The Data Engineering Work Nobody Wants to Fund Before an AI Project
· 7 min read
It's common for an AI initiative to be scoped around the model, with data engineering treated as a rounding error. In practice, pipeline and data-quality work is frequently the majority of the effort on a first AI deployment.
The fix isn't more process — it's sequencing. Auditing data quality, lineage, and access controls before committing to a model architecture avoids the expensive scenario where a promising model can't be trusted because nobody can explain where its training data came from.
Treating data infrastructure as a product with its own roadmap, rather than a one-time setup task, is what lets an organization ship a second and third AI use case faster than the first.