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Dr. David Swanagon

Dr. David Swanagon

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Cofounder

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Join date: May 4, 2025

About

Dr. Swanagon is a Cofounder of Machine Leadership and serves as a Senior Editor of The Machine Leadership Journal. He is an accomplished senior leader and academic in the technology, assessment, and leadership development fields. Dr. Swanagon has led major transformations for some of the world's most prestigious companies including Saudi Aramco, Ericsson, PPG Industries, The Cosmopolitan of Las Vegas, and The Venetian and Palazzo Resorts. Dr. Swanagon has also consulted for PE backed firms in the Cyber Security and AI domains. In addition to corporate leadership, Dr. Swanagon is an Adjunct Professor at the University of North Texas specializing in People Analytics and Organizational Behavior. He has served as a keynote speaker and SME for multiple industry conferences and podcasts. He has a Doctorate in Leadership and Learning from Vanderbilt University, a Masters Degree from Harvard, and is certified as a Senior Professional in Human Resources (SPHR).

Posts (10)

Aug 17, 2025 ∙ 3 min
How CEOs can improve AI adoption by sponsoring Domain-Adapted LLMs
Is your company struggling with AI adoption? A likely cause is that the trust and AI competencies of your non-engineering workforce is low. A great way to solve this problem is to embed non-technical employees into the engineering process, using a company sponsored initiative that capitalizes on the holistic knowledge of the entire workforce. This process requires the leadership of both the CIO and CHRO, with each respective organization working together to create a Domain-Adapted LLM.
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Aug 14, 2025 ∙ 2 min
Top Cognitive Processes for AI
The Age of AI requires engineers to lead machines, lead people that build machines, and lead organizations that adopt AI. A key part of this framework is developing the right cognitive skills. Our research indicates that six elements play a significant role in AI/ML engineering performance. The number sequence is purposeful. Big C Creativity (Frontier Thinking) should be cultivated first before placing constraints on the problem set.
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Aug 14, 2025 ∙ 2 min
How the context window impacts Large Language Models (LLM).
Large Language Models (LLM) that utilize self attention mechanisms are challenged by the context window. When the sequence becomes too long, models can make mistakes with tokens that are located far apart and in the middle of the text. The trick is finding the right balance between cost and decision requirements.
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