Build AI agents around the way business works

Oracle Industry Data Platform is a planned industry-aware data foundation designed to support AI applications, analytics, and industry-specific solutions.

It aims to connect relevant enterprise and external data with reusable industry models and ontologies that describe business entities, relationships, and processes.

Organizations often need to integrate, model, and prepare similar information separately for different applications and AI initiatives. Oracle Industry Data Platform is intended to provide a shared foundation that teams can build on across use cases, helping reduce repetitive data preparation and modeling work.

Data lakes, lakehouses, and data warehouses provide infrastructure for storing, processing, and analyzing data. However, applications and AI agents also need business context to interpret what that data represents and how different entities relate.

Oracle Industry Data Platform is intended to complement existing data architectures by adding reusable industry models, ontologies, and data products.

Rather than replacing existing infrastructure, the proposed platform would help connect information with industry-specific business meaning. Its value would depend on the customer's existing architecture, available data, and requirements.

Oracle Industry Data Platform is intended to support data from Oracle industry applications, Oracle Fusion Applications, other enterprise systems, and relevant external providers.

Potential external data sources include weather, economic, climate, market, and risk information. These sources could provide additional context for industry-specific applications and AI use cases.

The platform aims to support the use of relevant information across different systems while accounting for data access permissions and third-party licensing requirements.

Specific integrations, supported sources, data flows, and availability require confirmation.

Oracle Industry Data Platform is intended to bring together structured business records and relevant unstructured information.

Structured data might include transactions, contract metadata, customer accounts, and asset records. Unstructured information could include documents, emails, messages, and other business content.

By associating these sources with relevant business entities, the proposed platform could help applications interpret information within a broader industry context.

For example, a construction application could potentially connect project records with relevant contracts and supporting documents to provide additional context about project activities.

The approach would depend on available integrations, data quality, access permissions, and customer-defined controls.

Industry models define common business entities and their relationships, while ontologies describe industry concepts and how those concepts relate to one another.

Oracle is developing industry ontologies intended to help applications interpret information consistently across different systems.

For example, an industry model might describe relationships between projects, contracts, change orders, and invoices, allowing applications to understand how those records relate within a business process.

Oracle Industry Data Platform aims to combine Oracle's industry knowledge with each customer's own data and business context.

This approach could help organizations build on shared industry definitions rather than developing every model independently.

Specific models, ontology coverage, and availability require confirmation.

Oracle Industry Data Platform aims to make connected and modeled information available through reusable data products or services.

A data product could represent a defined collection of business information, including relevant entities and relationships, that multiple applications or AI use cases could consume.

Teams could build on shared data resources while adding the business logic, workflows, and information required for individual applications.

This approach is intended to separate reusable data preparation and modeling activities from application-specific development.

The availability of predefined data products, supported interfaces, and extension capabilities requires confirmation.

Oracle Industry Data Platform is intended to support customer-defined data access controls for information used across applications, analytics, and AI use cases.

These controls could help customers manage which users, applications, and services are authorized to access relevant data.

Third-party information would also need to be used in accordance with applicable licensing terms, permissions, and customer policies.

The platform's proposed approach would not replace customers' responsibilities for establishing appropriate data governance practices, managing permissions, or meeting applicable requirements.

Specific access-control capabilities and licensing arrangements require validation for each implementation.

AI applications and agents need more than access to individual data records. They also need relevant information about business entities, relationships, and processes to interpret data within a specific industry.

Oracle Industry Data Platform aims to provide this context through reusable industry models, ontologies, and connected business information.

For example, an AI agent working with project information could potentially use relationships between contracts, change orders, and invoices to better understand the business situation associated with a project.

This context could help agents generate more relevant responses, support recommendations, and inform business actions.

Agent behavior, permissions, and operational controls would still need to be designed and validated for each use case.

Oracle Industry Data Platform envisions associating relevant business information and interaction history with entities such as customers, accounts, projects, or assets.

This approach could help AI agents reference appropriately authorized information from previous interactions when responding to subsequent requests

For example, an agent supporting an asset-management workflow could potentially use relevant historical information alongside current asset records to interpret a new request.

The proposed functionality would depend on how interaction context is captured, retained, accessed, and made available to applications.

Details of agent memory, data retention, permissions, and other safeguards require confirmation.

Oracle Industry Data Platform aims to help organizations make industry data more useful across applications, analytics, and AI initiatives.

Potential benefits include reducing repetitive data preparation, increasing reuse of industry models and data products, and providing applications with more consistent business context.

By allowing teams to build on shared information and definitions, the proposed platform could help them spend less effort recreating similar data resources for individual projects.

These capabilities may support more efficient application development and better-informed business decisions.

Actual results would depend on the use case, existing infrastructure, data quality, available functionality, and implementation.

Developers often spend significant effort finding, connecting, interpreting, and preparing data before building AI applications.

Oracle Industry Data Platform aims to provide reusable industry models, ontologies, data products, and platform capabilities that could help reduce repetitive preparation work.

Teams could build on this shared foundation when developing applications and AI agents rather than assembling similar data resources for every project.

This approach may help developers focus more effort on application-specific functionality and business requirements.

Production readiness would still depend on integration, data quality, testing, security, access controls, and operational requirements.

Oracle Industry Data Platform is intended to support the development of industry-specific applications, analytics, and AI use cases that benefit from connected data and shared business context.

Potential examples include construction and engineering project insights, customer retention analysis, personalized customer experiences, and proactive infrastructure operations.

For example, a construction application could potentially use relationships between projects, contracts, and change orders to provide additional project context. An infrastructure application might combine asset records with relevant operational information.

These examples illustrate potential uses of the platform and do not represent a list of available packaged solutions.

Feasibility would depend on the industry, data sources, integrations, and Oracle services involved.

Oracle Industry Data Platform is intended for developers, product teams, data engineers, data scientists, and architects building industry-specific applications, analytics, and AI agents.

Data teams could use the proposed foundation to prepare and organize information for reuse, while application developers could build on shared industry models and data products.

Architects could use these capabilities to incorporate industry context into existing data and AI environments.

Business users and customers could benefit indirectly through applications and AI agents developed using the platform.

The specific tools and capabilities available to each audience require confirmation.

The Oracle Industry Data Platform vision includes self-service data discovery and exploration capabilities for development and product teams.

Developers could potentially access relevant information through reusable data products, APIs, or other platform services, subject to applicable permissions and access controls.

Teams could then extend shared industry models and data resources with additional information, application logic, and workflows to address specific business requirements.

This approach is intended to support specialized applications and AI agents while allowing teams to reuse common industry context.

Specific discovery interfaces, APIs, extension mechanisms, development tools, and availability require validation.