A new study uncovers the ways AI is reshaping the roles of data professionals in unexpected ways–through partnerships not replacements.
The 2025 State of Analytics Engineering Report reveals that AI is rapidly reshaping the role of data professionals, but rather than replacing jobs, AI is augmenting their capabilities. With AI playing an increasingly vital role, data teams are growing in size, driving greater investment and transforming workflows to improve decision-making. While AI is leading the charge, data quality and trust remain critical for success, as organizations face challenges in ensuring the reliability of data that powers these new technologies.
How AI is Transforming Data Teams and Their Workflows
AI is becoming a fundamental part of analytics engineering, with 70% of data professionals already using AI for tasks like code development and documentation. This has led to a significant shift in job functions, where AI tools assist rather than replace human expertise. Investment in AI tooling is surging, and data team salaries are rising, particularly in North America, with over 80% of individual contributors now earning over $100K. This growth in investment and salaries underscores the expanding role of data teams in organizations, which are increasingly relying on AI to enhance performance and streamline processes.
See also: How Mature Data Organizations Tackle the Data Quality Problem Differently
Data Quality Remains a Top Priority as AI Adoption Surges
Despite AI’s rapid adoption, data quality remains a major focus for data teams. Organizations are making significant investments in data quality and observability to address ongoing challenges related to inaccurate, inconsistent, or incomplete data. The report highlights that while AI has the potential to revolutionize data workflows, it hasn’t yet fully delivered on its promise to facilitate natural language queries of data. However, AI’s role in analytics development is expanding, and its impact is expected to grow as specialized tools are developed to address the specific needs of data teams. Moving forward, integrating AI with human expertise could be key to overcoming the barriers to accurate and trustworthy data.
