Oracle Conference: Why Enterprise AI Is Moving Beyond GPUs to Governance and Multicloud

Key Points

• Enterprise AI priorities are shifting beyond GPUs and model training toward governance, security, identity, cost control and connecting AI to enterprise data across thousands of workflows.
• Oracle is positioning distributed cloud and multicloud infrastructure as a way to keep data in required locations while applying consistent APIs, security and governance across public, private, sovereign and partner-cloud environments.
• Batta urged companies to invest for flexibility by using open models, frameworks and APIs while planning for long-term inference costs, GPU utilization and network efficiency rather than tying infrastructure to one model or hardware generation.

Enterprise AI discussions are shifting from model selection and training speed toward the operational requirements of deploying artificial intelligence across business processes, according to Karan Batta, senior vice president of Oracle Cloud Infrastructure.

Speaking during The Six Five Summit: AI Unleashed 2026, Batta said organizations are increasingly focused on connecting AI systems to enterprise data while maintaining governance, security, cost control and the ability to deploy technology across diverse environments. Oracle NYSE: ORCL is seeing those priorities reshape how customers evaluate AI infrastructure, he said.

“It’s gone from how do I build an AI model to essentially how do I operationalize AI across my entire organization,” Batta said. While questions about models, graphics processing units and training performance remain important, he said chief information officers are now asking how they can connect AI to enterprise data, protect sensitive information, govern deployments and scale AI to thousands of employees and workflows.

Infrastructure Broadens Beyond Compute

Batta said AI infrastructure is no longer simply a matter of GPU compute. Instead, organizations need to consider security, identity management, databases and large data sets as AI becomes embedded across enterprise applications and workflows, including ERP, human capital management, supply chain, health care, financial services and manufacturing.

Cost management has also become a more prominent issue as AI use expands. Batta said customers are asking how to contain expenses as usage rises, including whether to focus on inference costs or build their own models.

He said enterprises generally do not want to maintain numerous separate AI platforms. Rather, they want “AI to come to the data,” instead of moving data into AI environments. That approach reflects the fact that enterprise data can reside on premises, in Oracle databases, in other public clouds, in newer cloud platforms, or within sovereign environments subject to regulatory restrictions.

Distributed Cloud and Governance

According to Batta, customers want to retain data where it resides while applying AI consistently across environments under a single security and governance framework. He characterized distributed cloud as a central component of Oracle’s approach to those requirements.

Batta said Oracle has deployed more than 50 to 70 dedicated regions and Oracle Alloy environments globally. He described several deployment options, including public cloud, dedicated regions at customer facilities, Cloud@Customer configurations, sovereign cloud and partner clouds using Oracle Alloy.

The goal, he said, is to provide the same code and services through different deployment models based on customer requirements. Customers also expect consistent identity, security and governance services across their data environments, including environments that extend to other cloud providers, he said.

Industry needs can vary substantially, Batta added. Health-care customers may prioritize patient privacy, data residency and low-latency clinical AI near hospitals. Financial-services organizations may focus on regulatory compliance, sensitive financial data and low latency for workloads such as high-frequency trading. Manufacturers may seek factory automation, predictive maintenance and AI capabilities close to production environments, while governments may emphasize sovereignty and national security.

Across sectors, Batta said, the common measures of success are keeping data within prescribed boundaries, locating AI near existing core infrastructure and maintaining customer control.

Multicloud Moves From Defensive to Strategic

Batta said multicloud strategies have evolved beyond their earlier role as a way to avoid vendor lock-in. Enterprises are now deliberately using different clouds because each may offer distinct technology capabilities, applications, models or specialized AI services.

Oracle’s multicloud effort began with Azure and later expanded to Google Cloud and Amazon Web Services, according to Batta. He said network connectivity was a key starting point because moving data economically between cloud environments is critical for AI workloads.

Batta described Oracle Database as a central data platform that can span cloud environments, allowing customers to use models and services where they choose. He said the objective is not to operate identical infrastructure everywhere, but to make on-premises and cloud environments work together seamlessly through common APIs and a flexible control plane.

“Customers aren’t asking for multicloud because they love the complexity that it comes with,” Batta said. “They’re asking for it because they want the freedom to choose the best of the best, irrelevant of where it lives.”

Guiding Principles for AI Investment

As AI models, hardware and software frameworks continue to change rapidly, Batta advised enterprise leaders to design for flexibility rather than make infrastructure decisions tied to a single model or hardware generation. Infrastructure should outlast current model choices, he said.

• Build around open models, frameworks, APIs and multicloud architectures.
• Plan for long-term AI economics, particularly inference costs, GPU utilization, network efficiency and operational simplicity.
• Keep infrastructure as invisible as possible to developers, data analysts and business users.

Batta said the desired outcome is for users to trust that AI systems will operate securely, perform reliably and scale economically without requiring them to focus on the underlying infrastructure.

About Oracle (NYSE:ORCL)

Oracle Corporation is a multinational technology company that develops and sells database software, cloud engineered systems, enterprise software applications and related services. The company is widely known for its flagship Oracle Database and a portfolio of enterprise-grade software products that support data management, application development, analytics and middleware. Over recent years Oracle has expanded its focus to include cloud infrastructure and cloud applications, positioning itself as a provider of both platform and software-as-a-service solutions for large organizations.

Oracle's product and service offerings include Oracle Database and the Autonomous Database, Oracle Cloud Infrastructure (OCI), enterprise resource planning (ERP), human capital management (HCM) and supply chain management (SCM) cloud applications (often grouped under Oracle Fusion Cloud Applications), middleware such as WebLogic, and developer technologies including Java and MySQL.

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