Designing, testing, and scaling
workforce systems for the AI economy
Our work moves across four connected areas. Research generates the evidence. Education turns that evidence into learning models. Workforce innovation tests those models with real employers. Policy and partnership carry the findings to the institutions and decision-makers positioned to act on them.
Research
We study how AI is changing tasks, workflows, professional judgment, and responsibility within specific occupations, drawing on practitioner interviews, organizational research, and field-based evidence. Our research tracks readiness, learning outcomes, workforce transitions, and career results, then integrates those findings into reference models and capability frameworks that improve as the evidence base grows.
Education
Research shapes what and how we teach. We translate findings into learning pathways, workshops, assessments, portfolio projects, and train-the-trainer resources, working alongside colleges and workforce partners to keep equitable access central to every design.
Workforce innovation
Real employer problems serve as the testing ground. We co-design pilots with organizations to assess whether our capability frameworks hold up in practice, measuring readiness, real-world application, hiring, retention, and advancement, and revising the models as new evidence emerges.
Policy and partnership
Research has limited value if it stays unread. We share findings through publications, practitioner guides, open educational resources, convenings, and nonpartisan policy guidance so institutions across sectors can put the evidence to work.
Connecting employer demand to equitable workforce mobility
Employers join as active research and implementation partners, providing the workplace settings where BRIDGE studies how professional work is changing and tests whether its models reflect actual demand. Participants build capabilities tied to that work. The research connects employer requirements with workforce preparation and feeds outcomes back into the next cycle.
- Role-specific capability frameworks and implementation models
- Co-designed workforce programs tailored to particular industries and occupations
- Capability assessments linked to documented hiring demand
- Access to talent that has completed employer-informed, role-specific programs
- A research and innovation partner, not a staffing agency
- Capabilities defined by documented employer demand
- Industry problems and occupation-relevant portfolio work
- Skills and portfolios that reflect what employers actually look for
- Stronger readiness for AI-enabled roles
- A clearer path from learning to projects, interviews, and career transition
From research to implementation
We build the evidence base, workforce models, and practical tools institutions need to adapt to how AI is changing work.
Capability frameworks
Role-specific and industry-specific models of what AI-enabled work actually requires, grounded in employer research and refined over time.
Learning pathways
Structured learning systems built around documented employer demand, with assessments, portfolio projects, and credentials integrated from the start.
Employer pilots
Co-designed workforce interventions tested inside real organizations, with outcomes tracked from readiness through retention.
Research publications
Working papers, peer-reviewed articles, and book chapters that add to both the academic literature and the practical knowledge base.
Implementation resources
Open educational resources, train-the-trainer materials, and practitioner toolkits designed for other institutions to adopt and adapt.
Policy guidance
Nonpartisan, evidence-based recommendations for policymakers, philanthropy, and public-sector leaders who invest in workforce development.