Tag: research

For life sciences R&D teams, many of the obstacles to useful data and AI extend beyond one company’s systems. Research information moves among laboratories, partners and disciplines, yet the meaning of a term, the source of a finding or the rules for using a dataset may not move with it. Read about Progress' membership with the Pistoia Alliance and its approach to connected data, semantic context and governance for enterprise AI and analytics.

When it comes to FAIR life sciences data, keeping meaning and detail attached to pharmaceutical data supports trusted reuse across R&D, technology transfer and CDMO operations. Semantic harmonization can preserve source detail, add semantic context and relate different worlds without flattening them. Progress Data Platform helps connect complex data, documents and domain knowledge so information remains findable, reusable and trustworthy across teams, systems and partners.

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.

Hindered by complexity and frequent change, effective DevSecOps remains a challenge for many organizations. How can companies ensure they’re on the right track?