Applying Lean Six Sigma to Deliver Reliable, Scalable AI Deployment
Building Successful AI Deployments: Four Proven Steps
A promising AI pilot can falter when the process behind it has never been defined, measured, or improved. Before automating decisions, teams need to know what data matters, where waste enters the workflow, and how results will be monitored. Download Candy Mitchell’s white paper for a four-step approach to building AI deployments that hold up and scale.
An AI model is only as sound as the process underneath it. A model built on an undefined process gives unreliable answers. A model built on a process that carries waste automates the waste.
In this white paper, Candy Mitchell, Managing Director of PLG’s Enterprise Transformation practice, explains why AI deployments stall after a promising pilot. She identifies four pitfalls: model accuracy, model changes over time, a lack of required skill sets, and manual interference. She then lays out a four-step sequence that runs from business process definition to process automation. In that sequence, Lean Six Sigma supplies the discipline, governance, and measurement rigor that AI leaves out.
The paper closes with where to start, and the first step does not need a large budget. Read it before you scope your next AI investment.
