Xebia: On building the data foundation for AI agents – and then accelerating

Xebia's global CTO Niels Zeilemaker emphasizes that organizations implementing AI agents must prioritize building a strong data foundation first, as agentic AI performance scales directly with data quality and availability. The article argues that without proper data infrastructure and accessibility for AI consumption, organizations cannot effectively accelerate their processes using AI agents.

The article addresses a critical infrastructure challenge in the emerging agentic AI landscape: the prerequisite role of data foundation development. Zeilemaker's guidance reflects a broader industry maturation where practitioners recognize that deploying AI agents without foundational data work creates bottlenecks rather than acceleration. This perspective counters the tendency to rush into agent implementation without the necessary groundwork.

The emphasis on data strength as a scaling factor for agentic AI represents an important shift in how enterprises should approach AI adoption. Rather than viewing AI agents as plug-and-play solutions, organizations must conduct comprehensive data audits, ensure data quality, establish proper governance frameworks, and make data accessible across systems. This foundational work determines whether AI agents can meaningfully contribute to process automation.

For enterprise technology teams, this guidance impacts investment prioritization and project sequencing. Data infrastructure projects—often seen as unglamorous prerequisites—become critical dependencies for AI agent success. This creates demand for data engineering expertise, modern data platforms, and governance solutions.

Looking ahead, organizations will increasingly measure AI readiness not by agent capabilities but by data maturity. The companies that invest early in robust data foundations will gain competitive advantages in deploying functional AI agents, while those attempting shortcuts will face diminishing returns. This positions Xebia and similar consulting firms as essential guides through the complex data-first approach to enterprise AI transformation.

• →AI agents require strong data foundations to effectively scale and deliver meaningful process acceleration.
• →Organizations must prioritize data accessibility and quality as prerequisites before implementing agentic AI systems.
• →Data infrastructure investment is a critical but often overlooked dependency for successful AI agent deployment.
• →Enterprise AI readiness should be measured by data maturity rather than agent technology sophistication.
• →Consulting expertise in data foundation development becomes increasingly valuable as organizations pursue agentic AI initiatives.