BARC x Actian Research: Why Data Products are Key to Scaling AI
Summary
- Organizations with mature data products are far more likely to scale AI, with 85% running multiple AI projects in production.
- Agentic AI adoption is significantly higher in these organizations, with 77% deploying autonomous systems versus limited adoption elsewhere.
- Data products provide structured, governed, and reusable data foundations required for reliable production AI.
- Strong data ownership, quality standards, and contracts enable safer and more scalable AI operations.
Insights from the 2026 BARC x Actian global research study of 300+ enterprise data leaders
Organizations across industries are investing heavily in AI. Yet many initiatives still struggle to move beyond experimentation and deliver consistent business value.
To better understand what separates AI initiatives that scale from those that stall, Actian partnered with BARC, a leading global analyst firm for data and analytics, to conduct a global research study of enterprise data leaders.
Based on insights from more than 300 respondents across industries and regions, the study examines how organizations adopt and operationalize data products and data contracts, and how these approaches influence the success of AI initiatives.
One of the clearest insights emerging from the research is the strong relationship between data product adoption and the ability to scale AI into production.
BARC × Actian Global Research Report (2026)
Organizations With Mature Data Products Run More AI in Production
Many organizations today are experimenting with AI. However, moving AI initiatives from experimentation into production remains difficult.
The research reveals a clear pattern: organizations that deploy data products at scale run significantly more AI systems in production.
Among organizations that deploy data products company-wide, 85% report running multiple AI projects in production. By contrast, organizations that are not using—or only experimenting with—data products are far less likely to operationalize AI beyond pilot initiatives.
This finding highlights the role data products play in enabling reliable AI systems. By organizing data assets with clear ownership, defined quality expectations, and reusable interfaces, organizations create the structured data foundation required to support production AI.
Correlation between AI project adoption and data product usage – © BARC 2026
Agentic AI Adoption is Highest in Organizations With Mature Data Products
The same pattern appears when looking at agentic or autonomous AI systems.
Agentic AI systems—capable of acting autonomously, orchestrating workflows, and interacting with multiple data sources—require highly reliable and well-governed data environments to operate safely.
The research shows that 77% of organizations with company-wide data products already have at least one agentic or autonomous AI system in limited or full production. The inverse also holds true: 77% of organizations that are only experimenting with data products—or not using them at all—have not yet deployed agentic AI in production.
This finding reinforces an important insight: as AI systems become more autonomous and integrated into operational workflows, the quality, governance, and reliability of underlying data become even more critical. Data products provide the structure needed to manage these dependencies and support the safe deployment of increasingly complex AI capabilities.
Correlation between agentic AI adoption and data product usage – © BARC 2026
Why Data Products Enable AI at Scale
The correlation observed in the research reflects the role that data products play in modern data architectures.
By organizing data assets as products—with defined ownership, quality standards, and clear interfaces—often reinforced through data contracts—organizations create a reliable foundation for analytics and AI systems.
This approach helps ensure that AI systems rely on trusted, well-managed data, reducing the risks associated with inconsistent inputs, unclear responsibilities, or fragile data pipelines.
As a result, data products are emerging as a critical capability for organizations seeking to operationalize AI at scale.
Download the Full Research Report
This article introduces several key insights from the BARC x Actian global research study.
The full report, Data Products and Data Contracts in 2026: The Foundation for AI Success, explores how organizations across industries and regions are adopting and operationalizing data products and data contracts—and how these practices influence AI maturity, governance, and real-world outcomes.
Get the full report to explore the findings and learn how leading organizations are scaling AI with data products.
Download Full Report
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