If you lead a team or a function that relies on Green and Black Belts, you are probably hearing a lot about AI right now. Most of that advice is not written for Lean Six Sigma leaders specifically.
Vendors promise “autonomous operations” and “smart factories”. Internal teams experiment with new tools. At the same time, you still have goals to hit this quarter and a set of processes that already work reasonably well.
It can feel unclear how Lean Six Sigma, AI, and your existing operating model fit together.
In this email, I will walk through a simple way to think about AI and Lean Six Sigma from a leadership perspective, and how you can use both to build systems that improve themselves instead of just running more projects.
From one-off projects to self-improving systems
Lean Six Sigma has been incredibly successful as a project-based discipline:
- Define a problem.
- Measure and analyze what is going wrong.
- Improve the process.
- Put controls in place so the gains stick.
Most organizations still run improvement this way: a portfolio of projects led by belts, reviewed in steering committees, funded and staffed as needed.
AI gives you an opportunity to move beyond this, toward systems that:
- Monitor performance continuously.
- Detect problems as they emerge, not just after the fact.
- Trigger standard responses automatically where it is safe to do so.
That does not mean you stop running projects. It means your projects increasingly build reusable capabilities and feedback loops into the way work is done, rather than just fixing one point in time.
What changes in DMAIC when AI is on the table
At a high level, DMAIC still makes sense for your leaders to use. The questions simply expand.
- Define: Where could AI help us see problems earlier or in more places than we can today. Which customer signals (complaints, reviews, usage patterns) are too big or too noisy for manual analysis alone.
- Measure: What real time data streams do we already have, and which ones do we need to instrument. How clean and reliable are those data sources; are they ready for automated decisions.
- Analyze: Where can models help surface correlations, anomalies, and candidate root causes faster than manual work. Where do we still need deeper human investigation before we act.
- Improve: Can we use simulation or simple digital twins to test changes virtually before we roll them out. Can we embed specific checks, recommendations, or automations into the tools your teams already use.
- Control: What would it look like for this process to “watch itself” and alert us when it drifts. Which actions, if any, are safe to trigger automatically when certain patterns appear.
Your role is to make sure someone is asking these questions at the design level, not just at the project level.
What to expect from your belts in an AI-augmented world
If you want to use AI and Lean Six Sigma together, the role of your belts will evolve.
Instead of only asking them to “run more projects”, you will get more leverage if you start expecting work like:
- System and data design: defining where and how processes should be instrumented, what should be measured, and where data needs to be cleaned up before automation is even on the table.
- Problem framing for AI: translating real business pain into problems that AI tools can actually work on, with clear inputs, constraints, and outputs.
- Validation and governance: building checks so models are not quietly optimizing the wrong thing or amplifying bad assumptions.
Your best belts already think this way informally. The shift is to make it explicit: part of their value is in designing intelligence into the system, not just in closing this quarter’s project list.
Where leaders still need Lean thinking before AI
It is tempting to “throw AI” at messy processes. In practice, that can make a bad system faster and harder to reason about.
Lean thinking is still your first line of defense:
- Eliminate obvious waste before you automate.
- Clarify value from the customer’s perspective so AI is not optimizing the wrong outcome.
- Standardize work where it makes sense so models are learning from consistent patterns instead of random noise.
If you do not clean up the process first, you risk investing in sophisticated tooling that simply hides the underlying problems.
Practical moves you can make in the next 90 days
You do not need a massive transformation program to start moving in this direction. Here are some practical moves you can take as a leader:
- Pick one process to make “AI-ready”. Choose a high volume, high friction process where you already have data. Ask your belts to map the flow, identify key decision points, and assess data quality at each step.
- Ask better questions in reviews. Instead of only “What was the project result”, also ask: “What reusable capability did we build”. “How will this improvement stay alive in the system without more meetings”.
- Support one small AI experiment with clear boundaries. For example, a model that flags likely defects before shipment, or a small agent that drafts daily performance summaries for a team. Make it safe to test, but insist on clear metrics and validation before you trust it.
The point is not to automate everything overnight. It is to start building the muscle of thinking in terms of systems and platforms instead of only projects.
How AI + Lean Six Sigma can change your operating model
Over time, the organizations that do this well will see a quiet but important shift:
- Less time spent chasing after the same recurring problems.
- More time invested in designing processes and systems that prevent those problems in the first place.
- Improvement work that compounds because it is built into the way tools and workflows operate.
That is “operational excellence 2.0” in practice: not just better projects, but a better operating system for your business.
Your next step as a leader
Think about your area of responsibility and ask yourself:
- Where are my teams still running lots of one-off projects on the same processes.
- Where could we invest once in a better system and get the benefit every day.
- Which belts on my team already think this way, and how can I give them room to lead.
Then, if you have a minute, hit reply and tell me:
- One process in your world that you suspect is ready for AI-augmented improvement, and
- What has held you back from starting so far.
I read these, and your answers will help me write future pieces that are more useful to you and your peers.
Best, Ted.
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