Insights

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.

How do Robots map an environment? Welcome to the SLAM Algorithm. The wheel encoder and IMU tracks rotation and movement. The LIDAR and RGB-D sensors capture data from the environment. Noisy data is filtered using a formula that predicts the Robot's position versus landmarks (known as Kalman Filter). This information is fused using the onboard computing device, which helps optimize drift from the IMU. During the mapping process, the Robot constantly checks is it has revisited

Linear Algebra is a critical skill for AI leadership. It should be mandatory learning as part of high school and undergraduate curriculums. CHROs should emphasize this skill in their emerging leadership programs. Having basic skills in Linear Algebra allows professionals to proactively manage machines. You do not need to perform the calculations yourself. However, understanding the process will make your conversations with the engineering team more effective.

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.

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.

Presenting Data to Executives has never been more FUN than with today's AI tools. Check out the latest list of tools from Machine Leadership that drive effective storytelling. Top AI Tools for Presenting Data to Executives

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