Progress Data Platform Summit 2026

Context

AI needs access to trusted information, but access alone is not enough.

Organizations also need to provide the relationships, meaning and business definitions that help AI understand how their business actually operates. Knowledge models can help capture and organize this context, connecting data to the concepts, relationships and terminology that matter to the business.

Without that context, AI may have access to the right information but still struggle to interpret it correctly.

Control

As AI becomes embedded in business processes, governance becomes increasingly important.

Organizations need visibility into what information AI can access, which policies apply and when human review is required. The question is no longer only what AI can do, but what it should be allowed to do.

Control helps ensure that speed does not come at the expense of accountability.

Confidence

Ultimately, organizations need confidence in AI-generated outputs.

And that confidence comes from transparency. Teams need to understand what data informed a recommendation, which business rules were applied and whether the result is appropriate for the decision being made.

These are not technology-only challenges. They are organizational challenges that require strong data foundations and well-defined governance practices.