Navigating the Generative AI Revolution: Five Key Focus Areas for Leading into the Future

Leadership StrategiesTechnology and InnovationLeadershipArtificial IntelligenceTechnology, Data, and Digital OfficersBoard Effectiveness
min Article
August 21, 2023
3 min
Leadership StrategiesTechnology and InnovationLeadershipArtificial IntelligenceTechnology, Data, and Digital OfficersBoard Effectiveness
When implementing generative AI, consider its impacts on culture, leadership, organization model, commercial strategy, and risk management.

Generative AI refers to artificial intelligence that enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data.

The introduction of ChatGPT and subsequent surge in interest around large language models (LLMs) and generative AI has thrust technology conversation into the spotlight for both boards and leaders. While AI adoption to date has varied among organizations, generative AI has brought the barrier of AI adoption to an extraordinarily reachable place. Organizations, regardless of their size and industry, are actively exploring various use cases and leveraging AI's potential to gain a competitive edge.

As with any epochal transformation, generative AI’s emergence also brings major potential consequences. To seize opportunities and avoid the corresponding pitfalls, Russell Reynolds Associates identified five key areas to help CEOs, boards, and senior technology leaders navigate the impact of generative AI on their organizations:


Talent and culture      Leadership      Organizational structure       Commercial strategies     Risk management



Talent and culture: investing in a transformative AI mindset

Talent and culture  

AI has the potential to revolutionize the talent landscape. Roles may be eliminated, enhanced, or newly created due to this new paradigm. In fact, Goldman Sachs estimates that two-thirds of current jobs in the U.S. and Europe could be affected by generative AI.1 But while there's been considerable concern about the number of jobs generative AI might eliminate, many organizations are taking a more nuanced approach.

Rather than eliminating specific functions, a "cost out, value in" philosophy has led many organizations to repurpose certain groups to areas where they can make a bigger impact. Additionally, AI’s streamlining abilities are prompting firms to consider how the technology might free up executives' time to focus on higher-value tasks. Moreover, the adoption of generative AI will elevate the importance of architecture, data science, AI ethics, and risk management roles to support AI implementation and utilization.

As a result, organizations are reevaluating their AI adoption levels, exploring available third-party tools and integration possibilities, and strategically positioning themselves for future success. Crucially, they are also facing the mammoth task of upskilling their entire workforce to meet the demands of the AI-driven future.

With rapid change comes disruption. From a talent perspective, this may lead to resistance towards AI, rather than viewing it as a tool for enablement. As organizations grow increasingly technology-oriented, leaders can cultivate a culture of innovation and transformation by establishing two seemingly conflicting approaches:

  • A test-and-learn mindset that allows for quick iterations and first-mover advantages
  • A slower, deliberate approach to integrating AI into existing processes

Both mindsets are crucial as organizations embrace the core tenants of transformation, which include:

Systems thinking, empathy, curiosity, versatility, adaptability, continuous learning

Industry leaders have already embraced top AI talent. In 2018, JPMorgan recruited Manuela Veloso, former head of the machine learning department at Carnegie Mellon University, to lead its AI research. Since then, JPMorgan has recruited other AI experts to strengthen its data infrastructure and support machine learning capabilities. As of June 2023, the bank ranks #1 in the Evident AI Index—the first global standard benchmark of AI maturity for the banking sector.

Key talent and culture questions to consider:

• Is there a sufficient level of AI understanding at the right levels of the organization?

• Which roles on our leadership team will experience the greatest impact due to the introduction of generative AI?

• What skills should we develop to gain a competitive advantage from AI?

• Have we cultivated a culture of innovation and transformation? If so, how can we foster its continued growth?


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Leadership: choosing the right person to lead the AI charge


While culture and talent strategy is fundamental to an organization’s AI strategy, determining who will lead the transformation—and how they’ll do it—is crucial to success.

In some cases, organizations might opt for a chief AI officer (CAIO). Similar to the boon of chief digital officers (CDO) who led the 2010s digital transformations, the CAIO can bridge the gap between business objectives, customer engagement, and technology. Alternatively, chief data & analytics officers may be elevated to the leadership team, as their roles become more comprehensive and transformative, with direct responsibility for driving top-line growth. In other cases, organizations may incorporate AI efforts under the existing top technology officer, such as the CIO, CTO, or CDO. This convergent model has the appeal of bringing a consolidated strategic view across all of technology, data analytics, and AI to the executive leadership team.

Boards also bring valuable insights to strategic decisions concerning technology adoption, innovation, and competitive positioning. Over the past five years, more non-executive boards have brought in technology experts, originally favoring GMs from the tech industry, while more recently seeking out functional technology experts and specific leaders in cyber security, data, or AI, with the latter examples including:

  • Fei-Fei Li: Board member at Twitter, Sequoia Professor of Computer Science, and former Chief Scientist of AI/ML for Google
  • Bill Stasior: Board member at Avellino Lab, former Corporate Vice President of Technology for Microsoft, and former VP of AI/Siri for Apple
  • Richard Benjamins: Board member at CDP-Europe, Chief Responsible AI Officer at Telefonica
  • Xiaoqun Clever: Board member at BHP, Amadeus, and Infineon Technologies, formerly holding the positions of Chief Technology & Data Officer at Ringier AG, Chief Operating Officer of Technology and Innovation at SAP, and President of SAP Labs China.

In cases where adding an AI expert to the board isn't an immediate option due to capacity constraints, boards can leverage existing directors who can pose relevant AI-related questions to internal experts.

Key leadership questions to consider:

• Who is the overall leader responsible for overseeing and guiding the organization's AI initiatives?

• Who is able to challenge the technology and AI strategy at the board level?

• Is the existing leadership team sufficiently tech-savvy, understanding how to optimize AI investments?

• Do we have a leader who understands the technology, digital, and data functions that can commercialize AI capability and data assets?

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Organizational structure: using AI to break down silos


With AI’s potentially dramatic impacts on leadership and talent, your organizational structure likely needs reviewing. Previously manual tasks can be automated, and elements of shared services, reporting, and analysis will be ripe for AI disruption, requiring organizations to reevaluate their operating models and structures.

We envisage the first step on the path will focus on two key areas:

  • Embedding R&D capabilities in all areas of business: While dedicated R&D teams will continue to create new products, every function within the company can have its own R&D capability, leveraging AI tools to improve their respective workflows and processes.

  • Aligning AI across the organization: As technology teams—including data scientists, AI engineers, and machine learning experts—become integral parts of the workforce, a more hybrid business and technology leader will begin to emerge. The best structures will facilitate collaboration between business and technology, breaking down organizational silos. This organizational fluidity will enable knowledge-sharing, foster a culture of interdisciplinary problem-solving, and democratize data and predictive capabilities. Ideally, this will empower the whole organization to contribute to strategic initiatives and problem-solving, leading to a more inclusive structure.

Key organizational structure questions to consider:

• How does the adoption of generative AI impact our current organizational structure?

• Should AI and data teams sit within the technology function or be closer to business unit leaders?

• How will business units and corporate functions work intimately with technology and AI teams?

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Commercial strategies: new, AI-driven opportunities


As AI becomes increasingly integrated into various tools and software, it opens up new avenues for product revenue generation. By leveraging internal data and applying advanced algorithms, organizations have a unique opportunity to uncover valuable insights, identify trends, and develop innovative offerings that differentiate them from competitors.

For example, the NFL partnered with Amazon Web Services (AWS) to create the Digital Athlete, an advanced AI model designed to recreate NFL players in a virtual environment. This cutting-edge technology utilizes extensive data from the NFL, including player activity, equipment choices, speeds, weather conditions, and extensive video footage to enhance the league's understanding of injuries and improve player safety. In doing so, the NFL not only secures cost savings but also safeguards its fan base, viewership, and potential sponsorships.

In addition to creating new products, AI has the potential to revolutionize the customer experience. By harnessing AI to analyze vast amounts of customer data, organizations can better understand individual customer preferences and behaviors, delivering hyper-personalized and tailored experiences. This, in turn, allows organizations to provide more relevant products, services, and recommendations.

Key commercial strategies questions to consider:

• What potential revenue streams can we generate by commercializing our AI capability and data assets?

• Do we have the necessary infrastructure and resources to support commercialization efforts?

• How can AI help us better understand customer preferences, behaviors, and needs?

• How can AI enhance personalization in our marketing efforts?

• As we embrace AI technologies, how does our commercial strategy need to change?

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Risk management: centering ethics and organizational values in AI systems


The complicated convergence of data and privacy regulations, cyberattacks, and governance issues is expected to grow even more challenging. By increasing their reliance on AI systems, organizations face potential vulnerabilities that hackers may exploit. Additionally, handling vast amounts of personal information introduces significant risk; if mismanaged, organizations face substantial financial and reputational consequences. Implementing best practices is not a hindrance to innovation but a proactive approach to set necessary guardrails to innovate within.

Additionally, organizations need to consider AI’s ethical implications. AI systems must be designed and implemented with organizational purpose and values in mind, and clear standards for ongoing AI use are crucial. For example, companies like Microsoft and Google have embraced responsible AI practices by formulating guiding principles that prioritize fairness, reliability, privacy, security, inclusiveness, transparency, and accountability. However, despite these efforts, the potential risks stemming from AI’s black box nature persist and must be addressed to ensure effective risk management and regulatory compliance.

Managing risk is a complex topic, and even groundbreaking technology organizations are still finding their footing. Organizations need to assess their level of comfort with the risks associated with AI and make informed decisions about how and where AI is deployed within their operations.

Key risk management questions to consider:

• What is our current risk tolerance?

• Does the company have adequate technology, legal, and compliance functions to mitigate risks associated with generative AI?

• What privacy and ethical considerations should be addressed when implementing generative AI?

• What legal and regulatory considerations are involved in commercializing data and AI?

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What’s next?

To best harness the power of generative AI, organizations first need to understand their current digital capabilities. In doing so, they can develop a strategic AI roadmap tailored to their unique needs, values, and objectives. By considering the questions above, organizations can successfully adopt and ethically integrate AI into their future.

To learn how Russell Reynolds Associates can help you harness AI and digital capabilities, please visit our Technology, Data & Digital webpage.



  • Fawad Bajwa co-leads Russell Reynolds Associates’ AI, Analytics & Data Practice globally. He is based in Toronto and New York. 
  • George Head leads Russell Reynolds Associates’ Technology Officers Knowledge team. He is based in London.
  • Tristan Jervis leads Russell Reynolds Associates’ Technology Officers capabilities. He is based in London.
  • Suya Xiong is a member of the Technology Officers' knowledge team. She is based in Boston.


1 Joseph Briggs, Devesh Kodnani, “Global Economics Analyst: The Potentially Large Effects of Artificial Intelligence on Economic Growth (Briggs/Kodnani)”, Goldman Sachs, March 2023.