Notes on AI Learning, Literacy, and Leadership Fitness

With AI technologies driving rapid change in our ways of living, working, and leading, the newsletter will include a regular column that addresses major developments, promising, instructive, or inspiring practices, or other items of interest to creative leaders.

This month’s featured articles offer useful perspectives on how leaders can thoughtfully integrate AI learning and literacy priorities into their organizations – and their individual leadership practices – while preserving and enhancing human value. The Microsoft AI Business School initiative demonstrates the importance of developing AI fluency at the leadership level, focusing not just on technical capabilities but on organizational strategy, culture change, and responsible implementation. Meanwhile, Columbia University Professor Shivaram Rajgopal’s analysis of AI’s impact on business education provides a framework for thinking about how AI will reshape knowledge work – moving beyond using AI to simply automate or optimize existing processes toward leveraging it to enhance human judgment and decision-making, creativity, and complex problem-solving. Boston Consulting Group’s recent report “The CEO’s Workout for Peak AI Performance”frames leadership preparedness as a fitness challenge, arguing that executives need both “sprint” capabilities to make strategic investments and “marathon” endurance to sustain organizational transformation in a rapidly evolving landscape.

In fact, these perspectives all converge around a compelling fitness metaphor for leadership in the AI era. They recommend leaders embrace both investing strategically, accelerating adoption, and securing talent (“sprint” capabilities) and maintaining momentum, remaining agile, and imagining new possibilities (“marathon” endurance). As the BCG reports says, “Put simply, this is not a ‘wait and see’ moment. It’s a ‘go fast and accelerate’ moment.” Their research reveals that only one in four executives report significant returns from AI investments, suggesting a vast opportunity gap for leaders willing to commit to AI learning, literacy, and fitness.

Together, these pieces suggest that creative leaders should focus on three main areas:

First, leaders must prioritize organizational and individual AI literacy.

Microsoft’s AI Business School demonstrates that leadership fluency in AI is essential for strategic decision-making, not merely technical implementation. Their curriculum helps executives develop “AI IQ” through practical case studies that bridge technical concepts with business applications. Professor Rajgopal’s analysis complements this by highlighting the need to distinguish between tasks where AI enhances productivity and those requiring distinctly human capabilities like ethical judgment.

BCG reinforces this by recommending leaders commit to working with AI daily – treating it as a “digital chief of staff” they confer with regularly. This deliberate engagement enables leaders to make more informed AI investment decisions and avoid spreading resources across too many pilots. The impact can be significant: focused leaders “scale 2.3 more AI products across their companies and achieve more than twice the return on their AI investments than their unfocused peers.”

Second, leaders must personally commit to their own structured AI fitness.

The Microsoft AI Business School emphasizes that leadership fluency in AI isn’t optional – it’s essential for guiding strategic decision-making and organizational transformation. Their curriculum specifically addresses this by helping executives develop “AI IQ” through practical case studies and frameworks that bridge technical concepts with business applications. This approach recognizes that without leadership understanding, AI initiatives risk becoming siloed technical experiments rather than strategic drivers of value.

The BCG article argues that CEOs should designate a “personal AI fitness trainer” to help them navigate the rapidly evolving landscape. This acknowledges that leaders don’t have time to test every new model or digest all information, but they can’t delegate understanding. The Microsoft AI Business School exemplifies this approach by equipping executives with knowledge to develop AI strategy, build data-driven cultures, and implement responsible practices. This executive-level fluency ensures that AI adoption isn’t merely a technical implementation but a strategic, human-centered transformation.

Third, leading AI learning requires multiple perspectives and everyday commitment.

Professor Rajgopal’s framework emphasizes that AI integration requires rethinking not just how work gets done but who does which parts of the work. His analysis suggests that leaders must continually experiment with different human-AI collaboration models, acquiring both theoretical understanding and practical experience with the technology. This ongoing learning process enables leaders to better identify which tasks benefit from AI augmentation and which require predominantly human input.

BCG highlights the value of starting “a two-way dialogue with their employees, both to help them understand the role AI plays in their company and to glean ‘boots on the ground’ insights from those using it.” This recognizes that successful AI integration depends on culture change, capability building, and creating environments where humans and AI complement each other. The BCG model proposes a 10-20-70 formula: 10% of resources for building algorithms, 20% for data and tech infrastructure, and a full 70% for “transforming the way people and processes operate.”

Creative leaders navigating AI’s integration should embrace both strategic vision and adaptive learning. Microsoft’s curriculum particularly emphasizes the importance of responsible AI governance frameworks that establish ethical guidelines, transparency standards, and accountability mechanisms. Rajgopal’s analysis complements this by highlighting how business education must evolve to prepare future leaders for an AI-integrated workplace, focusing on critical thinking, ethical reasoning, and creative problem-solving that leverages rather than competes with AI capabilities. As BCG reports, nearly half of desk workers reportedly feel uncomfortable telling managers they use AI for simple tasks. When leaders openly use and discuss AI, they legitimize its role and encourage experimentation.

We need leaders – and need to be leaders – who can demystify AI for their organizations. The path forward requires deliberate leadership rather than passive or occasional adoption. Success with AI means moving beyond technical implementation to address the human elements – mindset shifts, culture change, and capability building. Success with AI also thus means creating environments where people and AI genuinely augment each other’s strengths, balancing immediate action with long-term vision that only committed, AI-fit leaders can provide.