Tag: Data & AI

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.

This blog explains how Mixture of Experts models work, why they improve performance and efficiency and how they compare to traditional model architectures.

This blog explores how organizations progress through stages of AI maturity, from early experimentation to operational transformation and why growth happens step by step.

Progress Corticon 7.3 and Corticon.js 2.4 introduce AI-powered test data generation, deeper AI integration, and governance-friendly export options, dramatically reducing manual testing effort and accelerating decision automation. These updates enhance accuracy, transparency, and speed, enabling organizations to model, validate, and deploy automated decisions with greater confidence.