HealthTech Leaders: Are You Prepared to Handle the Implications of Generative AI?

Technology and InnovationIndustry TrendsDigital TransformationHealthcareTechnologyTechnology, Data, and Digital Officers
min Article
Noël Auguston
October 16, 2023
5 min
Technology and InnovationIndustry TrendsDigital TransformationHealthcareTechnologyTechnology, Data, and Digital Officers
Executive Summary
As generative AI gains traction across the healthtech landscape, leaders must consider how it impacts their talent strategies.


Though artificial intelligence (AI) has long been used in healthcare to synthesize data in clinical and non-clinical settings, ChatGPT, a large language model (LLM) chatbot has taken the world—and healthtech industry—by storm.  According to September 2023 McKinsey research, generative AI could add the equivalent of $2.6 trillion to $4.4 trillion in annual economic benefits across 63 use cases. Generative AI is poised to make huge impacts in all areas of healthtech, from innovative drug discovery to efficiencies in patient care to reducing errors in back-end office procedures.

Russell Reynolds Associates spoke with several technology and product leaders from innovative healthtech companies to learn more about the immediate generative AI implementations and talent strategies their organizations are considering to ensure success.

While it’s impossible to know the extent of generative AI’s implications yet, interviewees agreed upon one thing: organizations across the healthtech ecosystem are working toward, if not already succeeding in, broadening their offerings to incorporate generative AI.


How generative AI will change healthtech

Experts in the healthtech industry agree that generative AI has tremendous potential in both clinical and nonclinical settings. However, most acknowledge that immediate uses will occur within nonclinical settings, improving back-office processes for payers and providers alike. The healthcare industry typically isn’t a first-mover on innovative technologies, due to the regulatory nature of the industry; clinical settings rightly require more testing, compliance, and approvals before LLMs can be fully integrated.

The below chart shows some of the common generative AI use cases in healthtech, and their immediate impact and technical complexity involved to implement.


Once leaders determine which use cases will be most relevant to their organization, they must then adapt their talent strategy and requirements around generative AI.


Five Talent and Organizational Observations of Generative AI in Healthtech

Generative AI is still new, and leaders are only beginning to uncover its long-term business impacts. As such, it’s too early to differentiate talent trends specific to the healthcare industry; many behaviors and requirements mirror what we observe across organizations in all sectors. However, here are five ways that we’ve seen generative AI impact healthtech’s organizational and talent landscape so far:

1. Generative AI implementations are unlikely to lead to new C-suite roles: We have not seen new C-suite roles solely dedicated to generative AI in healthtech yet. Instead, companies are harnessing innovation power within their existing leadership structures by augmenting desired qualifications. CEOs are tasking chief technology officers or chief data and analytics officers with leading the charge on integrating generative AI into their organizations. However, as new technology executives are hired in clinical settings, educational and prior career requirements will shift toward a blend of technology and science.

2. Companies outsource LLM development: Rather than building LLMs in-house, we are seeing healthtech companies partner with tech giants like Microsoft and Amazon to tailor existing models to their own organizational needs. These partnerships are critical to implementing generative AI use cases at speed. A powerful example of this collaboration is exemplified by EHR titan Epic Systems and Microsoft’s Azure OpenAI Service, who announced their partnership in April 2023. Microsoft’s services will enable efficiencies in clinical workflow through suggested text and summaries, increase medical coding and billing accuracy, and fill gaps in clinical data sets. Eventually, competitive advantage from generative AI may come from internally-built LLMs which will, of course, require specific skills from engineering talent.

3. Secure data pipelines for collection and aggregation of data are critical: As companies install LLMs, they will need large volumes of data to train their generative AI models, as well as enhanced security measures to protect said data. While data privacy is already an important tenet of any healthcare organization, this new use of patient data may require additional safeguards and training for those who may have not previously dealt with sensitive data. Leaders may reposition technology teams within the organization, and strive for an organizational structure that promotes access to and collaboration with security leaders. Increased communication around security will foster proactive problem-solving, manage risk, and prevent mistakes.

4. Business logic must work hand-in-hand with advances in technology: In order to yield the desired output from generative AI platforms, users must ask the right questions to guide the tool. Prompt engineering tasks will require technologists to develop their business logic skills. In healthtech specifically, prompt engineers will need to develop an understanding of healthcare’s business nuances. Those working on back-office, administrative applications will need to have a clear understanding of medical coding and billing processes. Prompt engineers working in clinical and research settings will need to have a scientific background, and may have a degree in computational biology or computational chemistry. The mastery of generative AI will require skillsets that blur the lines between technology, healthcare acumen, business expertise, and leadership.

5. Executive leadership must be able to manage the changes that generative AI will bring, while also preserving their existing culture: In an industry as personal as healthtech, engaged and articulate commercial leaders will be necessary for external consumer adoption and acceptance of generative AI. From electronic health records (EHRs), to wearables and sensors, to clinical trials, organizations are collecting and analyzing patient data in greater quantities than ever before. Patients need to trust that their data will be used and guarded responsibly, while still feeling the human connection with their care providers. Internally, for employees to embrace new innovations, leaders need to consider how to wield these new tools ethically and efficiently, investing in proper training around how generative AI will augment and accelerate their work. Human-centered leadership will be integral to AI’s effectiveness and long-term acceptance.

As generative AI remains at the top of healthtech leaders’ minds, they must be prepared to support their people through upcoming transitions, augmenting their executive teams and frontline employees with the right skillsets to succeed. By demonstrating agility, collaboration, and flexibility around role responsibilities, leaders can build trust and communication across their organizations as they explore this new era of intelligence.




Hope Cummins is a member of Russell Reynolds Associates’ Healthcare Knowledge team. She is based in San Francisco.
Noël Auguston leads Russell Reynolds Associates’ Healthcare Technology practice. She is based in Boston.