And two I wanted to include to provide some practical guidance.
In a recent article on ChannelE2E, Pedro Ferreira emphasizes the critical need for governance in artificial intelligence implementations within organizations. As companies increasingly adopt AI technologies, including generative tools and large language models, they face heightened risks related to data security and compliance. The article outlines a nine-step framework for effective AI governance, highlighting the importance of discovering and classifying data, enforcing governance policies, and ensuring regulatory compliance across multiple evolving standards. Research shows that without robust governance, organizations risk exposing sensitive data and violating regulations such as the Health Insurance Portability and Accountability Act and the General Data Protection Regulation. The framework encourages continuous improvement and collaboration across various departments to safeguard data integrity and accountability in AI applications.
In a recent article from The New Stack, Jon Udell discusses the emergence of the Model Context Protocol, or MCP, as a transformative tool for enhancing the interaction between users and documentation in software development. The MCP has been likened to Really Simple Syndication, or RSS, due to its straightforward implementation and potential for rapid adoption. The article highlights how the MCP server facilitates improved access to documentation by allowing users to engage directly with content through a structured query system. This interaction not only aids in the immediate retrieval of information but also helps identify gaps in documentation that can be addressed, thus enhancing overall productivity. With the increasing reliance on large language models for interpreting documentation, the MCP represents a significant step forward in making software resources more accessible and user-friendly.
Why do we care?
Because both stories offer practical, implementation-level guidance that IT service providers can act on today—especially those helping clients adopt AI or improve developer workflows. Amidst a flood of aspirational AI talk, these two pieces stand out by focusing on execution frameworks and standards-based solutions that can drive real, repeatable value.