AI is powerful, but only when humans guide it.

That’s the essence of “jidoka”: automation with a human touch and intention.

Much of today’s AI conversation centers on replacing work, automating tasks and driving speed. But that framing misses something essential. AI – like any technology or system – is an amplifier. It amplifies the good, the gaps and the friction already present in our organizations.

A tool doesn’t inherently make us better. It makes what’s already there more visible, more powerful and sometimes more problematic.

If your culture and systems are clear, connected and grounded in learning, AI will amplify that.

If they are fragmented, chaotic or overloaded, AI will amplify that , too.

This is why the real power of AI lies not just in what it can automate, but in how it accelerates learning. This point is validated by Google Cloud’s 2025 DORA report, which concludes that “AI’s primary role is as an amplifier, magnifying an organization’s existing strengths and weaknesses. The greatest returns on AI investment come not from the tools themselves, but from a strategic focus on the underlying organizational system,” which I explored in detail with DORA lead Nathen Harvey on the Chain of Learning podcast.

And the leadership mindset we most need right now to focus on our underlying organizational systems comes from jidoka, one of the foundational pillars of the Toyota Production System.

Jidoka: Automation With a Human Touch

Jidoka is the integration of machine automation with human thinking. In practice, it means designing systems where machines automatically stop when something is wrong, alerting people immediately so defects don’t cascade downstream.

But jidoka is more than a mechanical safeguard. It’s a cultural principle that empowers people to stop the process to assess quality, reflect on and learn why an issue occurred, and prevent its recurrence.

The goal of jidoka isn’t to replace people with machines. It’s to protect quality, expose issues early and free people from mindless monitoring so they can focus on judgment, creativity, problem-solving and improvement.

Jidoka is about elevating, not diminishing, human capabilities through the separation of human and machine work.

Every time I return to the Toyota Museum of Technology and Innovation in Nagoya to show global leaders participating in my Japan Leadership Experience the history of the Toyota Production System and lean management, I’m reminded of the origin story of jidoka – and how it enabled growth and innovation.

Sakichi Toyoda didn’t invent automated looms to remove humans from the work. He invented them to elevate human capability by preventing defects and reducing burden. His early designs stopped a loom automatically when a thread broke, preventing flawed fabric from piling up and freeing operators from constantly watching machines. Automation existed to support people, not eliminate them, and allow them to take on more and greater responsibility

This people-first philosophy is the foundation of Toyota’s success. As 40-year Toyota leader Isao Yoshino told me, and as I share in the opening line of my book Learning to Lead, Leading to Learn, “The only secret to Toyota is its attitude towards learning.” Every tool, framework, and innovative concept—including jidoka and just-in-time production—emerged from Toyota’s deeper commitment to “making people” by elevating their work and developing their capability to improve processes, solve problems and create value for customers and society.

Generative AI Is Simply the Latest Evolution of the Jidoka Principle

Just as Toyoda’s looms freed workers from repetitive physical tasks, AI frees us from repetitive cognitive tasks. It can draft, analyze, summarize, detect patterns and process information at scale. But the human touch is still essential: to guide, question, challenge, refine and improve the output.

Without intentional oversight, technology becomes a mirror reflecting our weakest tendencies.

With intention, AI becomes a lever that elevates our strongest capabilities.

This is modern jidoka:

Let machines do what machines do best: pattern recognition, summarization, automation, signal detection

Let humans do what only humans can: judgment, ethics, coaching, reflection, creativity, adaptation, collaboration and human connection

The future of work isn’t AI versus human. It’s AI + human, where technology amplifies our capability and humans are freed to lead, learn and solve higher-order problems.

But There Is a Responsibility Leaders Cannot Ignore

AI will not develop the next generation of leaders. We must.

A growing theme across lean, continuous improvement and leadership circles is concern about what AI means for early-career development: the apprenticeship ladder every organization depends on to grow its next generation of leaders. And the early warning signs are already visible. HP’s plan to cut 4,000–6,000 jobs as it accelerates AI adoption, and Amazon’s reduction of 14,000 corporate roles tied to an AI-driven reorganization, reflect a broader pattern: Some companies are scaling back hiring under the belief that “AI can do the entry-level work.”

As Annie Hedgpeth recently highlighted in her thoughtful article on the junior hiring crisis, this is more than a talent issue—it’s a cultural risk. When organizations assume AI can absorb early-career tasks, they risk dismantling the very pathways where future managers learn to lead.

But early-career roles were never just about executing tasks or becoming competent technical independent contributors.

Junior work is where people learn to make judgment calls, collaborate across functions, recover from mistakes, understand customers, ask better questions, build relationships and practice leadership behaviors in low-risk settings. And developing junior employees also develops new managers as they build their people and project management skills. This is the essence of a Chain of Learning—which I describe in this short clip—where both learner and leader grow together, the strength of the chain residing in the connection between each link.

These learning experiences remain essential even as automation advances. AI cannot develop such problem-solving and management capabilities. These uniquely human skills are cultivated through experience, reflection, coaching and support. 

For example, in manufacturing, an automated cell may detect an abnormality and stop production through jidoka, but often a junior operator or employee leads the initial response translating data to insights: gathering facts at the machine, coordinating with maintenance or quality, facilitating a quick 5-Why discussion to get to the root cause and testing and documenting the countermeasure. 

Junior employees also learn by coordinating the flow between automated stations—monitoring  where queues build up, testing adjustments and working across functions to smooth the process. These opportunities are where junior employees learn how to communicate across roles, make decisions under time pressure, and collaborate to solve problems at the source to prevent recurrence. Automation reduces repetitive physical tasks, while at the same time creating more opportunities for junior team members to practice leadership behaviors through their work.

In Learning to Lead, I also share Yoshino’s “paint mistake” (which you can watch me tell here). As a new hire just out of university, he accidentally mixed the wrong paint and solvent, causing over 100 cars to require repainting. But instead of punishment or blame, he received something far more powerful: reflection, support, and learning—not only about solving the immediate problem, but about how to lead when problems arise. His manager even thanked him for making the mistake because it revealed that they, as leaders, had not mistake-proofed the workplace to set him up for success.

That single experience shaped Yoshino’s leadership philosophy for the next 40 years at Toyota and beyond.

Multiply that by thousands of such moments, and you get the fundamental people-oriented learning culture that defines Toyota’s leadership DNA—a culture rooted in people development, respect and learning by doing.

This is what sits at risk when leaders assume AI can replace junior roles.

You can automate technical tasks. You cannot automate judgment, ethics, collaboration, or leadership capacity.

If organizations eliminate early-career pathways, they unintentionally break the very foundation that lean, jidoka and operational excellence rely on: the responsibility to develop people.

Jidoka teaches us that technology should elevate humans, not eliminate the conditions in which humans learn.

As AI becomes embedded across industries, leaders must design systems where quality is built in, people remain central to improvement, human capability is elevated rather than diminished and early-career employees have meaningful opportunities to grow.

Teams must be empowered to “pull the andon cord” (stop the work) on flawed outputs—whether generated by a machine or a human. And leaders must create cultures where AI strengthens learning rather than magnifies complexity or errors.

The future of operational excellence is not choosing between technology or people.
It is designing environments where AI accelerates learning — and humans are freed to lead, decide, reflect, and connect.

This is the promise of jidoka in the age of AI—leveraging technology to strengthen our humanity, not replace it.