The AI revolution demands a new data foundation
The rapid rise of AI, including Agentic AI and autonomous automation, is pushing enterprises to rethink their data architecture. Legacy systems often struggle to scale and meet the real-time demands of AI. Organizations that modernize now have an opportunity to transform competitively, scale AI initiatives, drive efficiencies, and unlock new revenue streams. Traditional data architectures are no longer fit for purpose—they were built for historical analytics and static reporting. Data architecture for an AI-driven enterprise must be intelligent, autonomous, and operate in real time. Modern AI governance programs require organizations to reimagine data lineage and stewardship, and those attempting to retrofit legacy architectures often face significant investment needs. Data is the fuel for AI—but only if it is accessible, high-quality, contextual, and available in real time. One of the biggest challenges facing organizations today is fragmented, inconsistent, and disconnected data. This barrier can prevent them from scaling advanced AI effectively.
