Building a strong data infrastructure for AI agent success

In the rapidly evolving landscape of artificial intelligence, businesses are standing at a critical juncture. According to Irfan Khan, president and chief product officer of SAP Data & Analytics, the next few months—or at most, the next few years—will be decisive for companies aiming to harness the full potential of AI. His message is clear: organizations must be prepared with the right data architecture, or risk being left behind in the AI revolution.

“The only prediction anybody can reliably make is that we don’t know what’s going to happen in the years, months—or even weeks—ahead with AI,” Khan asserts. This uncertainty underscores the need for businesses to adopt an AI mindset and, crucially, to ground their AI models with reliable, high-quality data. The stakes have never been higher, as data’s role in business is set to become even more pivotal in the age of AI.

Traditionally, data has been a cornerstone of business operations. However, as agentic AI—AI systems capable of autonomous decision-making—becomes more prevalent, the importance of robust data architecture and governance cannot be overstated. The capabilities of these advanced AI systems will hinge less on the evolution of the models themselves and more on the soundness of the underlying data infrastructure. To truly scale agentic AI, businesses must embrace a modern data infrastructure that not only delivers data but also provides the essential context needed for meaningful insights.

One of the most significant shifts in enterprise data architecture over the past decade has been the separation of compute and storage, enabled by cloud-scale flexibility. While this separation has brought about unprecedented scalability and agility, it has also led to data sprawl. Data now resides across multiple clouds, data lakes, warehouses, and a myriad of SaaS applications. This fragmentation presents a formidable challenge: how to ensure that AI systems have access to the right data, with the right context, at the right time.

A common misconception is that structured data is inherently more valuable than unstructured data. However, the advent of AI has complicated this distinction. High-value data for AI agents is defined less by its format and more by the business context it provides. For critical business functions—such as supply chain operations and financial planning—data must be context-dependent to be truly useful. While fine-grained, high-volume data from sources like IoT devices, logs, and telemetry can yield significant value, this is only possible when such data is delivered with relevant business context.

The real risk for agentic AI, according to Khan, is not a lack of data but a lack of grounding. “Anything that is business contextual will, by definition, give you greater value and greater levels of reliability of the business outcome,” he explains. This perspective challenges the traditional view that high-value data is synonymous with structured data, and low-value data with unstructured, repetitive information. In the realm of AI, both can hold immense value—provided they are leveraged correctly.

Context can be derived through various means, including integration with software, on-site analysis and enrichment, or through the governance pipeline. Data that lacks these qualities is likely to be untrusted—a significant issue, given that two-thirds of business leaders do not fully trust their data, according to the Institute for Data and Enterprise AI (IDEA). This “trust debt” has become a major barrier to AI readiness. Overcoming it requires shared definitions, semantic consistency, and reliable operational context to align data with business meaning.

Data sprawl demands a semantic, business-aware layer to bridge the gap between raw data and actionable insights. This layer must be capable of understanding and interpreting the nuances of business context, ensuring that AI systems can make informed decisions based on accurate, relevant information. Without this, the promise of agentic AI remains unfulfilled, and businesses risk falling behind in an increasingly competitive landscape.

In conclusion, the path to AI readiness is paved with data—but not just any data. It is data that is well-governed, contextually rich, and seamlessly integrated across the enterprise. As businesses navigate the uncertainties of the AI era, those who invest in building a robust data architecture today will be the ones who reap the rewards tomorrow. The next few months and years will indeed be critical, and the time to act is now.

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