Tag: Data & AI

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.

Progress MarkLogic FastTrack 2.0 empowers developers to build modern enterprise AI applications faster than ever to help organizations make actionable insight accessible to more people across the business.

Trust is now the differentiator: AI capability is rising fast, but enterprise adoption depends on governance, explainability and control. User-first beats tool-first: The winning model is bringing AI into the flow of work, not forcing people to learn complex tooling. Boring is what scales: Predictable, policy-aligned and auditable AI is what turns pilots into production outcomes.