Core Insights - Many boards are approving AI strategies without clear visibility into the effectiveness of underlying controls, exposing CFOs to scrutiny from regulators, auditors, or investors [1] - Health care organizations face significant challenges in AI governance and controls, with many lacking essential capabilities [4][5] Industry Challenges - 53% of organizations cannot remove personal data from AI models once used, leading to long-term compliance risks under GDPR and other regulations [2] - 63% of organizations cannot enforce purpose limitations on AI agents, 60% lack kill-switch capabilities, and 72% do not have a software bill of materials for AI models [3] - The government sector faces the steepest challenges due to legacy systems, while health care is particularly weak in AI governance [4] Health Care Sector Insights - Over 80% of health care respondents have no API agents planned, reflecting a conservative approach to AI spending [5] - Health care has lagged behind other industries in adopting advanced technologies due to economic constraints, with operating margins typically around 2-3% [6][7] - AI is increasingly viewed as essential for financial sustainability in health care, with leaders recognizing its potential to improve operational efficiency [6] CFO Perspectives - Health care CFOs are under pressure to balance AI investment with limited resources, making technology decisions feel critical rather than experimental [7] - Quantifying AI's return on investment is challenging, complicating the decision-making process for CFOs [7][8] - CFOs are being asked to approve significant technology investments without the necessary internal expertise to evaluate or manage them [8]