Progress Data Platform

This blog explains the difference between AI ethics and AI governance, showing how responsible AI principles such as fairness, transparency, privacy, safety, accountability, and human oversight can be translated into practical policies, controls, workflows, and evidence. It outlines why governance matters as AI adoption grows, how organizations can manage risks, and how a structured approach helps teams build AI systems that are trusted, explainable, auditable and aligned with both enterprise goals and broader public-good outcomes.

With CHAPS 2027 ahead, the real opportunity for payment providers is not just meeting ISO 20022 requirements, but building the data foundation needed to use richer payment information with confidence.

AI success in the enterprise is no longer about how powerful it looks in demos, but whether it can be trusted to operate reliably, transparently and at scale within real business workflows. Organizations that win will be those that prioritize governance, context and repeatability to turn AI from hype into dependable infrastructure that supports real decisions.

Organizations are dealing with unprecedented amounts of data, and while this data has the potential to help drive more informed business decisions and facilitate AI projects, data silos can arise and prevent companies from realizing the true potential of their data. One solution to this challenge is Microsoft Fabric, a platform that allows data from multiple different sources to be unified in a single data lake where it can be connected to different analytics and reporting tools, like Power BI.