Tag: Data & AI

Learn how to turn your style guide into an AI-powered self-review skill using GitHub Copilot to improve documentation quality, consistency and editorial efficiency.

With CHAPS 2027 ahead, the real opportunity for payment providers is not just meeting ISO 20022 requirements, but building the data foundation needed to use richer payment information with confidence.

AI pricing is increasing, but the real problem is token waste. Enterprises are overspending because poor data architecture forces models to process too much irrelevant context. Better retrieval, semantic enrichment, rules, and governance reduce cost, improve accuracy, and make AI more scalable.

Model Context Protocol (MCP) solves the real bottleneck in enterprise AI by standardizing how AI systems connect to tools, data and workflows. When combined with Progress Agentic RAG, it transforms retrieval into a reusable, governed capability, enabling AI agents to access trusted knowledge, compare sources and deliver grounded, traceable answers across multiple systems.

AI success in the enterprise is no longer about how powerful it looks in demos, but whether it can be trusted to operate reliably, transparently and at scale within real business workflows. Organizations that win will be those that prioritize governance, context and repeatability to turn AI from hype into dependable infrastructure that supports real decisions.