The integration of large language models (LLMs) into existing enterprise data stacks is no longer a futuristic concept; it’s a present-day imperative. Organizations aren’t simply experimenting with generative AI; they’re deeply invested. According to research from Enterprise Strategy Group, now part of Omdia, 64% of organizations plan to make significant investments in generative AI in the next 12 months.1 And they’re demanding AI that understands their unique business context and integrates seamlessly into their existing infrastructure. The discussion is about far more than model performance; the focus has shifted to reliable deployment and management of sophisticated models within secure, governed environments. This is especially true for high-stakes applications, where 72% of organizations prioritize built-in capabilities for their generative AI strategies. And this demand is fueling a significant increase in collaboration between AI model providers and data management platform providers.
With the increased pressure to leverage AI agents, organizations are ready to jump in, but they face significant challenges in building and deploying them effectively. Organizations struggle with ensuring security, accuracy, and access control over their proprietary data, and require systems that can efficiently handle large, complex data sets. Building custom AI agents requires specialized expertise, substantial resources, and effective strategies for mitigating risks. And then comes the ROI skeptics, which add delays and prevent these projects from ever getting into production. These challenges collectively are already affecting businesses, from increased project costs (41%) to lengthy rework (30%). A robust solution must not only deliver advanced reasoning capabilities but must also address these critical concerns end-to-end, mitigating risks and optimizing performance.
Enterprises need a unified platform that seamlessly integrates AI models that deliver advanced reasoning with their existing data infrastructure. Organizations are prioritizing partners that offer end-to-end solutions, integrating seamlessly with existing infrastructure while ensuring responsible AI development. And they desire access to
cutting-edge technologies, as cited by 49% of organizations, with a strong emphasis on time to value. The good news is when an organization finds the right partners, success is likely to follow. Ninety-one percent of organizations agreed that working with partners has enabled them to see value from generative AI faster than expected.
Powering AI Agents With Databricks and Anthropic
Databricks, a leader in data and AI, and Anthropic, a prominent AI research and safety company, have announced a five-year strategic partnership that brings Anthropic’s cutting-edge Claude models directly to the Databricks Data Intelligence Platform. This collaboration makes Anthropic’s latest model, Claude 3.7 Sonnet—a hybrid reasoning model—available to Databricks’ extensive customer base across all the major cloud platforms.
The integration is native and is available via SQL queries and model endpoints, offering key advantages for customers, including the ability to build and deploy secure, domain-specific AI agents that leverage their enterprise data through retrieval-augmented generation. Between Databricks’ Mosaic AI and Unity Catalog, which provide tools to build domain-specific AI agents with robust and unified data and AI governance, and Claude’s advanced reasoning capabilities to enable complex workflows, this offers significant advantages, including simplified integration, reduced costs through eliminating data replication, and enhanced security and control. Through this unified approach, customers gain confidence in their ability to build, deploy, and govern AI agents securely and responsibly, ensuring that ethical considerations are met throughout the development and deployment lifecycle.
1. Source: Enterprise Strategy Group Research Report, Navigating the Generative AI Partner and Alliance Landscape, November 2024. All research presented in this brief are taken from this research report.