How a unified data and AI platform can solve common AI adoption challenges
At the beginning of the AI boom, enterprises thought of the technology as a standalone product they could bolt on to their legacy solutions. Now, businesses are becoming more strategic with AI use cases and realizing the importance of domain expertise.
To deliver better intelligence and automation, companies need AI agents that can understand and reason in their unique environments. But as more organizations prepare to train AI systems on their own data, many find their data is stuck in proprietary systems. These silos are quickly undermining AI adoption and posing a significant threat to competitive edge.
Beyond data, fragmented governance frameworks and disjointed AI tooling are also quickly creating new operational barriers that impact agent performance.
How can enterprises rethink their AI strategies?
In the recent webinar, Smashing Silos: Driving Data Intelligence for Enterprise AI, Databricks Co-founder and SVP of Field Engineering Arsalan Tavakoli (Co-founder and SVP of Field Engineering, Databricks), Irfan Khan (President and Chief Product Officer of Data and Analytics, SAP) and Suresh Kaudi (Senior Information Officer, The World Bank) discussed the challenges to AI agent adoption, and how a unified data and AI platform can enable faster time to value.
Here are the key takeaways:
Enterprises remain excited about AI’s potential. Companies are doing many “proofs of concept,” but still struggling to deploy AI agents into the real world. And now, CFOs are starting to ask: “Where’s the value?
The key to getting back on the path to production is to refocus on the data layer. In the past, it was expensive and time-consuming for internal specialists to gather the data the business needed. Increasingly, organizations are investing in open, unified platforms to eliminate silos and enable more seamless sharing of assets across the enterprise.
For example, SAP serves as the operational backbone for many of the world’s most prominent businesses. And for AI agents to be effective, they need access to that information. In the past, enterprises had to rely on expensive workarounds to move data out of SAP systems. But on Databricks Data + AI Platform, they can easily combine SAP assets with the rest of the estate to train high-quality, domain-specific AI agents.
It also takes more than raw data to truly productionize AI accurately and trustworthily. Companies need to consider governance, evaluation, observability, and other components that together make up agentic AI systems.
During the webinar, Tavakoli highlighted three key challenges:
With a cohesive technology ecosystem, it’s easier to navigate each of these steps and ultimately build and scale the high-quality, domain-specific AI agents that enterprises want.
For example, the World Bank broke down its data silos by connecting the various sources into Unity Catalog, which also provides access controls, security, and governance for the centralized repository of data. As a result, AI agents can provide intelligence that would have taken six months for an analyst to do, according to Kaudi.
To learn how your enterprise can transition from general-purpose to specialized systems, check out the full conversation: Smashing Silos: Driving Data Intelligence for Enterprise.
A domain-specific AI agent is one trained to understand and reason within an organization's unique environment, rather than relying on generic, off-the-shelf capabilities. Building this kind of agent requires access to a company's own proprietary data, along with governance, evaluation, and observability to keep the system accurate and trustworthy. Without these pieces in place, agents struggle to reason effectively about a business's specific processes and terminology.
Data silos trap the information organizations need to train AI agents, which undermines adoption and threatens competitive edge. Beyond raw data access, fragmented governance frameworks and disjointed AI tooling create additional operational barriers that impact how well agents ultimately perform. Rethinking the data layer is described as the key step to getting stalled AI projects back on the path to production.
With the Databricks Data Intelligence Platform, companies can combine SAP data assets with the rest of their data estate to train high-quality, domain-specific AI agents. Previously, enterprises had to rely on expensive workarounds just to move data out of SAP systems before it could be used for AI. Because SAP serves as the operational backbone for many large businesses, this direct access is important for agents that need to reason over that information.
Databricks Co-founder Arsalan Tavakoli highlighted three key challenges: deciding what "good" looks like as agents take on more complex work, understanding the many interdependent parts of an agentic AI system well enough to manage them, and balancing the trade-off between accuracy and cost depending on the use case. A cohesive technology ecosystem makes it easier to navigate each of these steps. Addressing them together is what allows enterprises to build and scale high-quality, domain-specific AI agents rather than getting stuck in proof-of-concept mode.
The World Bank connected its various data sources into Unity Catalog, which provided access controls, security, and governance for a centralized repository of data. According to Suresh Kaudi, Senior Information Officer at The World Bank, this let AI agents deliver intelligence that would otherwise have taken an analyst six months to produce. The example illustrates how breaking down data silos can directly translate into faster, more valuable AI outcomes.
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