The fact that artificial intelligence (AI) has affected data management positions is among the least shocking things someone might state in 2025.

Less is known about the precise effects AI has had on those positions and if the emergence of automation, generative AI, and LLMs should make data management professionals feel empowered or frightened. The goal and structure of data management positions are changing quickly as artificial intelligence becomes more and more integrated into every facet of how businesses gather, manage, safeguard, and handle data.

AI has had a profound and contradictory effect on data management. Consider the vast amounts of laborious manual labor that AI has replaced, such as metadata management, data categorization, tagging, lineage tracking, and policy enforcement. Professionals in data management are now free to concentrate on strategy and governance instead of tedious tasks. However, it has also reduced the value, if not eliminated, of several fundamental jobs and abilities, such as rule-based stewardship, legacy master data management, and manual data quality checks.

To promote discoverability, compliance, and trust, data management teams laboriously categorized data assets, tagged metadata, and traced data history for years. These days, AI-powered data catalogs, such as Informatica CLAIRE, Collibra, and Alation, can automatically crawl, categorize, and recommend business definitions while keeping up with data at cloud scale. These platforms suggest privacy tags and policy assignments in addition to revealing hidden relationships. The benefit is that data is easier to find and manage at scale. The drawback is that the conventional IT position of metadata librarian has lost much of its significance.