Data Leaders Sound Alarm: AI's Speed Creates a Trust Deficit

A new industry survey from dbt Labs points to a widening rift in corporate data strategy. As artificial intelligence tools become deeply integrated, the push for rapid deployment is colliding with fundamental questions of reliability.

The 2026 State of Analytics Engineering Report, based on responses from 363 data professionals, finds that while 72% now prioritize AI-assisted coding for development, a mere 24% focus similar AI efforts on pipeline management, testing, and observability. This imbalance suggests teams are building faster without equivalent investment in the systems that verify what they build.

The consequence is palpable concern. Seventy-one percent of respondents identified incorrect or hallucinated data reaching decision-makers as a top worry. This risk is magnified as autonomous agents begin to act on organizational data. In response, establishing trust in data has surged to become the leading objective for 83% of leaders, marking the report's steepest annual increase.

"Two years ago, most of us didn't expect to generate the majority of our analytics code with AI. But today, that’s where we are," said Jason Ganz of dbt Labs. He notes the practitioner's role is shifting from writing code manually to constructing the trusted infrastructure that allows automated systems to operate reliably.

Financial pressure adds another layer. Fifty-seven percent reported rising data infrastructure costs, compared to only 36% who saw their team budgets grow. Despite this, the focus on cost reduction increased marginally, while priorities for speed and trust soared.

"There’s a real tension between moving fast and building trust, and you can’t optimize for both without intention," said Pooja Crahen, senior manager of analytics engineering at Okta. "Discipline in modeling, validation, and ownership becomes a requirement, not a best practice."

The report concludes that sustainable AI impact depends on treating governance as core infrastructure. Organizations that make this strategic shift, the data suggests, will be the ones to reliably convert acceleration into value.

Source: dbt Labs Blog