Are you ready to lead in the Age of AI? AI Leadership Essentials.

In the age of AI, leaders are required to do three things:

• Lead machines.

• Lead people that build machines.

• Lead organizations that adopt AI.

  
  

The below scenario is a common AI challenge. However, most corporate leaders are not required to deal with the issue. The solution is completed by the engineering team with the CEO approving the approach based on the recommendation of the CIO. Though the engineering team should take the lead in this scenario, other executives that are directly impacted by the AI tool should also provide input. For example, if the model focuses on employee engagement, the CHRO should care how the learning rate impacts retention patterns. Similarly, if the neural network deals with customer attribution, the CMO should care how reducing AI training costs (i.e., FLOPs) alters the recommendations of the company's discount strategies. The problem is that to lead in the age of AI, executives must understand AI. This means going beyond prompt engineering and AI agent courses to learn how things actually work.

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Are you ready to lead in the Age of AI

How would you approach this scenario? Each option has pros and cons, which can be argued as the preferred solution.

The Machine Leadership Model suggests that AI adoption is optimized when Machine Autonomy, Trust, and AI Competencies are balanced. This equilibrium is known as the AI Innovation Frontier. This state allows organizations to implement sophisticated AI models in a manner that optimizes risk and drives AI adoption. Achieving this state is challenging. It is only possible when technical and non-technical leaders work collaboratively on the design process. This means non-technical leaders must have a fundamental understanding of how AI tools are built, which extends beyond introductory classes on prompt engineering and AI agents.

The decision on how AI models are designed matters. Yet, most corporate leaders do not have any meaningful role in the design, testing, and rollout of sophisticated AI tools.

Leading in the Age of AI requires building the competencies needed to engage in the technical conversation. Having these skills helps executives establish a trust level based on rational thinking and experience, rather than blind acceptance of the technical experts. This cannot be achieved without AI skill development and practical experience. Nowhere is this fact more important than in organizations outside of IT that rely on AI models to drive their strategies. Moving forward, corporate leaders must be machine leaders. They need to understand computer vision, robotics and autonomous vehicles, deep learning networks, language models, and supporting frameworks such as MLOPs, data governance, and cyber security.

AI Adoption is increased when Machine Autonomy, Trust, and AI Competencies are balanced.

This means diving into the details, while keeping the team's vision focused on the business. In the above scenario, the company is considering model changes to lower computational costs (i.e., FLOPs). This is a cost consideration, which should be weighed against the revenue implications if the model's performance declines as part of the change. The scenario deals with a neural network that has a high DOT product and large gradients. Engineering leaders would likely be focused on model convergence and ask a number of questions.

• Does increasing the learning rate risk overshooting and model instability?

• Is there a risk of exploding gradients?

• Did we choose the correct learning optimizer (i.e., Adam)?

• Is the learning rate causing the model to stick at saddle points due to high dimensionality?

• Have we selected the correct weight initialization scheme (i.e., Xavier)?

The problem is that to lead in the age of AI, executives must understand AI. This means going beyond prompt engineering and AI agent courses to learn how things actually work.

There are many other issues but the point is that AI leadership requires technical competencies. A CHRO, CMO or other C-Suite leader outside of IT that cannot engage in this type of conversation is essentially deferring their leadership to someone else. Trust should be based on rational thinking, skills, and experience. This is where the AI Innovation Frontier occurs. Each option in the above scenario has merit. Irrespective of the choice, the AI Leader must know why they are making a particular decision and the reasons behind it. This will help IT design more effective models that lead to stronger adoption rates throughout the company.