Report reveals that investment in AI and data tooling on the rise as data teams scale to meet growing demands
10 June 2025 – dbt Labs, the pioneer in analytics engineering, has published its third annual State of Analytics Engineering Report, sharing new insights into the evolution of the data industry in the artificial intelligence (AI) era. The report revealed a major surge in investment in data teams, driven by the rapid adoption of AI, with 30 percent of respondents reporting budget increases. Additionally, the report also highlights that AI is reshaping how data teams work, with a surge in investment in AI tooling and a shift toward more automated, scalable workflows.
The report, based on responses from nearly 500 global data professionals, shows that investment is bouncing back after a cautious year. Data stack budgets are rising across the board, and analytics teams are not only growing in size, but also in influence.
Key findings from the report include:
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AI adoption is accelerating: 80 percent of data professionals already use AI in their daily workflows (a significant increase over last year’s 30 percent), and 45 percent plan to increase investment in AI tooling over the next 12 months
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AI is augmenting teams, not replacing them: 70 percent of data professionals use AI for code development, but 40 percent of teams still grew in headcount.
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Data quality is top of mind: Over half (56%) cited poor data quality as their biggest challenge, with growing adoption of observability and semantic tooling growing as a result.
Interestingly, while the tech industry is most heavily represented in the analytics engineering community, the report finds increased representation in regulated sectors including financial services (15 percent) and healthcare (10 percent).
“AI is disrupting the way that teams work with organisational data,” said Mark Porter, CTO of dbt Labs. “As companies increase AI investments, leaders are prioritizing the teams responsible for data quality and governance – the essential foundation for AI effectiveness. At the same time, data engineers are turning to AI to automate routine tasks, completely changing how data is delivered to the business. Because of this, the strategic role of the data team continues to grow, with AI as the catalyst. It’s a symbiotic relationship – data professionals make AI better, and AI makes data teams better.”