If you are a Green or Black Belt, you have probably noticed the same AI headlines I have.
AI is everywhere. New tools appear every week. People talk about “automation” and “agents” as if the whole world will run without humans soon.
It is reasonable to ask: where does Lean Six Sigma fit in that world, and where does that leave you.
The short answer: your skills are not going away. They are moving up a level.
In this email, I will walk you through a simple way to think about how AI is changing Lean Six Sigma, and what that means for your work and your career.
From improving processes to designing self-improving systems
Lean Six Sigma was built to reduce variation, eliminate waste, and improve processes through structured thinking.
Traditionally, that has looked like:
- Running DMAIC projects.
- Using historical data to find root causes.
- Implementing improvements and control plans so the gains stick.
This model assumes that:
- Processes are relatively stable.
- Data is collected and analyzed after the fact.
- Improvement work happens in projects separated from day to day operations.
AI does not throw this away. It changes the scale and the speed.
Instead of only improving processes, we now have the opportunity to design systems that improve themselves.
What AI actually changes in your world
AI is not just a faster calculator. It brings a few new capabilities into the picture:
- Adaptive systems instead of static processes. Models that learn from new data. Workflows that adjust in real time.
- Distributed intelligence instead of single bottlenecks. Decision making embedded in tools and at the edge of the process. Automated pattern detection instead of only manual analysis.
- Leading control instead of lagging indicators. Predicting failures before they happen. Triggering interventions automatically.
For you, that means less time hand building every chart from scratch, and more time deciding what to measure, how to use those measurements, and which actions are actually safe and valuable.
DMAIC does not disappear; it becomes continuous
DMAIC still makes sense in an AI world. The steps just start to look different.
- Define: AI can help you synthesize Voice of Customer data at scale and surface patterns that point to real problems.
- Measure: Instead of static sampling plans only, you can draw on real time data streams from systems and sensors.
- Analyze: Models can highlight correlations, anomalies, and potential causes in minutes instead of weeks.
- Improve: Simulation and digital twins let you test changes virtually before you touch the real process.
- Control: Control plans can become self monitoring; checks run continuously instead of only in periodic audits.
Your job shifts from “run a DMAIC project once” to “design how this DMAIC logic will live inside our systems so it runs every day”.
From data collector to system designer
If you think about your current work, a lot of it may be:
- Pulling data.
- Cleaning and summarizing it.
- Building control charts and capability studies.
- Explaining what you found.
Those skills still matter, but AI can assist with many of the mechanics.
The opportunity for you is to move into work that looks more like:
- System designer: deciding which processes should be measured, which data sources matter, and how to structure flows.
- AI orchestrator: framing problems in ways AI tools can work with, and deciding where in the process they should operate.
- Value architect: designing feedback loops so that improvements are tied to real outcomes like revenue, cost, and customer satisfaction.
Instead of being “the person who runs the project”, you become the person who helps define how intelligence is built into the way work gets done.
Where Lean Six Sigma discipline still matters
It is easy to overhype AI. It can also make quiet mistakes at scale if it is not guided well.
This is where your Six Sigma discipline still matters a lot:
- Validation: AI can suggest patterns and actions, but someone still needs to ask “does this make sense” and “what would we measure to be sure”.
- Measurement integrity: If the data is flawed or the metrics are poorly defined, AI will confidently optimize the wrong thing.
- Governance: Not every decision should be automated. High stakes, ambiguous situations still need human judgment.
In other words, the tools change, but the need for clear definitions, good measurement systems, and thoughtful control never goes away.
What this means for your career
If you are a practicing Green or Black Belt, the safest move is not to wait and see. It is to start shifting your focus now.
Some practical steps you can take:
- Learn enough about modern AI tools to have a concrete opinion on where they fit in your processes and where they do not.
- Start framing improvement ideas in terms of systems and feedback loops, not just one time projects.
- Look for one place in your current work where a simple model or automation could: Spot a problem earlier. Trigger a check automatically. Make a manual report unnecessary.
The belts who adapt fastest will be the ones who can talk credibly about both worlds: where structured improvement is needed, and where intelligent systems can take over some of the load.
Your next step
Take a moment to think about your current role and ask yourself three questions:
- Which part of my work today is repetitive analysis that could be assisted or automated.
- Where would continuous monitoring or prediction make more sense than periodic reviews.
- What new value could I create if I spent more time designing systems and less time moving data around.
Then, before you move on to the next email in your inbox, hit reply and tell me:
- One way AI is already showing up in your work today, and
- One place you think it could help but you are not sure how yet.
I read these, and your answers will help shape the follow up piece I write for managers and leaders.
Best, Ted.
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