What surprising skills do leaders need to innovate with generative AI?

This article from the Guardian, picked up my curiosity.

“What surprising skills do leaders need to innovate with generative AI?”

So I asked my model, to come up with a mindmap summary of the main concepts of the article:

It’s a bit short on details and underlying concepts.

Asking the same question directly to the model was (not-) surprisingly more verbose and interesting.

What the GenAI model says

llama3.2

As generative AI continues to evolve and become more pervasive, leaders will need to develop a range of surprising skills to effectively innovate and leverage its potential. Here are some of the key skills:

• Embracing uncertainty and ambiguity: Generative AI is still a relatively new technology, and its applications are rapidly evolving. Leaders will need to be comfortable with uncertainty and ambiguity, rather than trying to control or predict every outcome.
• Fostering a culture of curiosity: Generative AI requires a mindset shift from traditional problem-solving approaches. Leaders should encourage their teams to explore new possibilities, experiment with novel combinations, and learn from failures.
• Facilitating human-AI collaboration: As AI generates ideas and solutions, leaders will need to enable effective collaboration between humans and machines. This may involve designing new workflows, developing skills in areas like AI literacy, or even redefining job roles.
• Managing the “AI debt” of interpretation and validation: Generative AI can produce vast amounts of data, which requires interpretation and validation to ensure accuracy and reliability. Leaders will need to manage this process, ensuring that outputs are accurate, reliable, and actionable.
• Developing skills in AI ethics and governance: As generative AI becomes more widespread, leaders must consider the ethics and implications of using these technologies. This includes addressing issues like bias, transparency, accountability, and responsible innovation.
• Embracing the value of serendipity: Generative AI can produce unexpected results, which may lead to innovative breakthroughs or entirely new ideas. Leaders should foster a culture that values and encourages serendipity, rather than dismissing novel outcomes as “unpredictable.”
• Focusing on the art of human judgment: While generative AI excels at processing large datasets, leaders will need to balance machine-generated insights with human judgment. This requires developing skills in critical thinking, creative problem-solving, and contextual understanding.
• Learning to ’listen’ to AI outputs: Leaders should develop a way to interpret and understand the output of generative AI systems, including recognizing patterns, anomalies, or new relationships that may not be immediately apparent.
• Managing the pace of change: The rapid evolution of generative AI means that leaders must adapt quickly to changing circumstances. They will need to balance innovation with stability, ensuring that their organizations remain agile and responsive in a rapidly shifting landscape.
• Cultivating an organization-wide understanding of AI fundamentals: To unlock the potential of generative AI, leaders should educate themselves and their teams about the underlying technologies, data sources, and methodologies used in these systems.

By developing these skills, leaders can create an environment that encourages innovation, collaboration, and responsible use of generative AI. This will enable organizations to harness the full potential of this technology, driving growth, improvement, and positive impact.

As you could have guess the part of this answer that picked my interest again was…

Focusing on the art of human judgment

Asking if ‘Focusing on the art of human judgment’ isn’t a bit ironic coming from a Large Language Model

Yes, that’s quite an interesting and ironic response from a Large Language Model (LLM). It highlights the tension between the capabilities of generative AI and the importance of human judgment in leadership. Here are some surprising skills leaders may need to develop or revisit when innovating with generative AI:

• Curation over creation: Leaders will need to learn to curate and refine the output generated by generative AI, rather than solely relying on its creative suggestions.
• Critical thinking: With AI generating ideas, leaders must critically evaluate these ideas, considering factors like feasibility, ethics, and potential consequences.
• Emotional intelligence: Leaders need to understand how generative AI can amplify or mask emotional biases in decision-making. They’ll need to develop empathy and social awareness to make informed choices.
• Collaboration with AI: Effective leaders will learn to collaborate with generative AI tools, understanding their strengths and limitations, and using them as augmentations rather than replacements for human judgment.
• Data literacy: As AI generates insights from vast amounts of data, leaders must become more adept at interpreting these findings, recognizing patterns, and extracting meaningful conclusions.
• Adaptability: The rapidly evolving landscape of generative AI requires leaders to be adaptable, open to new technologies, and willing to pivot when necessary.
• Transparency and accountability: Leaders will need to ensure that the use of generative AI is transparent, accountable, and subject to regular review to maintain trust with stakeholders.
• Resilience and failure handling: As AI introduces new variables into decision-making processes, leaders must develop strategies for coping with unexpected outcomes or setbacks.
• Interdisciplinary knowledge: Leaders may need to expand their knowledge beyond their traditional domain expertise to understand the capabilities and limitations of generative AI.
• Embracing ambiguity: Generative AI often generates multiple possible solutions; effective leaders will learn to navigate these ambiguities, weighing trade-offs between competing priorities.

In response to your question about irony from an LLM, you’re right that it’s a clever play on words. However, the irony lies in recognizing the limitations of generative AI and the need for human judgment, which are often seen as mutually exclusive. The LLM’s answer encourages leaders to think critically about their role in leveraging generative AI while maintaining the essential aspects of human decision-making.

Question begging

Nico: I think the part of human judgment is also in “asking the proper question”, which can be refined with the help of LLMs. What do you think ?

Llama3.2: You’re absolutely right that “asking the proper question” plays a crucial role in human judgment. This idea is closely related to the concept of “question-begging,” which refers to the problem of trying to answer a question without asking the right one first.

Conclusion

To strike a balance:

1. Use LLMs as a tool, not a crutch: Employ LLMs to assist with question refinement, but also take the time to review and refine your questions critically.
2. Encourage critical thinking: Design interactions that prompt users to think critically about their questions, rather than simply relying on LLM-generated suggestions.
3. Monitor and adjust: Regularly assess the effectiveness of using LLMs for question refinement and make adjustments as needed to ensure the technology is augmenting, not replacing, human judgment.