Health leaders are increasingly embracing Artificial Intelligence (AI) to enhance patient care, streamline operations, and support healthcare providers. But are they truly leading an AI-capable organization—one that can harness AI’s full potential and long-term value while mitigating its risks? An AI-capable organization possesses the necessary infrastructure, governance, technical expertise, and cultural mindset to effectively develop, deploy, and manage AI systems. It ensures the safe, ethical, and strategic use of AI across its operations, aligning AI adoption with organizational priorities. This article outlines the essential components of an AI-capable organization, provides a framework for assessing AI maturity, and introduces a risk-proportionate approach to building an AI tool pipeline for healthcare delivery. We explore key leadership considerations, including the decision to build or buy AI solutions, and conclude with special considerations, including the rise of Bring Your Own AI and its implications for governance and oversight.
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References
1.
BajwaJMunirUNoriAWilliamsB. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi:10.7861/fhj.2021-0095
SriharanASekerciogluNMitchellC, et al.Leadership for AI transformation in health care organization: scoping review. J Med Internet Res. 2024;26:e54556. doi:10.2196/54556
4.
NovakLLRussellRGGarveyK, et al.Clinical use of artificial intelligence requires AI-capable organizations. JAMIA Open. 2023;6(2):ooad028. doi:10.1093/jamiaopen/ooad028
5.
RussellRGLovettNLPatelM, et al.Competencies for the use of artificial intelligence-based tools by health care professionals. Acad Med. 2023;98(3):348-356. doi:10.1097/ACM.0000000000004963
DixitAQuagliettaJGaultonC. Preparing for the future: how organizations can prepare boards, leaders, and risk managers for artificial intelligence. Healthc Manage Forum. 2021;34(6):346-352. doi:10.1177/08404704211037995
20.
BarranceEKazimEHilliardATrengoveMZannoneSKoshiyamaA. Overview and commentary of the CDEI’s extended roadmap to an effective AI assurance ecosystem. Front Artif Intell. 2022;5:932358. doi:10.3389/frai.2022.932358
21.
CrossJLChomaMAOnofreyJA. Bias in medical AI: implications for clinical decision-making. PLOS Digit Health. 2024;3(11):e0000651.
22.
DavisSEEmbíPJMathenyME. Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance. J Am Med Inf Assoc. 2024;31(5):1195-1198.
23.
FengJPhillipsRVMalenicaI, et al.Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digit Med. 2022;5(1):66.
DurlachPFournierRGottlichJ, et al.The AI maturity roadmap: A framework for effective and sustainable AI in health care. NEJM AI Sponsored. 2024. doi:10.1056/AIS2400177. https://ai.nejm.org/doi/abs/10.1056/AIS2400177. Accessed Sept 11, 2025.
27.
WilsonASaeedHPringleCEleftheriouIBromileyPABrassA. Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment. BMJ Health Care Inform. 2021;28(1):e100323.
Declaration on the Rights of Indigenous Peoples Act, British Columbia. https://www.bcdripa.org. Accessed Aug 19, 2025.
30.
RanjbarAMorkEWRavnJ, et al.Managing risk and quality of AI in healthcare: are hospitals ready for implementation?Risk Manag Healthc Policy. 2024;17:877-882.
31.
YasinZMBatesDWShahN. A Five-Level Path To AI Automation In Health Care. Health Affairs Forefront. June 20, 2024. DOI: 10.1377/forefront.20240617.335248. https://ai.nejm.org/doi/abs/10.1056/AI-S2400177. Accessed Sept 11, 2025.
32.
MelloMMGuhaN. Understanding liability risk from using health care artificial intelligence tools. N Engl J Med. 2024;390(3):271-278. doi:10.1056/NEJMhle2308901
National Institute of Standards and Technology (NIST) NIST Trustworthy and Responsible AI NIST AI 600-1 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf. Accessed Feb 21, 2024.
