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-Ad

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.

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.

Understanding how to construct effective times series forecasts is an essential skill for AI Leaders. Engineers should understand which charts are important and what questions to ask. This will make the LLM prompt questions and conversations with the engineering team more impactful.

In the age of AI, leaders are required to do three things: lead machines, lead people that build machines, and lead organizations that adopt AI. Occlusion is a common problem in Computer Vision. AI Leaders must find a way to balance technical requirements with cost considerations, employee skills, and the customer experience.

In the age of AI, leaders are required to do three things: lead machines, lead people that build machines, and lead organizations that adopt AI. To effectively lead in this environment, executives must understand AI. This means going beyond prompt engineering and AI agent courses to learn how things actually work.

Machine Leadership has been selected as a SHRM Recertification General Provider. Our organization is now authorized to award SHRM Professional Development Credits (PDCs) for programs aligned with the SHRM Body of Applied Skills and Knowledge (SHRM BASK). This includes training for HR professionals holding the SHRM-CP and SHRM-SCP credentials. SHRM is a human resources professional society that has 340,000 members in 180 countries.

We're thrilled to announce that David Swanagon, Ed.D, SPHR and Stephen McIntosh have been selected as presenters at the 5th Bi-Annual Columbia Coaching Conference, taking place October 14–16, 2025 at Columbia University in New York City. This year’s theme — High-Impact Coaching in an Era of Hybrid Intelligence — couldn’t be more timely. Their presentation will focus on: Machine Leadership - An integrated coaching framework for the age of AI.

The Age of AI requires new leadership skills. One of the cognitive processes that has become increasingly important for managing AI is short-term memory. George Miller's magic number of 7 plus or minus 2 remains a useful standard for measuring working memory. Our research indicates that AI Engineers that can remember more build better products. The reason is because the job places a premium on short-term recall to ensure daily updates, MLOPs process, model design changes, and

The Machine Leadership Journal has issued a Call for Papers. AI Research Scientists, Professors of Practice, and AI Industry Leaders are encouraged to submit abstract proposals that focus on the emerging field of Machine Leadership. This includes The AI Innovation Frontier, the balance between Machine Autonomy versus Trust, and how AI competencies drive adoption. The Journal is managed by an Editorial Board. All publications are subject to a robust review process and include