The framework guides project teams through a structured lifecycle, ensuring consistent evaluation, implementation, and operationalization of AI use cases. It includes a phased roadmap, key implementation activities, and project management artifacts designed for reuse across initiatives.
Role: Project Manager
A seven-phase delivery model was developed, covering everything from use case discovery through production deployment and reuse. This roadmap enables repeatable implementation while allowing flexibility based on project scope and tooling needs.
A team formation model was built to ensure alignment between business, data science, and engineering teams. This included defining roles like project manager, product owner, data modelers, and technology resources across the pilot and validation phases.
The framework includes templates and tools such as use case charters, feasibility checklists, RACI matrices, MVP definitions, risk logs, and validation rubrics—standardizing AI delivery while enabling customization for individual use cases.
The project began by defining a phased roadmap: Discovery, Feasibility & Design, Team Formation, Pilot Build, Validation, Production Deployment, and Scale & Reuse. Each phase includes specific activities and deliverables to ensure readiness before progressing. For example, Discovery focused on defining the business need and estimating ROI, while Feasibility assessed data quality and model fit.
A new phase was introduced to address team formation, with clear role definitions for the pilot build and validation phases. A typical team includes a project manager, product owner, business partner, data scientists with AI/ML expertise, and technology engineers for application and integration support. This step ensured teams were aligned before development began.
The pilot phase involved developing an MVP risk monitoring model and conducting internal demos to gather feedback. In the validation phase, an evaluation rubric was used to test the model against analyst judgment and refine performance through multiple iterations. The process emphasized user feedback and operational integration over pure model accuracy alone.
Once validated, the framework and pilot outputs were documented to support scale and reuse. Artifacts such as the use case charter, team RACI, and validation rubric were templatized. A reuse kit was created to accelerate future implementations, especially for use cases involving similar risk monitoring concepts.