Companies need to be ready with the right data architecture, and the next few months — years at most — will be critical, says Irfan Khan, president and chief product officer of SAP Data & Analytics.
“The only prediction anyone can reliably make is that we don’t know what’s going to happen with AI in the coming years, months or weeks,” he says. “To be able to get quick wins now, you need to adopt an AI mindset and … ground your AI models with reliable data.”
While data has always been critical to business, it will be even more so in the age of AI. Agentive AI capabilities will depend more on the robustness of enterprise data architecture and governance and less on the evolution of models. To scale the technology, enterprises need to adopt a modern data infrastructure that provides context with the data.
More business context, not necessarily more data.
Traditional views often associate structured data with high value, and unstructured data with low value. However, AI complicates this distinction. High-value data for agents is defined less by format and more by business context. Data for critical business functions—such as supply chain operations and financial planning—is context dependent. While great, high-volume data, such as IoT, logs, and telemetry, can create value, but only if provided with a business context.
For this reason, says Khan, the real threat to agentic AI is not a lack of data, but a lack of foundation.
“Anything that’s relevant to the business context, by definition, will give you a higher value and higher level of confidence in the business results,” he says. “It’s not as simple as saying high-value data is structured data and low-value data is where you have a lot of iteration — both can have a lot of value in the right hands, and that’s what’s different about AI.”
Context can be captured through integration with software, on-site analysis and enrichment, or through a governance pipeline. Data lacking these features will likely become unreliable—one reason why two-thirds of business leaders don’t fully trust their data. According to the Institute for Data and Enterprise AI (IDEA).. The resulting “trust debt” has held back businesses looking for AI readiness. Overcoming this lack of trust requires shared definitions, semantic consistency, and a trusted operational context to align data with business meaning.
The proliferation of data demands a semantic, business-aware layer.
Khan says that over the past decade, the most significant change in enterprise data architecture has been the separation of compute and storage, cloud-scale flexibility. Yet, this separation and move to the cloud also created sprawl, with data residing in multiple clouds, data lakes, warehouses, and a multitude of SaaS applications.