New dbt Labs report finds AI-driven acceleration is outpacing trust and governance

The 2026 State of Analytics Engineering Report reveals data leaders’ concerns over data quality amid rising need for reliability

Key findings:

• 72% of respondents now prioritise AI-assisted coding in their development workflows, while only 24% prioritise AI-assisted pipeline management, including testing and observability; this highlights an imbalance between acceleration and quality
• Trust in data and data teams as an organisational priority surged from 66% to 83% year over year, the steepest single-year increase of any measured objective; speed followed, climbing from 50% to 71%
• 71% of data professionals cite incorrect or hallucinated outputs reaching stakeholders as a top concern, which carries greater consequence as autonomous agents operate on top of organisational data at scale
• Data infrastructure costs are outpacing budget growth, with 57% reporting increased warehouse and compute spend, compared to just 36% reporting increased team budgets

dbt Labs, a leader in standards for AI-ready structured data, today 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.”

On 29 April 2026, a panel of industry experts from Hex, Ramp and dbt Labs will host the 2026 State of Analytics Engineering Virtual Event. The conversation will focus on the report findings, what the year-over-year changes signal, and how trust isn’t a constraint on AI-driven impact but the determining factor in how far it can scale.

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.