Spend :01 of your time each Monday morning as Twelve:01 delivers timely tools, trends, strategies, and/or compliance insights for the CME/CE enterprise.
Microsoft Copilot can evolve how CME/CE professionals interact with Excel by turning manual data work into automated processes. With simple prompts, users can ask Copilot to auto-categorize large datasets, create standardized labels, or even detect patterns that would otherwise require complex formulas. It can also be utilized to generate sortable drop-down menus and apply data validation rules. Copilot can help to produce ranked summaries of key metrics and quickly identify top-performing categories or trends without building pivot tables. For CME/CE professionals, this means more efficiently analyzing learner outcomes, identifying educational gaps, and streamlining reporting, allowing more time to focus on improving program impact rather than managing data.
Joint Accreditation associate member organizations periodically update their guidance for awarding profession-specific credits, with the most recent change from the Commission on Dietetic Registration (CDR) at end of 2025. These updates can affect how credit is designated, calculated, and reported for specific learner groups. Joint Accreditation accredited providers should review the latest Guidance for Profession-Specific Credits to ensure alignment with current standards across accrediting organizations. Because requirements can vary by board, staying current is essential for compliance and accurate credit designation. Regular reviews of this guidance help avoid discrepancies and supports appropriate recognition of learner participation.
DataDirect Networks (DDN), a private U.S. company that builds data storage and data intelligence infrastructure released its 2026 AI Infrastructure Report earlier this year, surveying over 600 U.S.-based IT and business leaders. Their report finds that 65% of organizations surveyed cite their AI environments are already too complex to manage, and more than half have delayed or abandoned initiatives as a result. The core issue seems to be fragmented infrastructure, siloed data, and the operational burden of stitching together disparate systems. Despite broad consensus that cloud infrastructure is critical for scaling AI, organizations continue to face infrastructure complexity and operational gaps that stall AI deployment. While this survey involved companies well outside the scope of CME/CE providers, the conclusions seem potentially relevant: as AI becomes embedded in strategy and workflows, success will depend less on tool selection and more on the underlying systems, governance, and partnerships that support sustainable use. In other words, the future advantage may belong not to those who adopt AI fastest, but to those who architect it best.
