Progress Data Platform

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.

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.

R&D doesn’t lack data—it lacks signal. AI-driven knowledge discovery only works when answers are grounded in trusted, contextual enterprise data, not probabilistic guesswork. Most AI tools break trust before they create value. Treating research data like generic internet content strips away context, provenance and scientific rigor. Boring, reliable AI wins in 2026. Knowledge discovery that is governed, explainable and embedded into real R&D workflows is what turns AI from pilots into lasting outcomes.

A strong data foundation is the offensive line of AI—rarely celebrated, but absolutely essential to protecting performance, ensuring consistency and powering every winning play.