Tag: Progress Data Platform

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.

If 2023–2024 were the years of pilots and prototypes, 2025–2026 will be about orchestration, governance and scale. The signal across serious researchers is consistent: adoption is widespread and business impact concentrates where companies redesign workflows, measure outcomes and hard-wire trust and controls into the stack. McKinsey reports that ~80% of companies use generative AI (GenAI), yet most still aren’t seeing material earnings contribution, because scaling practices and operating models lag the hype. This gap is a roadmap that can be leveraged by Frontier Firms and individuals looking for an advantage (or many) in today’s AI-powered world.

What exactly is a vector, and more importantly, why should you care? In simple terms, a vector is a numeric representation of data. For example, a paragraph of text, an image, or even a sound clip can be transformed into a vector, which is a series of numbers that capture its meaning, context or features. Vectors allow computers to “understand” unstructured data and compare it in ways that traditional databases cannot, both of which are foundational to AI.

MarkLogic Server 12 enables you to speed innovation and power intelligent search experiences with native vector search, new relevance algorithms, optimized query scaling and more.

This blog post, titled "Building a Healthy OpenEdge Codebase: Practical Strategies for Reducing Technical Debt," provides insights and practical recommendations for maintaining a clean and efficient OpenEdge codebase, addressing technical debt and leveraging AI tools to enhance software quality and security.