5 Key Steps to Navigating the Generative AI Revolution

Generative AI refers to artificial intelligence that enables users to quickly generate new content based on a variety of inputs. It is changing how business leaders go about their work. A new report from Russell Reynolds Associates explores key areas to help CEOs, boards, and senior technology leaders guide the impact of AI on their organizations.

January 23, 2024 – 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, according to a new report from Russell  Reynolds Associates. Organizations, regardless of their size and industry, are actively exploring various use cases and leveraging AI’s potential to gain a competitive edge, the study said.

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:

1. Talent and culture: investing in a transformative AI mindset.

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.

“But while there’s been considerable concern about the number of jobs generative AI might eliminate, many organizations are taking a more nuanced approach,” the Russell Reynolds report said. “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.”

Related: The Emergence of the Chief Generative AI Officer

As a result, the report explains that 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,” Russell Reynolds said. “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, the report says that 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, and continuous learning,” the Russell Reynolds report said. “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 number one in the Evident AI Index—the first global standard benchmark of AI maturity for the banking sector.”

2. 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, Russell Reynolds notes that 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,” the report said. “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.”

Using Analytics for Executive Search
As the competition for highly skilled talent intensifies, companies are turning to big data and analytics to gain a competitive edge. With the help of these tools, businesses can now identify top candidates with precision and speed, reducing the time and resources required to fill executive-level positions. By measuring data on the success of previous hires, companies can improve the quality of future hires by identifying key attributes and characteristics that have led to success in the past. Predictive analytics is also becoming increasingly popular in executive recruitment. By analyzing data on past hiring trends, companies are building predictive models that can identify potential future trends and help them make more informed decisions about recruitment strategies. These models can provide valuable insights into the types of candidates that are most likely to succeed in particular roles and within certain industries.

Boards also bring valuable insights to strategic decisions concerning technology adoption, innovation, and competitive positioning, according to the Russell Reynolds report. “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,” it said. “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.”

3. Organizational structure: using AI to break down silos.

The Russell Reynolds report explains that 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.

Related: How HR Can Harness AI to Advance DE&I

Russell Reynolds envisages 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.

4. 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, the Russell Reynolds report notes. The firm says that 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,” the Russell Reynolds study said. “This, in turn, allows organizations to provide more relevant products, services, and recommendations.”

5. 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, Russell Renolds explains. “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,” the search firm said. “Implementing best practices is not a hindrance to innovation but a proactive approach to set necessary guardrails to innovate within.”

Additionally, Russell Reynolds notes that 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, the firm says that 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,” the report concludes. “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.”

Related: How to Use AI to Stand Out to Executive Search Consultants

Contributed by Scott A. Scanlon, Editor-in-Chief; Dale M. Zupsansky, Executive Editor; Lily Fauver, Senior Editor – Hunt Scanlon Media

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