Connecting AI-generated insight to governed, upstream process remediation.
Situation
A large national managed-care organization faced decentralized provider issue management across markets and business functions. Tools, data fields, and processes were inconsistent, visibility was local and manual, and repeat issues were often identified only after creating provider abrasion, rework, operational cost, and potential compliance exposure.
Leadership mandate
Design and operationalize an enterprise repeat-issue identification model combining standardized issue management, AI/ML-enabled intelligence, governance, and Continuous Improvement—so the organization could identify and eliminate recurring provider issues at scale.
Rod's role
Provided Senior Director-level leadership to transformation leaders designing the end-to-end operating model. Led the transformation strategy, future-state process, governance model, prioritization framework, stakeholder alignment, and phased deployment approach across Process Excellence, business SMEs, analytics/technology, and governance stakeholders.
Approach
Standardized issue framework, AI/ML clustering and predictive intelligence, impact-based prioritization, SME validation, enterprise governance, CI-led root-cause remediation, results validation, and control plans.
Designed outcome
A future-state Enterprise Issue Intelligence Ecosystem designed to shift repeat-issue management from reactive, associate-dependent discovery toward predictive enterprise identification and resolution—and to connect insight directly to improvement work rather than better reporting alone.