Corporations should be ready with the suitable data architecture, and the following few months — years, at most — might be critical, says Irfan Khan, president and chief product officer of SAP Data & Analytics.
“The one prediction anybody can reliably make is that we do not know what is going on to occur within the years, months — and even weeks — ahead with AI,” he says. “To have the option to get quick wins immediately, that you must adopt an AI mindset and … ground your AI models with reliable data.”
While data has at all times been vital for business, it’s going to be much more so within the age of AI. The capabilities of agentic AI might be set more by the soundness of enterprise data architecture and governance, and fewer by the evolution of the models. To scale the technology, businesses must adopt a contemporary data infrastructure that delivers context together with the info.
More business context, not necessarily more data
Traditional views often conflate structured data with high value, and unstructured data with less value. Nevertheless, AI complicates that distinction. High-value data for agents is defined less by format and more by business context. Data for critical business functions — resembling supply-chain operations and financial planning — is context dependent. While fine-grained, high-volume data, resembling IoT, logs, and telemetry, can yield value, but only when delivered with business context.
For that reason, the actual risk for agentic AI is just not lack of knowledge, but lack of grounding, says Khan.
“Anything that’s business contextual will, by definition, offer you greater value and greater levels of reliability of the business final result,” he says. “It’s not so simple as saying high-value data is structured data and low-value data is where you’ve a lot of repetition — each can have huge value in the suitable hands, and that’s what’s different about AI.”
Context might be derived through integration with software, on-site evaluation and enrichment, or through the governance pipeline. Data lacking those qualities will likely be untrusted — one reason why two-thirds of business leaders don’t fully trust their data, based on the Institute for Data and Enterprise AI (IDEA). The resulting “trust debt” has held back businesses of their quest for AI readiness. Overcoming that lack of trust requires shared definitions, semantic consistency, and reliable operational context to align data with business meaning.
Data sprawl demands a semantic, business-aware layer
Over the past decade, a very powerful shift in enterprise data architecture has been the separation of compute and storage, cloud-scale flexibility, says Khan. Yet, that separation and move to cloud also created sprawl, with data housed in multiple clouds, data lakes, warehouses, and a large number of SaaS applications.
