Introduction
Every month, we conduct a short survey through our newsletter and social channels to understand what’s most relevant to modern data teams. Our goal is to surface practical benchmarks and peer-driven insights you can apply to optimize your own data stack. These reports are designed to reflect the evolving challenges faced by engineering, analytics, and operations professionals.
This month’s focus: Artificial Intelligence in the Data Stack.
AI capabilities rapidly become embedded into modern data tooling, from anomaly detection and pipeline tuning to metadata enrichment and cost optimization. But how far along are teams really? And what’s driving or blocking adoption?
We got survey responses from 116 data professionals across various industries and team sizes to find out.
Why This Survey Matters
AI is transforming the data stack, but successful adoption depends on more than just new features. It requires a strong foundation and strategic readiness. At the recent Gartner Data & Analytics Summit 2025, analysts highlighted that effective AI deployment is built on:
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High-quality, accessible data, since unreliable pipelines can derail any AI initiative
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Adaptable governance models, to ensure trust, accountability, and explainability
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Modular, open platforms that allow experimentation without vendor lock-in
Our survey provides an in-depth view of how teams are currently integrating AI into the data stack. These insights help teams benchmark their progress toward building an AI-ready stack that aligns with practical best practices.
Methodology
This report is based on a survey shared with targeted data engineering, analytics, and operations professionals.
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Total respondents: 116
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Company sizes: From startups to large enterprises
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Industries represented: SaaS, financial services, healthcare, retail, media, and manufacturing
Key Insights
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52% of teams are already seeing or expecting measurable value from AI
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AI is most commonly applied in data quality, transformation, and cost forecasting
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Only 14% are using AI extensively, while 62% are experimenting with or deploying it in focused areas.
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The top benefits are reduced engineering hours, proactive issue detection, and improved data reliability
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The biggest blockers include a lack of technical expertise, unclear ROI, and trust in data quality
How Teams Are Applying AI Today
To what extent is AI delivering business value?
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Insight
Over half of respondents see early or consistent business value from AI, signaling that pilot initiatives are maturing into operational wins. However, 42% are either not yet using AI or haven’t achieved measurable outcomes. This suggests that many teams are still stuck in the experimentation phase, often due to tooling limitations, data readiness, or lack of strategic alignment.
How is AI used operationally?
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Insight
Most teams are in the early or transitional stages of operationalizing AI, with 62% experimenting or applying it selectively. Just 14% report heavy AI reliance across workflows, revealing that while tooling may support intelligent features, full-stack AI automation is far from common. It also indicates that many organizations are testing AI in silos rather than embedding it as a cross-functional capability.
Where AI Is Being Applied
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Insight
AI is being adopted most actively in areas that directly improve reliability and performance, such as data quality and transformation. These are the stages where proactive intelligence, like anomaly detection or auto-enrichment, can directly reduce manual burden. Lower adoption in metadata and lineage or cost forecasting suggests that AI is still seen more as an engineering efficiency enabler than a strategic planning tool.
What’s Driving Adoption
Benefits Experienced (or Expected)
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Insight
The strongest value of AI lies in improving execution. Teams are not using AI to replace human judgment, but to accelerate pipeline stability and reduce firefighting. The benefits align with use cases like intelligent alerts, pipeline monitoring, and assisted transformations, tasks where time savings compound quickly.
What’s Slowing Adoption
Top Barriers to Greater AI Use
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Insight
A lack of interest isn’t holding back AI adoption; the operational burden of adoption is slowing it. Teams point to technical skill gaps, unclear ROI, and concerns around data quality and trust. These blockers highlight that successful implementation requires more than good models; it needs internal advocacy, cross-functional coordination, and workflows that don’t break when AI is introduced.
Tooling Preferences
How Teams Want to Use AI in Tools
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Insight
The majority of teams favor managed tools that include AI features that are out of the box, reflecting a desire for low-friction adoption and minimal infrastructure lift. The fact that nearly 30% still choose hybrid approaches shows that teams with strong engineering resources are open to customizing AI solutions, but only when they have a clear path to value and ownership.
Final Thoughts
AI is poised to transform the data stack, but the journey is not uniform. Teams that are already applying it are seeing faster time-to-insight and more efficient operations. For others, the biggest challenge is getting from idea to impact. The most effective AI features will be those that address real problems, demonstrate quick value, and integrate cleanly into existing pipelines. We’ll continue tracking this shift over time and bring you new insights as adoption deepens.
About Integrate.io
Integrate.io helps data teams simplify and optimize their pipelines by automating data centralization through a single, low-code platform. We support emerging AI capabilities while keeping your stack lean, reliable, and easy to manage.
Want to see how AI-enhanced workflows could work for your team? Request a demo!
