View From the Top by Tanya O’Connor, Vice President of Product Marketing, Progress Software
Enterprise AI Has a Credibility Problem: The Context Gap
Enterprise AI has a credibility problem. Not a model problem or a data volume problem.
Across industries, AI produces answers that look right but cannot be explained, trusted, or consistently reproduced. The issue is not the intelligence of the models; it is the inconsistency of the data behind them.
This has always existed. However, what has changed is the scale and consequence. AI is now operating across decisions that directly impact compliance, revenue, and reputation. At the same time, enterprises are expanding beyond structured data into unstructured and semi-structured sources, such as documents, transcripts, and logs, where critical context lives but is rarely connected or governed.
The result is predictable: AI amplifies inconsistency instead of resolving it. The challenge is not integrating more data; it’s making data understandable and trustworthy across systems, formats, and use cases. This is where a context layer becomes essential—not as another data store, but a way to connect data through shared meaning, relationships, and governance.
This is the role of the Progress Data Platform. By combining semantic structure with support for multimodal data, it enables organizations to unify structured and unstructured information into a consistent, governed foundation for AI. Because enterprise AI is not about generating answers. It’s about standing behind the answers, and this requires trusted context.
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Progress
https://www.progress.com/data-platform