AI Employment Navigator
What job seekers and career planners use.
- Candidate profile + privacy choice
- Master resume
- Multi-source Job Watch
- Qualification screening
- Application tailoring
- Interview preparation
- Outcome tracking + learning loop
Lincoln, Nebraska • Executive Proposal & Implementation Hub
AI Employment Navigator
Demonstrate it locally. Implement it responsibly. Build it for replication.
A leadership-ready proposal to help American Job Center Lincoln translate AI literacy into practical employment services: participant navigation, master-resume development, realistic job matching, application support, interview preparation, staff training, governance, measurement, and long-term implementation.
Project origin
Concept, framework & prototype development: Ryan Uher
Proposed role: Project Lead & Framework Developer
Institutional roles, ownership, branding, funding and external use remain subject to appropriate City/AJC review and approval.
Executive proposition
One initiative. Two connected components. A clear path from proof to implementation.
The combined proposal separates the participant-facing service from the implementation capacity needed to make it consistent, governable, measurable and sustainable.
AI Workforce Implementation Pilot
What leadership needs to make the service operational.
- Dedicated implementation ownership
- Staff + participant AI literacy
- SOPs and approved workflows
- Responsible-AI safeguards
- Evaluation + quality assurance
- Funding + sustainability
- Replication documentation
Leadership-ready language
AI Employment Navigator is proposed as a workforce-service modernization initiative—not a software purchase. Lincoln would first test the participant service in a controlled demonstration, then use defined evidence, governance, cost and implementation criteria to determine whether a longer implementation phase is warranted.
Participant service model
A personalized employment navigation layer—not another job board.
The Navigator is designed to meet the job seeker at the front door, provide immediate value, and connect the person to deeper workforce services when appropriate.
Candidate Profile
Experience, skills, education, transferable strengths, salary, geography, interests and exclusions.
Job Watch
Multi-source discovery that compares current openings against the participant's real criteria.
Application Assistant
Truthful master-resume development, job-specific tailoring and application support.
Interview Coach
Employer research, likely questions, STAR preparation and participant practice.
Outcome Tracker
Lead → application → interview → offer → placement.
Learning Loop
Use real results to improve targeting and implementation quality over time.
1Walk in“I need a job.”
→
2Build NavigatorProfile + AI literacy
→
3Search + coachImmediate value
→
4Identify needTraining / barrier / credential
→
5Refer when appropriateProgram pathway
→
6EmploymentTrack outcomes
Universal Navigator
Candidate profile, Job Watch, AI literacy and independent continuation before intensive enrollment is necessarily the first conversation.
Program-Enhanced Navigator
Add career planning, training pathways, supportive services, barrier reduction and ongoing program support where rules allow.
AI Job Search Stations
Turn Resource Room technology access into a guided, structured AI-assisted employment service.
Implementation capacity
AI implementation is not a one-time training event. It requires ownership.
If the 30-day demonstration warrants continuation, the proposal moves into a 12–18 month implementation phase with dedicated accountability for adoption, training, governance, measurement and sustainability.
PROPOSED IMPLEMENTATION FUNCTION
AI Workforce Implementation & Employment Specialist
A workforce-development implementation role—not a software-engineering role.
Proposed implementation lead: Ryan Uher Subject to organizational approval, classification and funding.
12–18month implementation pilot
1.0FTE proposed capacity
Evidencebefore expansion
Implement
Lead Navigator, Job Watch and approved AI-assisted career-service workflows.
Train
Teach job seekers and staff practical AI literacy, prompting, verification and responsible use.
Standardize
Create SOPs, guides, workshops, privacy boundaries and human-review procedures.
Evaluate
Test tools, document failure points and determine which capabilities create actual workforce value.
Measure
Track adoption, lead quality, applications, interviews, placements, time and errors.
Sustain
Develop funding, partnership and replication strategies for long-term implementation.
Leadership-ready language
Dedicated implementation capacity is proposed as a project requirement because responsible AI adoption requires continuous training, standardization, governance, evaluation, measurement and improvement. The request is to fund and govern an implementation project that requires accountable staffing—not simply to create a position.
Demonstration + evidence
Prove the service model before scaling it.
30-Day Demonstration
~10 universal-access job seekers + ~10 selected program participants. Test workflow, safeguards, usability, lead quality, staff impact and early outcomes.
Continue, modify or stop
Review participant value, operational feasibility, governance, risk, cost and strategic value.
12–18 Month Implementation
Train, standardize, measure, improve, establish funding, and build a sustainable operating model.
Ready to share?
Determine whether evidence, documentation, cost and governance support external replication.
HallucinationVerify against original posting/source before action.
Fabricated contentMaster resume uses only participant-provided information.
PII / sensitive dataMinimum-data design, privacy mode and approved-platform rules.
Bias / screeningJob-related must-haves, participant choice and human review.
Eligibility errorsAI does not make benefits, sanction or eligibility decisions.
Vendor dependencePlatform-flexible methodology rather than one-vendor lock-in.
Stakeholder-ready briefing language
Use the same initiative. Change the emphasis for the audience.
These sections are deliberately written so leadership can reuse the language in briefs, emails, presentations and proposals.
Public-service modernization
Lincoln has an opportunity to test a locally developed, human-led AI employment model that expands workforce access while building privacy, accountability and measurable outcomes into the service from the beginning.
Future-ready workforce
If the evidence supports it, AI Employment Navigator can complement Lincoln's future-ready workforce agenda by showing how the City is preparing residents not only for changing jobs, but also for the changing tools people increasingly use to find and compete for those jobs.
Local innovation + measurable need
The demonstration provides a controlled way to test whether contextual AI literacy and guided AI-assisted employment services address emerging job-seeker needs, improve service quality and create a model worth broader workforce-system consideration.
Guidance + implementation evidence
Lincoln is considering a controlled demonstration of a human-led AI employment-service framework that embeds AI literacy into job-search preparation. We are seeking guidance on responsible implementation, evaluation, applicable workforce-system requirements, and how lessons could be shared if the demonstration produces value.
Fund the project—not just a position
The implementation request should be evaluated as a workforce innovation project with defined services, deliverables, safeguards and outcomes. Dedicated staffing is an implementation cost required to train, standardize, govern, measure and document the work.
Evidence before publicity
Potential future story, only after approval and evidence: Lincoln pilots a locally developed, responsible-AI employment model designed to help job seekers build AI literacy and navigate an evolving labor market.
Funding + sustainability
Start with existing infrastructure. Fund what implementation actually requires.
Existing workforce infrastructure
AJC staff, Resource Room, computers, NEworks and public job sources where allowable.
Existing workforce funds
Explore WIOA, Employment Service, One-Stop/Resource Room and related service costs only after fiscal/compliance review.
State innovation
Monitor workforce innovation, digital-literacy, technology and Governor's Reserve opportunities.
Competitive grants
Evaluate DOL/ETA, reemployment, AI-literacy, workforce-technology and public-sector innovation opportunities.
Partners
Higher education, foundations, employers and community partners may support curriculum, evaluation or access.
Funding allowability is not assumed. Each cost should be mapped to the underlying service function and reviewed by the appropriate fiscal, program, legal and grant-management staff.
Replication architecture
Standardize the framework. Localize the service.
The goal is not to make every AJC identical. The goal is to make the core responsible-AI implementation method reproducible while local systems retain control.
Human review • truthfulness rules • Navigator methodology • minimum metrics • implementation SOP • training • issue logging • fidelity checklist
Approved AI platforms • state workforce links • local job sources • industries • workshops • contact information • supportive-service pathways • branding
Privacy/security • accessibility • participant notices • program/fiscal use • retention • procurement • communications
Lincoln demonstration→Lincoln implementation pilot→Evidence + refinement→Selected validation→AJC replication toolkit→Voluntary wider replication
Leadership-ready language
The project is designed for replication across the American Job Center network, not for immediate national adoption. Lincoln would first establish evidence, implementation standards, cost assumptions and governance lessons before any external replication is considered.
Executive resources
Give the next stakeholder the package they need.
01
Executive Demonstration + Implementation ProposalLeadership-ready PDF for City, Board, DOL and executive review
↓
02Editable Executive ProposalWord version for internal adaptation and proposal development
↓
03Live AI Employment NavigatorParticipant-facing working prototype
↗
04DOL / ETA AI Literacy FrameworkFederal workforce-policy alignment
↗
05American Job Center LincolnExisting universal-access workforce-service environment
↗
06Lincoln Future-Ready WorkforceLocal workforce-development context
↗
Executive FAQ
Questions leadership is likely to receive.
Is this replacing NEworks?
No. NEworks remains part of the workforce system and can be included as a source. The Navigator adds a personalized discovery, preparation and coaching layer.
Is this a ChatGPT project?
No. The standardized asset is the methodology. The participant workflow is designed to work across organization-approved AI platforms.
Does this require a software procurement?
Not for the proposed demonstration. The service model should be tested using existing infrastructure and approved tools before major technology investment.
Why a dedicated implementation role?
Because adoption, training, safeguards, quality assurance, measurement, documentation and continuous improvement require accountable ownership if the demonstration moves into implementation.
Are we asking DOL to endorse it?
No. The proposed DOL posture is to request guidance on responsible implementation, evaluation and how useful lessons could be shared.
Are we claiming national adoption?
No. The proposal uses replication-by-design: establish local evidence first, then determine whether a toolkit is appropriate for voluntary replication.
Who developed the project?
The original concept, participant workflow, prototypes, implementation framework and initial pilot model were developed by Ryan Uher within American Job Center Lincoln. Institutional roles and ownership remain subject to organizational review.
Executive action
Lincoln first. Evidence first. Replication by design.
Authorize the demonstration protocol, identify governance partners, preserve documented project leadership, seek DOL/ETA guidance, and let the evidence determine whether the model moves into dedicated implementation.