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Evolving lessons on AI governance, innovation, people and risk

By John M. Bremen | December 5, 2024

Is AI better at enhancing the productivity of lower skilled or higher skilled workers?
Employee Experience|ESG and Sustainability|Ukupne nagrade |Benessere integrato
Future of Work|Artificial Intelligence

At the annual Directors & Boards Character of the Corporation summit in New York last month, a panel of corporate directors discussed AI and its impact on risk and people. They recognized the vastness and complexity of the topic as well as how quickly it is changing. Effective leaders stay on top of new learnings and incorporate them into their practices.

For example, a study published this month by Aiden Toner-Rodgers of MIT, Artificial Intelligence, Scientific Discovery, and Product Innovation, challenges earlier research that suggested AI is most helpful to employees who use basic skills and perform basic tasks in their jobs. The new MIT paper shows the most skilled scientists and innovators benefitted the most from AI – doubling their productivity – while lower-skilled staff did not experience similar gains.

This appears counter to previous studies. For example, a 2023 National Bureau of Economic Research paper by Erik Brynjolfsson et al. showed that low-skilled call-center workers demonstrated significant improvements when using AI, while higher-skilled workers did not demonstrate similar benefits. A 2023 Boston Consulting Group (BCG) study showed similar findings, as lower-skilled consultants increased their performance by 43% when using AI augmentation tools in comparison to 17% for higher-skilled consultants.

Why the difference in results? As it turns out, the benefit of AI augmentation varies depending upon the kind of task and the employee’s level of skills and proficiency:

  • Routine tasks: AI tools tend to enhance the performance of lower-skilled employees more than they do for higher-skilled employees.
  • Tasks involving more advanced human judgment (such as innovation, research and development, and advanced programming): AI tools tend to enhance the performance of higher-skilled employees more than lower-skilled employees.

Further research published by BCG in 2023 and 2024 was consistent with this finding, as was a report by Evercore ISI and Professor David Shrier showing AI impacts jobs differently based on the type of work and who is doing that work.

How can boards and senior management teams use the knowledge gained in these studies to inform AI governance, risk management and decision making?

Effective leaders take the following six actions:

  1. 01

    Vary governance requirements by situation – one size does not fit all

    Effective leaders know AI tools require different governance protocols for different circumstances. For example, management actions and board oversight of AI requirements and risks might be different for the use of routine, administrative AI tools than for AI tools used for more complex, creative or innovative tasks.

  2. 02

    Stay on top of quickly changing tools, learnings and risks

    As the research shows, the AI space – as well as its tools, learnings and use cases – are changing rapidly. Effective leaders stay on top of developments and know what is true in 2024 may not be true in 2025. They continuously evaluate and adapt AI implementation to identify and mitigate risks (e.g., disparities, security, quality, homogeneity) and maximize value.

  3. 03

    Understand the risks of different applications

    The research highlights the risks of different uses for AI. The MIT study showed that specialized AI tools foster radical innovation at the technical level within a domain-specific scope, but also risk narrowing human roles and diversity of thought. The BCG study showed AI tools improved individual creativity, but led to homogeneous outputs, reducing group-level variety of ideas. Effective management teams work to ensure AI tools are fit for purpose, and boards provide oversight to ensure appropriate risks are being managed and mitigated.

  4. 04

    Ensure workflows and skill requirements are designed thoughtfully

    The research shows that equipping workers with the same tools for different skill levels and purposes can be counterproductive, and that overreliance on AI for idea generation risks diminishing workers’ autonomy and critical thinking. Effective leaders provide the right tools, upskill some roles and down skill others. They balance AI productivity with appropriate opportunities for human creativity, exploration and decision making.

  5. 05

    Monitor employee engagement

    The MIT study reported a decrease in work satisfaction among 82% of the R&D staff using AI tools. One scientist said, "While I was impressed by the performance of the [AI tool]… I couldn't help feeling much of my education is now worthless. This is not what I was trained to do.” The most common complaint was skill underutilization (73%), followed by tasks becoming less creative and more repetitive (53%). Effective management teams and boards monitor the impact AI has on workforce engagement, wellbeing and productivity.

  6. 06

    Ensure users are in the loop

    The research demonstrates that human users continue to be necessary to maximize the effectiveness of AI tools. Effective senior management teams design processes to ensure the right people are involved at appropriate points in the process, and effective boards ensure appropriate protocols and processes are followed.

Together, the various studies highlight the transformative potential of AI while underscoring the critical role of senior management and boards in shaping implementation to maximize productivity, creativity and impact.

A version of this article originally appeared on Forbes on November 27, 2024.

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