Operationalize your enterprise AI

It means making AI repeatable, governed, economical, and resilient enough to become part of how the business runs, rather than a one-off pilot. Teradata's POV explains it as the shift from "Can we build AI?" to "Can we operate AI across the enterprise?"

Executives accountable for AI outcomes, such as CIOs, CDOs, and chief AI officers; the data, platform, and AI leaders who run production; and the architects who evaluate the options.

The challenge is broader than the model itself. Context, infrastructure, deployment, control, security, privacy, and resiliency all become part of the AI architecture once you try to scale.

AI is moving from episodic, human-driven interactions to always-on agents that continuously access data, apply context, generate workloads, and take part in business processes. The operating model that supported experimentation isn't enough for that.

Understand, deploy, scale, control, and protect. Each addresses a different barrier between experimentation and sustained enterprise use, and they work best as one operating model rather than five separate technology decisions.

It's the cost and latency that build up when AI must rediscover business definitions, rebuild relationships, recreate governance rules, or compensate for missing knowledge through extra retrieval, prompting, and reasoning. It grows as the number of agents, models, users, and interactions grows.

It connects the systems where enterprise context already lives and turns that information into governed, reusable knowledge. It works across Teradata and third-party data, AI, and catalog environments, and it doesn't require Teradata to be your underlying data platform. Key elements are a native context graph, Industry Knowledge Models, deterministic retrieval, and governed, open context reuse. It also learns from AI responses, corrections, and outcomes, so context improves over time.

It's an integrated, ready-to-run, validated stack for data warehouse, lakehouse, analytics, AI, and agentic workloads, on configurable Dell and NVIDIA infrastructure in a customer-controlled on-premises environment. It reduces the effort of assembling and integrating each layer yourself, and lets you scale compute, GPUs, and storage independently by workload.

It combines persistent Active Compute with Elastic Compute, which adds focused, isolated capacity in smaller increments when needed. That avoids permanently expanding the production core and keeps variable workloads from competing with critical ones.

They have different performance profiles, concurrency patterns, service levels, and economics. A single scaling model can force you to overprovision or let variable workloads crowd out critical production resources.

It goes beyond data residency to three dimensions. Data sovereignty covers where data and workloads live and how they're governed. Operational sovereignty covers who operates the environment, plus continuity and recovery. Digital sovereignty covers preserving choice across infrastructure, models, inference, tools, and platforms.

Teradata Factory supports customer-controlled on-premises environments. Teradata Sovereign Cloud on Google Distributed Cloud Air-Gapped supports physically isolated, air-gapped deployments with a cloud operating experience.

More than stored data. The protection boundary expands to include the business knowledge, policies, relationships, and operational context AI depends on, across resiliency, security, privacy, and governance.

No. Most organizations begin by identifying the constraint blocking their next important AI workload, whether that's context, deployment, scale, control, or protection. The POV includes five questions, one per requirement, to help you find it.