A $1.2M enterprise talent development program built to upskill and cross-train claim professionals into advanced roles — at depth, within regulatory constraints, and with evidence of impact.
The Challenge
A regulated, multi-line enterprise faced a persistent shortage of mid-level talent across several specialized practice areas. The solution had to upskill current employees and cross-train them across business lines — within tight legal, regulatory, time, and budget constraints.
My Role
Led a cross-functional team of 12 to design, build, and run a $1.2M cohort-based development program from the ground up — from competency mapping and gap analysis through implementation, coaching, and iterative evaluation.
Promoted into advanced roles — before the program ended
The stated post-completion promotion goal
Average learner satisfaction — 5-point Likert scale
Pages per AI-generated mock claim scenario
The Approach
ADDIE
Analyze
Used backwards design to define the ideal candidate, then decomposed this target into skills, knowledge, and experiences — tagging each as line-agnostic or line-specific. Mapped every existing asset against this matrix and ran a gap analysis to scope the build.
Design
Architected a 9-month cohort with parallel tracks for shared and specialized competencies. Added a technical coaching layer to address competencies that couldn’t be covered within regulatory limits — such as hands-on experience in a different line of business.
Develop
Produced 25 reinforcement assignments, 18 assessments, 18 VILTs, three weeks of live learner training, and a three-day live coach training. Built a peer mock-file sharing program and used generative AI to scale 18 full mock scenarios at 200+ pages each — work that would have been infeasible at that depth otherwise. Authored reflection and discussion guides for experiential learning events (litigated proceedings, negotiations, role-shadowing). Managed other designers and integrated all content into a monthly objective map.
Implement
Ran the cohort with monthly checkpoints across all components — coach and learner surveys, focus groups, analysis of program work, assessment data, and unit manager observations.
Evaluate
Built automations to pull, organize, and visualize survey data monthly. Used a Claude project to code qualitative responses and surface themes. Pre/post skill assessments, rubric evaluations, and promotion data validated program success and drove ongoing refinement.
Skills Demonstrated
Program Leadership · Project Management · Gap Analysis · Curriculum Writing · Instructional Design · Quantitative & Qualitative Data Analysis · AI-Enabled Content Development