AI is Driving a Surge in Data Budgets, According to New Report from dbt Labs

2025 State of Analytics Engineering Report reveals how the demand for quality data to feed AI is fueling data team growth

dbt Labs, the pioneer in analytics engineering, today 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 reveals that AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications.

Data Budgets Spike 

According to the report, data budgets are growing significantly this year with 30% of participants reporting budget growth compared to just 9% last year. Additionally, AI tooling was recognized as the largest area of investment for the year ahead with 45% of respondents citing it as a key priority.

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Data team sizes are increasing too (40% reported growth, compared to 14% last year), clearly demonstrating how AI is driving demand for larger teams that can better ensure high quality, governed data. Additionally, as data budgets increase, so have salaries in North America, with 80% of individual contributors making over $100,000 (compared to 69% last year) and 49% of managers making over $200,000 (compared to 32% last year).

Data Teams Increase AI Usage
AI isn’t just fueling investment, it’s also disrupting the way data professionals operate. The report found that 80% of respondents are using AI in their daily workflow, compared to just 30% last year. Of those using AI daily, 70% said they use AI for code development and 50% use it for documentation. Organizations are clearly increasing investments in tools to accelerate – not replace – their data teams, and leveraging AI in the data workflow is improving developer productivity while bolstering data quality in the process. As a result, company perception of data teams is positive, with respondents overwhelmingly agreeing (75%) that their organization values the data team.

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“AI is disrupting the way that teams work with organizational 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.”

Looking to the Future
Effective AI requires high quality inputs, and poor data quality continues to be the challenge most frequently reported (56% of respondents). That’s why building trust in data is cited as the top priority for growing data teams, accentuating the importance of data governance and observability – and data professionals are hopeful that AI can help. Data teams are optimistic about the potential impact of AI in the analytics workflow, citing its ability to help bridge the data quality gap with features like proactive data monitoring and pipeline debugging.

“This past year has shown us that investing in data is critical for AI success,” said Piyush Bhargava, Sr. Director Global Data & Analytics, Docusign. “Establishing trust in Artificial Intelligence starts with trust in enterprise data, which is why we’ve invested in a modern technology stack with dbt as a key pillar. By leveraging dbt to build reusable data assets, we’ve built a scalable data foundation and are now looking to boost productivity through new AI tools in dbt Cloud. This report confirms what we’ve seen firsthand: good data is the bedrock of strong AI, and with dbt as the cornerstone of our strategy, we’re well-positioned to continue driving innovation.”

On April 30, dbt Labs will host a panel of industry experts for the 2025 State of Analytics Engineering Virtual Event. The conversation will focus on strategies for building effective data organizations in a rapidly shifting landscape and address how data teams are integrating generative AI, adapting to economic shifts, and addressing persistent industry challenges.

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