The Model Context Protocol, or MCP, is quickly becoming part of the enterprise AI conversation. Its appeal is easy to understand. Instead of building a different custom integration every time an AI assistant or agent needs to interact with an external system, MCP provides a standardized way for AI applications to connect to tools, data […]
As enterprise AI moves from experimentation into real workflows, more technology vendors are talking about context layers. The challenge is that the term can describe very different approaches. One platform may specialize in semantic definitions. Another may focus on agent memory. A data catalog may expose governed metadata to AI, while a cloud data platform […]
As organizations connect AI assistants and agents to enterprise data, several familiar parts of the data stack are taking on new roles. Data catalogs organize and govern data knowledge. Semantic layers standardize business meaning. Governance systems define how information should be used. Now, the context layer is emerging as a way to make this knowledge […]
A context layer gives AI systems the business meaning, relationships, trust signals, and rules they need to understand and use enterprise data correctly. Giving an AI assistant or agent access to a database is relatively straightforward. Helping it understand what the data actually means inside your organization is much harder. A table might contain a […]
Published in Business Reporter The most expensive line item in enterprise AI is no longer compute – it is the gap between what AI promised and what it is actually delivering. After two years ofpilots, boards have stopped asking what AI can do and started asking what itreturns. By 2027, Gartner forecasts that 80 per […]
A conversation with Paloma, Senior Data & AI Strategist at DataGalaxy Portfolio, on why the most expensive failure in AI today is not a technical one — it is a communication one. There is a question quietly undermining AI programs in every organization right now. It gets asked in CFO reviews, board meetings, and investor […]
A vision from Nicolas, Chief Product Officer at DataGalaxy, on why technical explainability has become a comfortable illusion and how to build an accountability framework that actually keeps pace with AI. For the past few years, the conversation about responsible AI has revolved around explainability. Can the model tell us why it made a decision? […]
AI investment is accelerating fast, but for most organizations, the biggest blocker to scaling AI isn’t model capability. The biggest blocker for CDAOs and Heads of Data and AI is the lack of organizing their AI investments like an AI portfolio, with the fundamentals of portfolio management: visibility, accountability, cost governance, and product lifecycle management. […]
At some point in the last eighteen months, you probably ran a meeting to get visibility on what your organization is actually doing in AI. Someone pulled a deck together. A few team leads sent updates. And by the end of it, the original question was still not answered. The uncomfortable part is not that […]
A conversation with Joe Reis, author and host of The Joe Reis Show, on why the unsexy fundamentals of data management have become the most strategic investment leaders can make in 2026. For the past two years, every board meeting started with the same question: what’s our AI strategy. Decks were built. Pilots were funded. […]
Are you looking for a faster way to understand, trust, and use your data? Every organization aspires to make data truly self-service. However, without the right context, clarity, or confidence, users often fall back on old habits. These can include asking a colleague for help, copying an outdated report, or rebuilding a project from scratch. […]
