
Regional Data Connections – Cohort I Descriptions and Summaries
New Jersey
Problem/Opportunity
While New Jersey has robust data, current delivery methods limit the accessibility, usability, and real-time application of LMI. Key gaps include:
- Static datasets and complex reports are difficult for workforce boards to translate into actionable insights.
- Sub-state projections are underutilized.
- There is limited integration between long-term projections and real-time labor demand signals such as job postings.
New Jersey aims to reimagine LMI delivery at the regional level through interactive data visualizations, AI-enabled conversational tools, and structured feedback loops between state LMI producers and workforce boards.
Targeted Support Needed
- Technical assistance on dashboard development, data architecture, and combining projections with real-time job postings.
- Guidance on AI-enabled chatbot design, vendor selection, and responsible use of generative AI.
- User-centered design and stakeholder engagement support.
- Best practices from peer states developing LMI dashboards and AI tools.
- Support in developing scalable SOPs, technical workflows (e.g., R code), and replication documentation.
Definition of Success
A clear strategic plan and roadmap for an interactive dashboard and AI-enabled chatbot; a framework for integrating projections with real-time data; demonstrated WDB engagement; and scalable resources (SOPs, templates, technical guidance) replicable across regions.
What New Jersey Contributes to Other States’ Learning
New Jersey can share its approach to AI-enabled tools including chatbot design and strategic planning, replicable technical resources (R-based dashboard workflows, SOPs), and its model for structured feedback loops between LMI producers and workforce boards.
What New Jersey Hopes to Learn
Effective user-centered LMI tool design; how other states integrate real-time data with projections; scalable implementation practices around governance, procurement, and cross-agency collaboration.