SAP CEO Christian Klein on His Company’s Partnership with Databricks, Leveraging Its Position as a Custodian of Critical Business Information

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In a strategic move that could reshape how companies harness artificial intelligence covered by AI Insider this week, SAP and Databricks announced a landmark partnership to integrate Databricks’ capabilities into SAP’s enterprise resource planning (ERP) software. The collaboration aims to solve one of the most pressing challenges businesses face when implementing AI: fragmented data.

SAP CEO Christian Klein explained that while companies recognize AI’s potential, they struggle with scattered information across various systems.

“Every company today sees the massive potential of AI for their company. Now every company is also facing the same challenge: data silos,” Klein said in an interview with CNBC.

The partnership leverages SAP’s position as a custodian of critical business information.

“SAP sits on a big treasure. We are actually having access to the world’s most mission-critical business data,” Klein noted. By combining this data with Databricks’ technology, companies can create a unified view of their operations, from consumer behavior to supply chain management.

This integration will power SAP’s digital assistant, which can coordinate activities across various business functions. The company plans to release more than 400 new embedded AI use cases this year across its portfolio, ranging from supply chain to human resources and finance.

Klein also shared his perspective on the evolving AI landscape, particularly in light of recent developments like DeepMind’s advances.

“What really makes the difference at the end, where the value creation will happen, is on the business on the application side,” he said, explaining why SAP focused on building its AI assistant rather than developing its own large language models.

The partnership represents a significant step in SAP’s AI strategy, offering customers the ability to combine their enterprise data seamlessly while maintaining high data quality — a crucial factor for effective AI implementation. This approach allows businesses to extract meaningful insights and automate processes across their entire operation, potentially transforming how enterprises utilize AI in their daily operations.

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