the monitoring. They have not built the process that determines what happens when the monitor fires — and without that process, data observability tooling is a detection mechanism with no one listening.
Data quality is not an engineering problem
Most data quality problems are not engineering failures. They are quality decisions nobody made — thresholds inherited by default, definitions that were never agreed, policies that exist only as institutional habit.
