
Leading Transformation in the Age of Lean Ai
Exploring how leaders can combine Lean thinking with today's narrow AI tools through five practical levels of collaboration—from basic chat to agentic systems—while keeping humans at the center.
Where Traditional Excellence Meets Modern Intelligence

Humans + AI > Problems
Sustainable improvement requires more than technology. It demands the right combination of advanced tools, disciplined behaviors, and committed management. When humans and AI work together, guided by Lean principles, we create solutions greater than either could achieve alone.
Exploring the practical intersection of Lean thinking and AI innovation

Exploring how leaders can combine Lean thinking with today's narrow AI tools through five practical levels of collaboration—from basic chat to agentic systems—while keeping humans at the center.

Humans perceive and verify end-to-end, while LLMs reason middle-to-middle. How combining both creates a powerful two-layer PDCA loop for learning together.

The signals surrounding AI couldn't be more confusing — warnings of massive unemployment, glitzy robot demos, yet quiet transformation happening in software and consulting. The key lies in understanding Big AI versus Narrow AI, and partnering AI with lean rather than treating it as an all-encompassing solution.

Drawing from 30 years of Lean implementation, this framework shows why successful AI adoption requires three elements working together: Technology, Behavior, and Management systems. When any element is zero, impact is zero. Learn how to multiply results instead of adding them.

Most Lean stories focus on the assembly line—kanban, standardized work, 5S—but overlook Toyota’s hidden engine of excellence: Production Engineering. Beneath the visible face of the Toyota Production System lay a decades-long effort to raise process capability through deep engineering kaizen—redesigning chucks, spindles, bearings, and fixtures. While consultants documented the daily management tools (Track 3) and researchers studied product development (Track 1), the middle layer—Track 2 Production Engineering (Seisan Gijutsu)—remained invisible. This “missing link” explains how Toyota moved from 5 % scrap rates to 0.05 % while holding micron-level precision. The story reminds us that Lean is not just about managing work—it’s about engineering process capability that others can’t see.

After spending some time building various coaching tools with AI, I began to see clear patterns in how people collaborate with large language models. The result is a five-level framework for AI proficiency — from casual chat use to full system integration. It explains why some users create breakthrough results while others see only hype, and how Lean thinking can guide us toward smarter human-AI collaboration.