AI in Healthcare Services: Talent Strategies for the Future

Leadership StrategiesDigital TransformationHealthcareArtificial IntelligenceTechnology, Data, and Digital Officers
min Article
December 04, 2023
6 min
Leadership StrategiesDigital TransformationHealthcareArtificial IntelligenceTechnology, Data, and Digital Officers
Executive Summary
Key talent considerations for healthcare services organizations to consider as they navigate emerging AI technologies.


Generative AI has gained significant traction over the past year, necessitating universal attention and strategic consideration across all industries. Healthcare services organizations are actively exploring artificial intelligence’s (AI) potential—for example, its use in clinical decision-making and documentation improvements.

But AI-driven healthcare goes beyond chatbot doctors and AI diagnoses. Many of the transformations happen behind the scenes, with productivity and comprehension enhancements. With 83% of executives agreeing that science tech capabilities could help address health-related challenges around the world, the move to AI-driven health care will only continue to accelerate.1 Yet only 6% of health systems currently have an AI strategy in place (including Baylor, Scott, & White Health, Duke Health, Stanford Health Care, and Northwell Health to name a few).2

To effectively harness AI's transformative power, healthcare organizations and their leaders need a proactive AI talent strategy. To support healthcare leaders on this journey, Russell Reynolds Associates delved into the key talent considerations healthcare leaders should prioritize as they navigate this groundbreaking technology.


AI’s emerging role in revolutionizing healthcare services talent needs

AI technical skills in the healthcare workforce:

Healthcare services is not a historically innovative industry. As such, it is essential for its leaders to identify the AI-related skill sets that the next generation will need to keep pace as the technology evolves. This includes hiring professionals with expertise in machine learning, data analytics, natural language processing, and AI model development.

Continuous learning and development:

AI is a rapidly evolving field, and healthcare leaders must encourage a culture of continuous learning on the topic. This involves providing opportunities for employees to stay updated with the latest advancements in AI and offering training programs to enhance their skills. Organizations that offer upskilling and even reskilling initiatives5  will effectively bridge the divide between medical expertise and AI proficiency.

Change management and organizational culture:

The successful implementation of AI technologies in healthcare requires a cultural transformation within healthcare organizations. To achieve this, leaders should consider allocating resources towards change management professionals. These experts are instrumental in shepherding employees through the transitional phase, adept at addressing concerns, and cultivating a constructive mindset regarding the integration of AI solutions. Such an investment not only streamlines the adoption process but also fosters an organizational environment that is receptive and adaptable to technological advancements.


What does the AI revolution mean for healthcare services talent needs?

Leadership with AI understanding:

Healthcare organizations need leaders who understand the potential and limitations of AI technologies. Leaders who can communicate AI's strategic value, set realistic expectations, and guide implementation play a pivotal role in the successful adoption of AI.

Interdisciplinary collaboration:

Partnership between technical experts and healthcare professionals is critical for successful AI implementations in healthcare. Data scientists, AI engineers, clinicians, and administrators are all essential for crafting AI solutions that will blend clinical relevance and technical expertise.

Furthermore, given AI's heavy reliance on data, healthcare organizations must also prioritize the acquisition of data science and analytics expertise. These experts are vital in cleansing, processing, and interpreting complex healthcare data, ultimately transforming it into actionable insights that empower informed decision-making. Leaders are encouraged to invest in data science talent to harness the full potential of data-driven improvements in healthcare practices and outcomes.

Ethics and regulatory expertise & diversity:

Healthcare leaders must prioritize hiring professionals who understand the ethical implications and regulatory requirements associated with AI technologies in healthcare. Given AI’s potential risks, including misdiagnoses and patient data breaches, leaders need to understand its implications in an already high-risk environment (e.g., patient diagnoses.3) This expertise ensures that AI applications adhere to patient privacy, data security, and regulatory guidelines.

Additionally, ensuring diversity in these AI teams is crucial to avoiding biases in AI algorithms and applications.4 Diverse perspectives within these teams are a safeguard against the inadvertent introduction of biases into AI algorithms and applications. Therefore, healthcare leaders should continue focusing on diversifying their workforce and fostering an inclusive work environment. This is especially critical, given the complex nuances of healthcare delivery that vary across different socio-economic strata.

Governance and Board Composition:

As demands for AI increase, healthcare services organizations must also consider their board composition and governance strategy to accommodate shifting needs. Establishing dedicated AI-focused board directors, akin to scientific advisory boards in biotech, is imperative to guide strategic decisions, foster innovation, and ensure alignment with cutting-edge technological advancements in the rapidly evolving healthcare services landscape.


Generative AI: What Boards Need to Know and Do (or Not Do) About It

Generative AI: What Boards Need to Know and Do (or Not Do) About It

Where will AI talent sit within healthcare’s organizational structure?

As the industry adapts, there is no right answer to “who owns our AI strategy?” In canvassing the market, our research found that AI ownership is typically split amongst three main groups of healthcare leaders. We observed three nascent constructs in healthcare services, outlined below (see Figure 1):

1) Existing Non-Tech Roles: AI is embedded in the Chief Medical Officer purview

2) Existing Tech Roles: AI is split into three groups; Chief Technology / Information Officer, Chief Digital Analytics Officer, Chief Digital Officer

3) Emerging New Role: AI strategy is coordinated under a Chief AI Officer


Figure 1: Potential AI organizational structures across healthcare organizations

Potential AI organizational structures across healthcare organizations

Source: Proprietary research on organizational structures, Russell Reynolds Associates, 2023.


The future of AI healthcare services talent

The integration of emerging AI technologies into healthcare services offers unprecedented opportunities for improving patient care, operational efficiency, and medical research. To realize these benefits, healthcare leaders must proactively address talent considerations. By recruiting and developing a workforce with the right skill sets, fostering interdisciplinary collaboration, and prioritizing ethics and regulatory expertise, healthcare organizations can navigate the complexities of AI integration successfully. With the right talent in place, healthcare leaders can lead their organizations toward a future where AI-driven innovations enhance the quality of care and positively impact patient outcomes.




Sarah Eames leads the Russell Reynolds Associates’ Healthcare Services practice. She is based in New York.
Amy Saddington is a senior member of Russell Reynolds Associates’ Healthcare Services and Health Tech Practices. She is based in Dallas.
Olivia Floto is a member of Russell Reynolds Associates’ Healthcare Services knowledge team. She is based in Chicago.
Tiffany Yam is a member of Russell Reynolds Associates’ Healthcare Services knowledge team. She is based in San Francisco.


1 Accenture. March 2023. When Atoms meet Bits – the foundations of our new reality. When Atoms meet Bits. The foundations of our new reality | Accenture
2 Becker’s Hospital Review. September 2023. Why health systems aren’t creating gen AI strategies. Why health systems aren't creating gen AI strategies (
3 BMC Medical Informatics and Decision Making. January 2023. Ethics and governance of trustworthy medical artificial intelligence.
4 Yale School of Medicine. 2021. Hard choices: AI in health care. Hard choices: AI in health care < Yale School of Medicine
5 Harvard Business Review. October 2023. Reskilling in the Age of AI. Reskilling in the Age of AI (