Contextual reasoning, one central capability, requires trusted data persistence. SAP Business Data Cloud delivers that foundation with SAP HANA Cloud as its high performance, multi-model processing layer, offering vector search, knowledge graphs, and relational capabilities that keep AI services grounded in real business data, relationships, and rules. Together, they help organizations scale AI from experimentation to enterprise value.

Enterprise AI creates meaningful value when it can do more than generate convincing responses. Effective AI solutions must have access to trusted business data, understand relationships among stakeholders, and operate within the rules that govern the organization. Without that foundation, AI systems can remain disconnected from how the business actually works.

At SAP Sapphire 2026, SAP introduced the SAP Business AI Platform as a foundation for building, connecting, and governing AI across the Autonomous Enterprise. The platform brings together AI, data, business process context, and governance to help organizations develop intelligent solutions that can scale across SAP and non-SAP landscapes. By creating reusable capabilities rather than isolated point solutions, organizations can extend the value of AI across teams, processes, and business domains.

A central capability of the SAP Business AI Platform is contextual reasoning. Contextualization gives an AI solution access to the data, business policies, relationships, semantic definitions, and process logic relevant to a task. Reasoning uses that context to determine an appropriate response, recommendation, or next action.

SAP Business Data Cloud provides trusted data and business semantics that support contextual reasoning across enterprise sources. SAP HANA Cloud complements that foundation as a high-performance, multi-model database and processing layer for applications, analytics, and AI services. Together, these capabilities help data and AI teams build solutions that are informed by the full context of the business rather than by disconnected information alone.

Engineering trusted, reusable data

Data engineers establish the trusted, reusable data foundation that contextual AI requires. They make relevant data accessible, define consistent business logic, and design integration patterns that meet operational, security, and governance requirements. Their work ensures that applications, analytics services, and AI workflows can use trusted information without creating unnecessary copies or inconsistent definitions.

SAP HANA Cloud supports federation, replication, and even batch transfer of SAP and non-SAP data. These options allow data engineers to select an integration approach that aligns with source-system constraints, workload needs, latency expectations, and governance standards. Within the SAP Business AI Platform, this flexibility helps make relevant business data available to the services and teams that need it.

Once data is available, engineers can use SQL, SQLScript, and calculation views to create reusable data models and transformations. Calculation views allow teams to define business logic close to the database, where applications, analytics, and AI workflows can consume it consistently. Rather than rebuilding data preparation for individual use cases, engineers can create governed models that support multiple types of workloads.

Storage design is also central to an effective data persistence strategy. Frequently accessed and performance sensitive data can remain in memory, while less frequently used data can be managed through the Native Storage Extension option. This approach helps data engineers align data placement with access patterns, operational requirements, and cost considerations as workloads evolve.

SAP HANA Cloud can also contribute to a broader business data fabric. SAP Datasphere provides business semantics and data federation capabilities, while SAP HANA Cloud provides high-performance data persistence and processing for data intensive workloads. Together, these services can establish a shared foundation for custom applications, analytics experiences, and AI-enabled services.

Finally, data engineers should design the data architecture to support authorization-aware access, policy controls, lineage, and traceability. These controls help ensure that AI services retrieve only the information appropriate to the user and business task. They also provide a foundation for investigating how data was accessed, transformed, and used to support an AI-generated result.

Turning business data into insight

Data scientists need timely, trusted business data to support experimentation, analytical processing, and operational use. Repeatedly moving information into separate data science platforms can introduce delays, create additional copies, and increase governance complexity. SAP HANA Cloud provides an environment where relational data, multi-model data, and machine learning capabilities can work together close to the business data that supports enterprise processes.

Embedded machine learning capabilities allow advanced analytical processing to occur in the database environment. Data scientists can work with business information in place instead of relying only on extracts that must be separately transferred, managed, and reconciled. This proximity can be especially valuable when models require current operational data or when analytical results must inform applications and business workflows.

Multi-model capabilities also allow data scientists to choose the data model that best fits an analytical question. Relational models can represent structured measures and business entities, JSON can support flexible application data, and spatial data can support location-oriented analysis. Graph structures can support the analysis of hierarchies, networks, and connected entities while retaining a shared connection to enterprise data.

A business data fabric can further strengthen this work by making governed data products and business semantics available throughout the data landscape. Data scientists can use curated assets in analytical workflows while SAP HANA Cloud provides the persistence and processing layer for data intensive tasks. This approach helps preserve the relationship among business context, analytical output, and the downstream services that use it.

Grounding AI in business context

AI engineers must connect AI services to the data, rules, and relationships that inform a reliable response or action. The SAP Business AI Platform provides an enterprise environment for building AI solutions around data and processes, but each service still requires task specific context. SAP HANA Cloud provides vector, property graph, knowledge graph, and relational capabilities that AI engineers can use to create this foundation of business context.

The vector engine supports embedding storage and vector similarity search. AI engineers can create embeddings for relevant content and store them alongside business records and operational data, then use similarity search to identify information that is semantically relevant to a user request. Keeping vectors and business data in a shared persistence environment can reduce unnecessary duplication and maintain a close connection between retrieved content and its source records.

Knowledge graph and property graph capabilities provide another form of business context. A knowledge graph can represent facts and relationships among products, policies, processes, organizational concepts, and other business entities that document search alone may not capture. Property graph capabilities can support connected data analysis for hierarchies, networks, and relationship paths that influence how an AI service interprets information.

Consider an AI agent that supports procurement decisions. The agent could use vector similarity search to identify relevant policy content, relational queries to determine the relevant supplier details, and graph processing to evaluate approval paths or supplier relationships. The result is a recommendation shaped by relevant content, current business records, and process context.

Joule Studio provides a unified environment for designing, building, and extending AI agents, applications, and workflows grounded in business context. SAP HANA Cloud can provide the persistence and processing layer that supports these solutions with structured, semantic, and relationship aware data. This combination helps AI engineers build services that retrieve relevant information, apply business constraints, and support intelligent workflows across the enterprise.

From experimentation to enterprise scale

Enterprise AI depends on collaboration among data engineers, data scientists, and AI engineers. Data engineers establish reliable access and reusable models, data scientists develop analytical and machine learning capabilities, and AI engineers apply context to create services that retrieve, reason, and act. A shared data foundation reduces the distance between these disciplines and helps organizations move AI solutions from isolated experiments into enterprise use.

SAP HANA Cloud provides a common foundation through high-performance processing, multi-model capabilities, and integrated analytical and machine learning functions. SAP Business Data Cloud contributes trusted data and semantic context, while the SAP Business AI Platform provides the broader environment for building, connecting, governing, and scaling intelligent solutions. Together, these capabilities can help organizations develop AI services that reflect the relationships, policies, and processes that shape the business.

A practical next step is to identify one high value workflow where AI needs both unstructured content and structured business context. Teams can then define the data sources, business rules, relationships, governance controls, and success measures required to support that workflow. Starting with a focused use case helps establish reusable patterns for contextual reasoning before extending those capabilities across additional applications, analytics services, and AI agents.

SAP HANA Cloud offers trial and free-tier options for teams that want to explore its capabilities, subject to applicable service plans, quotas, and regional availability. The basic trial provides a shared tenant environment with sample data and guided resources for evaluating selected capabilities. As teams identify practical use cases, they can use the free-tier version to prototype a technical design that can easily be converted to a production project.