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.
As organizations accelerate AI adoption, a new McKinsey analysis argues that organizations should measure AI’s economic value, not simply adoption, by comparing the current cost of completing tasks with the cost of AI-enabled work. The analysis introduces an “AI value curve” that evaluates the economics of AI-enabled work, including the costs of human review, validation, and rework, and finds opportunities in augmenting high-skilled professionals. The analysis finds particularly strong economics in augmenting highly skilled, higher-wage work, with substantial opportunities beyond technology and financial services, including healthcare and education. Because labor costs vary geographically, the same AI application may generate savings in one market but offer little or no cost advantage in another.
ACCME’s Content Validity for AI-Enabled Clinical Learning guidance, released September 22, 2026, addresses activities in which AI dynamically generates clinical answers, recommendations, or guidance developed solely by a large language model (LLM) or an AI agent. Because each response is created during use, the content may differ by learner and cannot always be reviewed before delivery. Standard 1 still applies: providers remain responsible for ensuring that content is valid. ACCME may therefore require an independent assessment during a complaint or inquiry, reaccreditation, or an initial-accreditation application involving this activity type. The review examines the specific activity and configured AI system for accuracy, completeness, scientific validity, balance, citation problems, omissions, hallucinations, outdated recommendations, and commercial bias. The provider will be responsible for arranging and paying for the assessment, whose findings inform ACCME’s decision but do not certify the AI platform.
In its October 23, 2026, National Advocacy Update, the American Medical Association (AMA) supports the FDA’s proposed risk-based approach to regulating generative AI–enabled medical devices while emphasizing that regulatory flexibility must not compromise safety or effectiveness. The AMA underscores the importance of physician involvement in evaluating device performance but cautions that clinician oversight should not substitute for appropriate regulatory safeguards. Importantly, manufacturers must remain accountable for their products rather than shifting responsibility for device performance to clinicians. While the AMA’s position focuses on medical devices, its emphasis on oversight and accountability offers a relevant parallel for the CME/CE enterprise. As AI becomes increasingly integrated into educational planning, content development, and operations, CME/CE organizations need clear governance frameworks that define accountability, establish appropriate human oversight, safeguard educational integrity, and support compliance with accreditation standards for content validity.
