Enterprise AI is putting data management to the test with its ever-expanding scale and increasingly diverse range of use cases. The challenge has become much broader than simply storing or querying information, because models don’t just retrieve information. Rather, they actively interpret, synthesize and act upon it. In legacy environments, duplicate records or stale data may remain isolated within a single workflow or query. AI systems, by contrast, can amplify those defects across complex chains of automated actions.
Progress Software moved to address this recurring problem on September 22 with its $400 million purchase of Domo’s AI and data platform business. The acquired operation brings over 2,400 business customers into Progress and spans business intelligence, data integration, analytics, workflow automation, AI-powered data products and agents, and data governance. That breadth is significant because the data requirements surrounding AI are only going to become more complex. For instance, an AI application or agent may need to find information across systems, understand what that information means in a business context, respect access policies, and then trigger a workflow or action. Given the complexity of these use cases, simply giving the model access to more data doesn’t by itself guarantee a trustworthy or useful result. In other words, the underlying information still needs to be connected, governed, and placed in the correct context.
CEO Yogesh Gupta described the shift by saying that “the challenge is no longer simply access to AI technology.” The bigger requirement is instead creating trusted context around information while maintaining governance, security and oversight at increasingly massive scale. Domo’s cloud-native data platform, which will now become part of Progress’ existing data offerings, will extend the company’s ability to connect, analyze and act on information across different business systems.
The acquisition is one of the latest examples of how the market is responding to an increasingly urgent need for broader data platforms as enterprises scale AI. Traditionally, things like data integration, analytics, semantic context, governance and automation have largely all been handled by different products and teams. However, this has created a fragmented environment where true interoperability—and, therefore, AI at scale—has become notoriously difficult to achieve. Bringing more of these mission-critical functions under the same environment is now a top priority for enterprises across the board, given that they increasingly need a way to retrieve information and automate actions without losing visibility into how those decisions and actions were made.
Although the demand for consolidation is high, it isn’t without its caveats. While a broader platform may simplify integration and governance, enterprises also need to consider product overlap, roadmap changes and perhaps most importantly, the greater risk of getting locked into a single vendor. Progress has only just closed the transaction, however, so exactly how Domo’s technology will be integrated remains to be seen. As far as software and data leaders are concerned, the most important message is that AI readiness now depends on far more than the model layer itself.