dbt Labs has released its fourth annual State of Analytics Engineering Report, revealing a growing gap between the speed at which AI is transforming data work and the systems designed to ensure its reliability.
As AI becomes embedded in analytics workflows, organisations are producing data faster than ever, but governance, validation, and trust mechanisms are not keeping pace. As a result, trust in data has emerged as the most widely prioritised organisational objective, rising to 83% year over year. In this environment, organisations that invest in governance, validation, and data quality as strategic priorities are best positioned to scale AI-driven outcomes reliably and turn acceleration into sustainable impact.
AI moves from experimental to embedded
According to the survey, AI is scaling across two key areas of analytics engineering: AI-assisted coding that increases productivity and AI-generated, stakeholder-facing insights. The majority (72%) of respondents now prioritise AI-assisted coding in their development workflows, and 77% of leaders report pushing teams to improve productivity with AI.
“Two years ago, most analytics practitioners and leaders didn’t expect to be generating the majority of their analytics code with AI. But today, that’s where we are,” said Jason Ganz, dbt Labs Director, Community, Developer Experience and AI. “This signals a fundamental shift in the role of data practitioners, away from manually creating code and toward building the systems that enable agentic data workflows at scale, while providing the trusted infrastructure those agents need to operate reliably. Organisations that treat governance as infrastructure, not an afterthought, are the ones that will make the most of what AI can do.
Trust and governance as key enablers of AI at scale
Even though technical integration challenges have declined (from 35% to 27% year-over-year), governance issues like ambiguous data ownership (41%) and poor data quality remain persistent obstacles. Nearly three-quarters (71%) of data professionals are concerned about incorrect data reaching stakeholders.
In parallel, trust and speed have emerged as the dominant priorities among respondents, clearly separating from cost reduction. The importance placed on increasing trust in data rose sharply from 66% in 2025 to 83% in 2026, while the priority of “shipping data products faster” climbed from 50% to 71%. An emphasis on cost reduction, however, increased by only 5% (from 48% to 53%).
“There’s a real tension between moving fast and building trust, and you can’t optimise for both without intention,” said Pooja Crahen, senior manager of analytics engineering at Okta. “That’s where discipline in modelling, validation, and ownership becomes a requirement, not a best practice.”
Methodology
dbt Labs collected survey responses in late 2025 and early 2026 from 363 data practitioners and leaders across industries and regions. Of the respondents, 73% identified as practitioners, and 27% as managers or executives overseeing data teams.
