The Shift Toward Holistic Performance Metrics

The New Plant Managers in Mexico As a headhunter, I’ve met countless plant managers across Mexico’s vibrant manufacturing sector for the past thirty years. In the early days, these leaders were often defined by their deep technical/operational expertise, relentless drive for efficiency, and almost laser-focused approach to problem-solving on the production floor. While that technical core remains critical, the definition of what makes a “great” plant manager is evolving—and fast. Today’s plant managers aren’t just master troubleshooters; they must become strategic thinkers who bridge corporate goals with on-the-ground realities. They operate at the intersection of supply chain dynamics, financial performance, and global market shifts while nurturing an inclusive workplace culture. Many of the plant managers I’ve placed over the years are discovering that their success isn’t only measured by the number of units shipped on time but also by their ability to foster diverse teams, anticipate technological shifts, and adapt the business to changing regulatory standards. What used to be an almost singular focus on operational metrics—cycle times, scrap rates, and OEE—has grown into a role demanding robust leadership, emotional intelligence, and data-driven decision-making. Leaders must embrace AI-powered predictive analytics, Industry 4.0 innovations, and agile project management methods. The plant manager of tomorrow must cultivate an environment where innovation flourishes, from the shop floor to the C-suite, balancing both cost and creativity. Perhaps the most significant change I’ve witnessed is the shift in these plants' corporate offices toward a more holistic, human-centric leadership style. The ability to resolve conflicts, inspire a multigenerational workforce, and integrate diverse perspectives is becoming as vital as technical expertise. Many of these organizations realize that people strategies—accelerated learning, flexible job assignments, and career development—can be as critical to long-term success as hitting quarterly KPIs. In short, while the fundamentals of running a manufacturing plant will always revolve around efficiency and quality, the plant managers who genuinely stand out will be those who can anticipate the future. They will leverage data and technology for powerful insights, adapt to ever-shifting market pressures, and unify teams around a shared purpose. It’s a remarkable evolution—one that keeps me excited about uncovering the next generation of impactful manufacturing leaders.

RETHINKING AI SUCCESS: A HOLISTIC APPROACH BEYOND BENCHMARKS Why AI Measurement Must Evolve to Focus on Human Collaboration, Ethics, and Long-Term Reliability The evaluation of artificial intelligence (AI) and machine learning (ML) systems has traditionally centered on benchmarks, accuracy rates, and performance speeds—metrics that, while quantifiable, offer a limited perspective on AI's potential and responsibilities. This focus often overlooks critical aspects such as societal impact, ethical considerations, and long-term reliability. This imbalance prompts a vital question: How can we trust AI to serve humanity effectively if we fail to assess its real-world consequences comprehensively? Addressing this issue necessitates a paradigm shift in AI evaluation methodologies, integrating ethical and societal considerations alongside traditional performance metrics to ensure AI systems are aligned with human values and societal well-being. 💡 The Future of AI Measurement To ensure AI is ethical, reliable, and aligned with human values, we need new metrics that measure: ➤ Human-AI collaboration outcomes rather than standalone AI performance ➤ Bias and fairness in AI systems to ensure ethical decision-making ➤ AI’s ability to detect its own limitations and recommend human oversight ➤ The quality of human-AI partnerships in decision-making processes ➤ Alignment with long-term societal benefits, not just narrow optimization goals As AI continues to evolve, its true value won’t be measured by speed or accuracy alone—but by how well it enhances human potential and serves society. #ArtificialIntelligence #management #humanity #Innovation #performance

Working long hours only matters if they’re completed to drive winning results. The 40-hour workweek wasn’t designed for knowledge work. It was designed for factories. Yet many companies still equate “hours worked” with “value created.” That mindset is broken. Here’s what research from top institutions tells us about how the best companies manage performance: MIT Sloan Research: Organizations embracing results-based models—like Neiman Marcus Group’s “total freedom” policy, saw 40% productivity gains and lower attrition. MIT recommends replacing annual reviews with quarterly check-ins for strategic alignment. AI tools can reduce administrative work by 75% while eliminating bias in performance tracking. Harvard Business School Findings: HBS advocates strategic alignment between individual goals and organizational priorities using performance scorecards. They caution against over-focusing on revenue, arguing for balanced metrics that include brand equity and team engagement. Stanford Graduate School of Business: Stanford’s GPS program uses continuous, forward-looking feedback instead of backward-facing reviews. AI helps employees align personal growth goals with cross-functional team needs, making career mobility more fluid and equitable. McKinsey & Company: Companies with modern performance systems are 4.2x more likely to outperform peers and see up to 30% more revenue growth. They recommend using AI to synthesize feedback and simplify decisions—not overwhelm teams with complexity. Boston Consulting Group: Outcome-driven performance frameworks paired with quarterly business reviews help teams stay aligned in fast-moving markets. AI tools rapidly assess skill gaps and rebalance workloads during organizational pivots. ⸻ Three Key Trends: ✅ AI as an enabler reduces administrative burden, eliminates bias, and improves predictive performance planning. ✅ Culture trumps compliance as managers shift from scorekeepers to coaches. ✅ Agility beats rigidity through frequent check-ins and dynamic goals over annual reviews. ⸻ The shift can create a competitive advantage. How is your company measuring what really matters? What’s working (or not working)? Share your thoughts below 👇 ♻️Repost & follow John Brewton Do. Fail. Learn. Grow. Win. Repeat. Forever. ____ 📬Subscribe to Operating by John Brewton for weekly deep dives on the history and future of operating companies.