Everyone is declaring Lean Six Sigma obsolete. The current argument goes like this: “We have AI now. Why bother with belts, charters, or control plans?” Traditional improvement projects can feel slow and ritualistic when the business wants results yesterday. But the answer isn’t to throw out Lean Six Sigma. The answer is to modernize it; inject the power of AI into Lean Six Sigma to supercharge process improvement.
Using AI changes the tempo, not the truth about process improvement. Waste is still waste. Variation still erodes quality. Customers still judge you on value, accuracy, and speed. What AI gives us is a way to do the work of seeing and processing things faster and at scale. It catapults Lean Six Sigma into the present day.
1.The bottleneck has always been the middle
There are three main parts to every process improvement project – Beginning: the problem is defined; Middle: understanding how work happens, improving it, and developing supporting documentation; End: implementation and sustaining changes.
All the heavy lifting takes place in the large middle portion; it’s a bottleneck. It is time consuming assembling artifacts, reviewing documentation, scheduling meetings, and interviewing subject matter experts. All this work takes weeks and must be done before the initial “as-is” process model is created, and the high value work of improvement can get started.
AI breaks through the bottleneck. Instead of weeks to produce the first process map, you can generate an accurate “as-is” from documents, notes, plain text, or transcripts in minutes. The process team moves from “discussing” the work to reviewing, editing and improving the work almost immediately. Process improvement project execution speeds up exponentially.
2. Democratizing the front line without diluting the craft
There is also a worry that AI turns process improvement into a push-button exercise. Actually, it does the opposite. When the model exists on day one, you can involve the people who do the work – the operators, analysts, coordinators – immediately and in a valuable way, analyzing the as-is without demanding weeks of their time.
They react to something they recognize – a graphical illustration of their business process. They validate what’s right and point to what’s wrong. That early clarity draws people in instead of wearing them down. Lean Six Sigma experts are still needed for deep analysis, statistical estimation, and control strategy. With AI, these experts spend their time adding value instead of juggling calendars to schedule meetings.
From “dinosaur methods” to living systems
Most resistance to Lean Six Sigma and process improvement isn’t about the ideas. It’s about the overhead. Leaders see a long runway and wonder if the organization can wait. AI shortens that runway dramatically. It makes continuous improvement feel relevant with models that update with new rules, link from a task to the SOP that governs it, training that flows directly from the process definition, and dashboards that watch and measure what matters. The rituals stay core Six Sigma — Define, Measure, Analyze, Improve, Control.
AI will keep getting better at reading documents, summarizing meetings, and proposing changes. But the job of running a reliable operation still hinges on seeing the work, testing improvements, and sustaining what works. Lean Six Sigma gives you that backbone. AI gives you the acceleration.
We don’t need fewer Green or Black Belts, and this isn’t the end of Lean Six Sigma. It’s the start of a new era of process improvement.
Founder and CEO at The Efficiency Group
Frank Vega is the founder and CEO of The Efficiency Group, a process-improvement firm using Gen AI to create BPMN models in minutes. Under his leadership, TEG has supported organizations across healthcare, government, supply chain, finance, and manufacturing, including initiatives with the National Institutes of Health, the Department of Energy, the Department of Defense, and General Dynamics IT. Frank’s work centers on making Lean Six Sigma and continuous improvement accessible to non-experts, enabling faster kaizen cycles, cleaner documentation, and measurable gains in throughput, quality, and cost. He frequently speaks on the practical use of AI in process mapping and on building a culture of improvement that scales beyond specialist teams.
