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Now, unstructured data is growing at three times the rate of structured data, yet most platforms were not built to handle both together. The consequence is that AI agents cannot access a comprehensive enterprise context. Think about a financial services agent trying to answer a question about loyalty discount eligibility. It needs to combine unstructured policy documents with structured business data simultaneously, and SQL alone simply cannot address that. Without unified access, enterprises are left with incomplete answers and agents that cannot do their job properly.
What is your favourite feature of Enterprise Vector Store?
Teradata’ s ability to concurrently deliver structured data and vector data processing with the same engine not only implies efficiency and ease of use, but it also means that enterprises can co-locate their data and knowledge with their AI tools eliminating risks that can arise from movement of data. Teradata Enterprise Vector Store is multi-modal with built-in ETL and feature enrichment capabilities for documents, audio, images and soon videos. Lastly, Teradata’ s compute choices enable a choice across always-on efficiency with mixed workloads as well as ephemeralelastic capabilities for workloads that fit that pattern.
How do you see the workforce changing over the coming year? future, it will feel like working with a human. You ask a complex inventory question, and agents instantly pull the context, run the analytics and give you prescriptive, explainable strategies to act on.
Every engineering team inside Teradata is already building agents to make our own stack more intelligent and easier to use. The workforce will increasingly be defined not by who can operate software, but by who can collaborate effectively with agents.
What’ s next for Teradata?
We’ re focused on one central mission: making enterprise Agentic AI real, not just in pilots, but in production, at scale, with full governance. The launch of our Enterprise AgentStack brings together AgentBuilder, our MCP infrastructure, AgentEngine, and AgentOps into a single integrated toolkit that takes organisations from experimentation to deployment.
Every engineering team inside Teradata is working to make our stack more agentic, intelligent and easier to use, and we are moving toward a chat-first, natural-language-driven interface, where working with our platform will feel like working with a human. I always tell my team that distribution is everything in innovation. We may have built a Ferrari, but if it stays in the garage, nobody benefits. The real challenge is turning that potential into something people can use with confidence.
The workforce transformation we are entering can be exciting when approached with the right mindset. We are building for a world where humans, agents and data all work together, because this is not about replacement, but collaboration.
The shift I see most clearly is the move from workflow-centric work to outcome-centric work. Today, knowledge workers spend large amounts of time navigating applications and manually connecting dots across siloed systems. AI agents will absorb much of that friction. When you work with an intelligent platform in the
What differentiates our approach is that we’ re not asking customers to choose between capability and control. Teradata is the knowledge fabric that binds enterprise AI together. We are a governed, high-performance data foundation that makes every model, every agent and every workflow more effective and more trustworthy, whether deployed on-premises or in the cloud. As AI agents become a standard feature of enterprise operations, the question won’ t be which LLM you choose; it’ ll be whether your data platform can support them at scale, with integrity. That’ s where we’ re building, and that’ s where we’ re winning. x
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