Mutual of Omaha: modernizing regulated policy and claim-data workflows
Mapped high-volume correspondence workflows, improved data and exception handling, and coordinated delivery across more than 50 business teams.
- My role
- Product owner for workflow, data, prioritization, UAT, and stakeholder alignment
- Timeframe
- Enterprise platform modernization
- Evidence boundary
- 10% is the validated broader platform efficiency result; 5% is the broader cost reduction result.
The operating problem
Teams across acquisition, underwriting, policy service, billing, and claims used an enterprise platform to assemble regulated customer communications. Manual data and content assembly created rework, inconsistent handoffs, and a growing stream of ad-hoc requests.
How I worked
I mapped the flow from policy and claim-data inputs through content selection, document generation, batch processing, printing, and dispatch. That revealed the highest-volume manual touches, recurring defects, unclear ownership, and exception paths.
I converted the findings into prioritized requirements, data rules, acceptance criteria, UAT scenarios, and explicit exception handling. I also replaced fragmented request handling with a visible Jira intake and prioritization mechanism so teams could see the decision, tradeoff, and owner.
Difficult tradeoffs
- Efficiency versus communication risk: lower-cost printing changes were segmented by volume and risk instead of applied indiscriminately to sensitive policy and claim communications.
- Urgency versus durable process: I moved stakeholders from private escalation and heroic coordination toward one visible intake mechanism.
- Delivery versus trust: after a delayed ad-hoc launch damaged confidence, I owned the gap and changed how decisions, risks, and involvement were communicated.
Outcome
The broader platform work improved efficiency by 10% across workflows used by more than 50 teams and contributed to a 5% broader operating-cost reduction. The exact contribution of individual changes is not presented as separately measured.
What I learned
Operational automation needs data ownership, exception paths, adoption measurement, and UAT coverage at the beginning—not after the happy path works.