Tag: Data & AI

Modernization doesn't require replacing every system at once. Instead, organizations can accelerate progress by connecting existing platforms through a data and integration layer, improving visibility, interoperability and access to critical information. This incremental approach reduces risk, preserves current investments and delivers value faster while creating a scalable foundation for future modernization.

Disconnected data across multiple systems can make it difficult for teams to access the full context needed for timely decisions. By connecting structured and unstructured data through a scalable integration layer, organizations can create a unified operational view, improve visibility and maximize existing technology investments without replacing critical systems.

This article argues that the biggest shift in AI design tools isn't better mockup generation—it's the emergence of agents that work directly on the design canvas, much like coding agents now operate inside code repositories. It maps the leading agentic design tools by where their agents live (canvas-native versus code-native) and contends that the most important strategic decision isn't which tool has the best AI, but where a team's source of truth resides: the design file, the codebase or the running product. Ultimately, the piece concludes that a well-structured design system has become the foundation of effective AI-assisted design, making workflow architecture—not flashy features—the key factor in choosing the right tool.

Traditional RAG systems are designed around a simple idea: find the most relevant information, add it to the prompt and generate an answer. This works well for questions where the answer can be found in a small set of relevant documents.

Keyword search and semantic search each solve different retrieval problems, but neither is perfect on its own.