Each framework solves one part of the same problem: most AI projects don't fail because the model is weak. They fail because there's no method connecting the idea to something that actually ships.
Lean Six Sigma's Define, Measure, Analyze, Improve, Control cycle, rebuilt for a world where the "Improve" step means deploying an AI agent instead of a process tweak. It's the operating system that takes AI from idea to scaled, measured outcome, instead of a pilot that quietly dies in a slide deck.
Most people building with AI can't answer a simple question: are they actually working with agents, or still automating single tasks one prompt at a time? This model maps five maturity stages, from manual process to fully autonomous multi-agent systems, so you know precisely which rung you're on and what the next one actually requires.
Most AI business cases collapse under scrutiny because the value claims are vague. This scorecard forces every use case through the same rigor as a capital investment: hard cost avoidance, cycle time reduction, and risk mitigation, quantified before a rupee is spent.
A visual map plotting every candidate use case by effort against value, so leadership can see in one glance which three initiatives deserve this quarter's budget, and which forty should wait. It kills AI sprawl before it starts.
These frameworks are taught in full inside the LSS-AI Black Belt cohort. Follow along, or join a session.
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