Bringing AI Into a Lean Transformation: Where to Start
The question isn’t “AI or lean?”
It’s how to use AI as a thought partner to enable lean leadership—to shorten the time from problem seen to problem solved, sharpen strategy through data-driven insights and strengthen alignment through digital catchball.
When used well, AI becomes a digital sensei: a partner that challenges your thinking, accelerates learning, and helps you build stronger, more resilient systems.
As I wrote earlier this year:
“AI should amplify respect for people and continuous improvement—not replace them.”
Guardrails for Responsible AI in Lean
The companies that embrace AI this way will set the standard for the next generation of lean.
- Transparency: Be explicit about when and how AI influences decisions. Hidden algorithms undermine trust; visible ones invite dialogue and learning.
- Continuous learning: Treat AI like standard work—it should evolve and improve through frequent reflection and experimentation.
- Human-centered design: Build systems that engage operators rather than doing work to them.
- Capability-building: Just as the “least operator” concept reinvests human talent into improvement, AI adoption should include training pathways that elevate skills rather than replace them.
- Customer focus: Every AI application must trace back to its ultimate purpose—serving customers better.
Key References for This Series
Lean Enterprise Institute. “RootCoach Demonstration and Lean AI Applications,” 2025.
Graban, Mark. “Lean Hospitals AI Knowledge Assistant,” 2025.
Flinchbaugh, Jamie. “Transparency and AI in Lean Leadership,” 2025.
IndustryWeek. “Toyota’s AI Platform Accelerates Lean PDCA Cycles,” 2025.
IndustryWeek. “GE Appliances: Lean Technology Strategy in Action,” 2025.
Woods, Geoff. “The AI-Driven Leader: Harnessing Digital Thinking Partners,” 2024.
Mueller, Pam A., and Oppenheimer, Daniel M. “The Pen Is Mightier Than the Keyboard: Advantages of Longhand Over Laptop Note Taking, Psychological Science,” 2014.
Rother, Mike. “Toyota Kata: Managing People for Improvement, Adaptiveness and Superior Results,” McGraw-Hill, 2009.
Next Level Partners. “AI as an Enabler for Lean Leadership and Continuous Improvement,” January 2025 Blog.
A 90-Day Starting Plan
Introducing AI into a lean transformation should be deliberate and testable, just like any kaizen activity.
This framework gives you three initial beachheads, each with clear success measures tied to business outcomes and customer value.
1. Problem-solving and A3 logic
Purpose: Use AI as a coach to improve the quality and clarity of thinking, not just speed.
AI should review the A3 or 8D problem-solving methodology for gaps in logic, evidence and root-cause connections while helping teams ask better “5 Why” questions and brainstorm potential causes.
What good looks like:
- Countermeasures are more directly tied to validated root causes.
- Leaders see improved clarity of logic flow in A3 reports, as measured through coaching reviews.
- Problem-solving maturity scores (based on a defined rubric) trend upward across the organization.
Key metrics:
- Percentage of A3s meeting internal quality criteria (logic flow, root-cause clarity).
- Increase in corrective actions sustained beyond 90 days.
- Reduction in repeat occurrence of chronic issues.
2. Quality at source through mistake-proofing
Purpose: Deploy AI-enabled poka-yoke systems, such as computer vision, to detect abnormalities earlier and prevent defects from moving downstream or reaching customers.
What good looks like:
- Defects are caught immediately at the source, reducing rework and scrap.
- Operators receive real-time alerts and feedback that help them solve issues quickly.
- Quality issues are systematically addressed through engineering and process improvements.
Key metrics:
- First-pass yield (FPY): Increase in the % of units meeting specs without rework.
- Defect escape rate: Reduction in defects reaching downstream operations or customers.
- Cost of poor quality (COPQ): Financial impact reduction from rework, scrap and returns.
3. Maintenance and flow stability through TPM
Purpose: Apply AI-driven predictive analytics to improve equipment reliability and support total productive maintenance (TPM) initiatives.
AI should complement foundational operator care routines by predicting failures earlier and providing actionable insights for planned interventions.
What good looks like:
- Stable, predictable equipment performance that supports takt-driven production.
- Maintenance teams shift from firefighting to proactive improvement.
- Operators are actively engaged in asset care, with AI serving as a diagnostic guide.
Key Metrics:
- OEE improvement: Gains in availability through reduced unplanned downtime.
- Mean time between failures (MTBF): Increase driven by predictive maintenance insights.
- Planned vs. unplanned work ratio: Higher % of maintenance work scheduled proactively.
Guidelines for All Beachheads
- Start small, scale fast: Begin with one pilot value stream or cell for each beachhead.
- Run true plan-do-check-act (PDCA) cycles: Treat AI implementation as an experiment—define the hypothesis, run tests, and reflect on results before expanding.
- Connect to strategy deployment: Tie every outcome directly back to hoshin priorities and customer value streams.
The Trap to Avoid
The greatest risk is treating AI as a shortcut around capability-building.
As Mike Rother describes in his book “Toyota Kata,” the interaction between coach and learner is central to developing scientific thinking and problem-solving capability. This ongoing dialogue is what builds the habits and culture that sustain continuous improvement over time.
AI should not replace this essential feedback loop. Instead, it should act as a supplemental tool, helping both the coach and the learner:
- See patterns in data more clearly.
- Ask better, more targeted questions.
- Explore hypotheses through faster, data-driven experiments.
If leaders begin to rely on AI to provide answers rather than fostering a coaching culture, they risk undermining the very routines and mindset that make lean transformations successful.
The kata routine—learning through practice, reflection and structured dialogue—must remain at the heart of improvement.
AI should serve as a thought partner, strengthening the coach/learner relationship and accelerating learning cycles, not as an automated substitute for human guidance and development.
About the Author
Eric Lussier
Principal, Next Level Partners
Eric is a hands-on student and practitioner of lean with a passion for building problem-solving cultures built on the pillars of continuous improvement and respect for people. Originally trained by a Japanese sensei as an engineering co-op student, he has over 30 years of experience implementing continuous improvement practices in all aspects of operating companies, in a variety of industries, leading to accelerated operating and financial performance.
Before joining NEXT LEVEL Partners®, LLC, Eric held executive and leadership roles with public and private equity-backed companies including Steel Partners, Sequa Corporation, and Allied Signal.
Eric earned an MS in Industrial and Systems Engineering from the University of Alabama Huntsville, an MS in Industrial Engineering / Engineering Management from the University of Tennessee, and a BS in Industrial Engineering from the University of Tennessee.