Tag: Progress Data Platform

Trust is now the differentiator: AI capability is rising fast, but enterprise adoption depends on governance, explainability and control. User-first beats tool-first: The winning model is bringing AI into the flow of work, not forcing people to learn complex tooling. Boring is what scales: Predictable, policy-aligned and auditable AI is what turns pilots into production outcomes.

AI fines are no longer theoretical; regulators are now enforcing control requirements as AI moves from pilots to production in financial services. The post explains why contracts alone won’t satisfy supervisors, what evidence regulators will expect to see in production and how organizations can operationalize governed, defensible AI with runtime guardrails, provenance, lineage and auditable controls—setting the agenda for the RegTech Conference in London on March 26.

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.

Progress Semaphore 5.10.2 helps organizations capture, govern, and reuse meaning with greater precision through enhancements like metadata on labels, SKOS collections, flexible metadata assignment, and concept reuse. With smarter semantic search and more collaborative modeling, this release makes trusted knowledge easier to discover, adapt, and use for confident, AI-ready decision-making.

Deep research is iterative, not transactional. AI must preserve context, reasoning and evidence across long-running investigations to be useful in R&D and turn isolated insights into institutional advantage. Trust is the gating factor here—when outputs can’t be traced, reviewed or defended, AI stalls at the pilot stage and never reaches production. It's production-ready AI that compounds research value.