3 Reliable ways to build AI leadership

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Artificial intelligence (AI) is making its mark in all industries, and its impact on healthcare is inevitable. Indeed, KLAS Research and Bain & Company found that 58% of health system leaders are working on an AI adoption strategy or already have one in place.

Healthcare providers of all types have identified AI use cases across clinical, administrative, and other categories. According to KLAS and Bain, the top priority for IT investment at health systems and hospitals is revenue cycle management (RCM), which also ranks as the second-highest priority for physician groups. This category rises to the top because of its direct impact on revenue and cost as well as its near-term ROI realization.

Given the momentum behind AI for RCM and other categories, how can providers position themselves to reap the benefits? The first step is to establish effective AI leadership.

3 ways to develop AI expertise

With the pace of change and the proliferation of offerings, staying on top of AI opportunities can feel like a full-time job. Indeed, some organizations have started setting aside part-time or dedicated roles focused on AI. However, there are other options for providers to stay informed, identify opportunities, and implement AI successfully. Here are three reliable ways to develop AI expertise for provider organizations with varying goals and sizes.

1. Chief AI Officer (CAIO): Health systems are catching on to the necessity of having some form of AI leadership, with the role of Chief AI Officer growing in popularity. As AI efforts expand, more organizations will likely install a Chief AI Officer or similar executive over time.

Some of the largest health systems, including Mayo Clinic, UC Davis Health, UCSF Health, and UC San Diego Health, appointed CAIOs in 2023, responding to the dramatic pace of change with AI. According to Becker's Healthcare, Dr. Bhavik Patel, CAIO at Mayo Clinic, believes that the position will foster inter-departmental collaboration, keep organizations abreast of trends, and maximize the health system's use of resources. He explained: "While AI brings forth myriad benefits, it also carries inherent risks … a CAIO provides the necessary oversight to ensure that the implementation of AI is ethical, responsible, and in line with regulatory guidelines."

As the CAIO role is relatively new, there isn't yet broad consensus on the responsibilities or ideal profile. But some of the first systems to appoint CAIOs have chosen leaders with a unique blend of both medical and technical expertise, such as doctors with data science backgrounds.

2. AI governance group: An AI committee or other oversight group has the advantage of keeping multiple people engaged, allowing for a well-rounded approach to AI. When building a committee, organizations should include several functions and departmental voices, such as IT, security, finance, and clinical leadership.

For example, UNC Health's chief analytics officer, Rachini Ahmadi-Moosavi, told Healthcare Innovation, "When we really started to think about AI … the need for ensuring that we are doing that build responsibly and we are providing the best possible solutions to our healthcare system — whether we build it ourselves or we purchase it from a vendor — comes into question." This desire led to UNC Health developing a multi-disciplinary group whose goal is to define and operate a "responsible" AI framework.

3. In-house point person: An organization may also opt to choose an existing employee to become an expert in all things AI. In this case, the company needs to budget adequate time for the employee to build familiarity with the technology. Moreover, some investment in this expert's professional development – such as including them in conferences like the Ai4 Conference or the Enterprise Generative AI Summit to stay informed – is likely needed.

What model makes sense for your organization depends on your goals, AI mandate, organizational structure, and resource constraints. For example, a health system with lofty ambitions for AI may opt for a CAIO, as a high-profile leader is needed to drive a large new agenda. In contrast, many physician practices may start by tapping an in-house AI expert. This path requires the least investment upfront, enabling providers to gain expertise despite tough budget conditions. Organizational structure plays a role too. Top-down organizations may benefit from the centralized authority of a CAIO. In contrast, a decentralized structure may function better with an AI committee representing a broad stakeholder set.

Impact of AI competency on practices

Regardless of the model chosen, making a deliberate effort to build AI expertise provides the leadership needed to guide organizations through a fast-changing market and to secure the first few successful applications of AI. The impact of this competency appears in at least three ways: clearer agendas, improved buying processes, and company-wide education.

Given the plethora of use cases, the individual or group tapped to lead AI efforts will first aid the organization in aligning concrete priorities. Similar to how KLAS and Bain have surfaced the priorities for many providers' IT investments, the AI leader will deliver a set of focus areas to limit distraction. An administrative application such as AI coding or AI scribe will often rise to the top because of its clear ROI and non-clinical purpose.

Besides clarifying priorities, the AI expert will enable stronger buying processes and more comprehensive vendor sets. As there are no universal standards for adopting AI, this expert's attention will adapt existing procurement processes for AI consideration, clarifying decision-making criteria and expectations. Moreover, by engaging in the industry and attending conferences, this AI leader will surface the most compelling vendors and solutions available.

Finally, the individual or group dedicated to AI will help to foster company-wide education, acting as a sounding board or problem-solver for AI considerations. In tough labor markets, this visible commitment to innovation can provide an edge: Younger candidates have higher expectations for technology to improve their day-to-day experiences, as BCG notes.

Unleashing full potential

Building AI leadership is no longer a choice but a necessity. Successful adoption of AI for providers requires more than just technological know-how; it needs strategic vision, effective communication, and a culture of innovation. As AI continues to make an impact on healthcare, organizations must invest in finding the right person or group to help them unleash AI's full potential and deliver the strongest outcomes for providers and patients alike.

Austin Ward is Head of Growth at Fathom, the leader in autonomous medical coding. He oversees the company's go-to-market efforts and client analytics. He brings broad experience in health systems, technology, and data science and has worked at BCG, the Bill & Melinda Gates Foundation, and in venture capital. He holds an MBA from Stanford University, MPA from Harvard University, and BAs from the University of Chicago.