Assessing AI Skills in Leadership: Why it Has Become Critical for Business Leaders

Every organization today is racing to harness the potential of AI transformation. AI drives efficiency, innovation, and customer impact. Yet one critical factor often determines whether those ambitions succeed or stall: leadership readiness. 

The differentiator today isn’t technology itself, but whether leaders themselves have the ability to set direction, apply, and champion AI. This is why assessing for AI skills has become so critical—and is no longer a niche exercise. It is now foundational to ensure strategic alignment, operational readiness, and cultural adaptability in the face of rapid technological change. 

AI is no longer a buzzword; it is fundamentally transforming industries, reshaping business models, and altering customer expectations. When it comes to AI for business leaders, the challenge is twofold: grasping the possibilities of AI while mobilizing their organizations fast.

• Misallocating resources. 
• Overlooking critical opportunities for innovation and competitiveness. 
• Undermining cultural adoption by failing to model AI-driven thinking.

• Drive innovation within their organizations. 
• Promote collaboration across different functions. 
• Build resilient organizations that are prepared for the future. 

• Identify capability gaps across different tiers of leadership. 
• Align leadership development initiatives with strategic goals. 
• Equip both business and technical leaders to guide their teams through transformation.  
• Embed AI thinking into the organizational culture and decision-making.

Done well, the assessment of AI skills for leaders enables organizations to position themselves for sustained success in a rapidly evolving landscape, gaining a true competitive advantage.

To help organizations evaluate readiness, we developed three complementary frameworks. Each addresses a different AI leadership archetype, ensuring both business leaders and technical experts are prepared to drive AI transformation. The three frameworks are designed to address individual assessment needs.  

The first framework provides a comprehensive evaluation applicable to various functions, offering an overarching perspective on AI leadership.  

In contrast, the second and third frameworks delve deeper, allowing for a nuanced exploration of specific aspects such as strategy, understanding customers, or leading change. These frameworks are tailored to cater to distinct roles: the second framework is oriented towards business-focused positions, referred to as "transformers," while the third framework is geared towards more technical roles, known as "builders."

This universal model applies to executives across functions and industries. It evaluates how leaders understand, apply, and lead AI initiatives. 

Using a seven-point scale—ranging from red flag behaviors to exemplary leadership that drives industry-wide AI transformation—this framework assesses understanding from fundamental awareness to transformative methodologies.  

This approach not only provides insights into each leader's current level of competence but also offers actionable steps leaders can take to advance to the next level of AI leadership.

This framework is geared towards AI Transformers—business leaders who drive the adoption and integration of AI across strategy and culture. 

AI Transformers are the execution engine of AI transformation. They drive the strategic adoption and integration of AI across a function or an organization, not necessarily through technical expertise, but through vision, influence and execution. These leaders are defined by their passion to create value through AI, not just their understanding of technology. 

We assess AI Transformers on how they: 

• Lead change in the face of AI disruption. 
• Use data to make strategic decisions and prioritize AI use cases. 
• Understand customers through AI-enhanced insights. 
• Collaborate across functions to embed AI into business processes. 

Each competency is rated across four levels: Inactive, Reactive, Proactive, and Transformational. This helps pinpoint a leader’s maturity and ambition in applying AI. For example, when we are looking at a leader's skills for collaboration, we ask how they have been facilitating collaboration between technical and non-technical teams to implement AI solutions or we ask him/her to provide a detailed example over a cross-departmental project involving AI—how the leader created buy-in from the business. 

Importantly, AI transformation is not confined to technical teams. Every member of an organization, from HR to finance to marketing, should act to some extent as an AI transformer—by implementing AI in their own work, then teaching colleagues and helping in shaping the organization's overall AI transformation.

This framework is oriented towards AI Builders—technical leaders who design, develop, and scale AI systems.  

AI Builders are the technical architects of AI transformation. They design, develop, and deploy AI solutions that power business innovation and operational efficiency. Deeply embedded in the technology itself, they build the infrastructure, models, and systems that make AI scalable, secure, and responsible. These individuals are defined by their technical depth, strategic foresight, and ability to lead high-performing AI teams.  

They progress from developing use cases to leading AI programs, often heading AI labs or Centers of Excellence. Builders must anticipate trends, manage risks, and ensure that AI systems are robust, ethical, and aligned with enterprise goals. One key pitfall for AI Builders is that they may approach AI transformation from the technical side and are too disconnected from the business.  

Therefore, AI Builders are assessed on how they: 

• Lead change and foster innovation within technical teams. 
• Use data and machine learning to inform strategic decisions. 
• Collaborate across departments to integrate AI into business processes. 
• Mentor and develop technical talent to scale AI capabilities. 

Each competency is rated similar to the scale we use for AI transformers. This model ensures that tech leaders are not only technically sound but also strategically aligned and communicatively effective. 

In summary, while business leaders are assessed on their ability to integrate AI into strategic goals, manage change, and align teams around AI-driven value creation, technology leaders are evaluated on their ability to build scalable AI systems, manage technical risk, work with other departments collaboratively and translate AI capabilities into business impact. 

Download the spreadsheet to learn more about each AI leadership archetype: What Type of AI Leadership Does Your Organization Need?

The lesson is clear: AI transformation is not just about tools—it’s about people. Having success depends on whether leaders have the AI skills to guide their organizations with confidence.  

By assessing and strengthening AI for leaders at every level, organizations can build AI-ready leadership pipelines, foster a culture of innovation and continuous learning, make informed decisions about hiring, promotion, and development, accelerate enterprise-wide AI adoption with confidence and clarity. 

In short, success in the AI era depends on more than technology. It depends on whether your leaders are prepared to lead the way. Organizations that assess AI skills systematically are those best equipped to thrive in AI transformation.