BARC x Actian Research: Why Data Products are Key to Scaling AI
Resumen
- 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.
Conclusiones del estudio global realizado en 2026 por BARC y Actian entre más de 300 responsables de datos en empresas
Organizaciones de todos los sectores están realizando importantes inversiones en inteligencia artificial. Sin embargo, muchas iniciativas siguen teniendo dificultades para ir más allá de la fase experimental y generar un valor empresarial constante.
Para comprender mejor qué diferencia a las iniciativas de IA que prosperan de las que se estancan, Actian se asoció con BARC, una firma líder mundial en análisis de datos, para llevar a cabo un estudio de investigación global entre los responsables de datos de las empresas.
Basándose en las opiniones de más de 300 encuestados de diversos sectores y regiones, el estudio analiza cómo las organizaciones adoptan y ponen en práctica los productos y contratos de datos, y cómo estos enfoques influyen en el éxito de las iniciativas de inteligencia artificial.
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.

Informe de investigación global de BARC × Actian (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.
Descargar el informe completo de investigación
Este artículo presenta varias conclusiones clave del estudio de investigación global realizado por BARC y Actian.
El informe completo, Productos de datos y contratos de datos en 2026: la base del éxito de la IA, analiza cómo las organizaciones de distintos sectores y regiones están adoptando y poniendo en práctica los productos de datos y los contratos de datos, y cómo estas prácticas influyen en la madurez de la IA, la gobernanza y los resultados en el mundo real.
Consigue el informe completo para conocer los resultados y descubrir cómo las organizaciones líderes están ampliando el uso de la IA mediante productos de datos.
Descargar el informe completo