Lean Six Sigma helps teams examine how work is done, reduce waste, and understand variation. Artificial intelligence can support that work by helping teams explore data and identify patterns. Useful results still depend on a clear problem, reliable data, and human judgment.
Start with the process
Before selecting an AI tool, define the problem in practical terms. For example, a team might want to understand why customer requests take too long to resolve. Map the current process, agree on what counts as a completed request, and measure the starting point.
Use DMAIC to structure the work
Define: Describe the customer need, the problem, and the project boundaries.
Measure: Collect relevant data consistently. Check missing values, definitions, and recording errors before drawing conclusions.
Analyze: Investigate possible causes of delays or defects. AI can help explore patterns, but a pattern alone does not establish a cause. Compare findings with process knowledge and additional evidence.
Improve: Test a focused change on a manageable scale. Compare the results against the baseline and look for unintended effects before expanding the change.
Control: Assign an owner, document the revised process, and monitor performance over time. Review automated outputs when the process or data changes.
A practical example
A service team could group support requests by topic, compare resolution times, and identify recurring handoffs. An AI tool might suggest themes in the request descriptions. The team would then check a sample manually, investigate the handoffs, and test a clearer routing rule. The improvement should be judged by measured service results, not by the novelty of the tool.
Choose a useful first project
Start with one repeatable process and one measurable outcome. Keep personal and confidential information within approved systems, check the tool outputs, and make responsibilities clear. A small, well-measured improvement provides a stronger basis for future work than an ambitious project with unclear success criteria.
Build practical skills
Learning process mapping, root-cause analysis, basic statistics, and improvement planning can help professionals evaluate where AI adds value. Explore GREEZA Academy's course catalogue to find training relevant to your role and development goals.