Skip to main content
The framework

Training-to-Outcome, end to end.

Five phases. AI-powered where it matters. Measured from first contact to a verified 12-month employment outcome. The framework is documented at framework.worldinnovationleague.com and open source on GitHub.

Read the live docsSee the impact

Most workforce programs measure inputs: enrollments, hours, completions. They rarely measure what actually matters, which is whether someone got a job, kept it, and earned more than they would have otherwise. The Training-to-Outcome Framework was built to close that gap. It is an operating model for taking a learner from first contact to a verified 12-month employment outcome, with measurement baked into every phase.

There are five phases: targeted recruitment, cohort-based training, mentorship and community, work-integrated learning, and AI-powered job placement. Each phase has defined activities, outcomes, and metrics. What makes it work is the compounding effect. Structured recruitment feeds high-completion training. Training feeds mentored work experience. Work experience feeds an AI-augmented job search that produced an 82% interview rate in DTTP 2.0, against an industry baseline of 2 to 5 percent.

The framework was validated across 5 cohorts and 2 programs (DTTP 1.0 and DTTP 2.0), 790 participants, 87 employer partners, and $2.18M of public investment. Headline results: 75% completion, 532 work placements delivered, 50% job placement in four months (DTTP 1.0), 192 jobs tracked within 12 months (DTTP 2.0), and an 8.03x ten-year government ROI. This page is a summary. The full documentation, templates, and playbooks are at the live docs site linked below.

The 5 phases

From recruitment to verified outcome

Each phase is designed, measured, and replicable. Expand any phase to see the activities, outcomes, and metrics that validated it across 790+ learners.

  1. 1

    Phase 1 · Targeted Recruitment & Assessment

    2–4 weeks

    Reach talent through inclusive community networks, then assess readiness and role fit using structured, merit-based evaluations.

    Key activities
    • Outreach through inclusive community partner networks
    • Skills and motivation assessment
    • Role fit and track placement
    Outcomes
    • Ready-to-train cohort
    • Clear track placement per participant
    Validated metrics
    Equity-deserving share
    82%
    Women (DTTP 2.0)
    49%
  2. 2

    Phase 2 · Cohort-Based Technical Training

    12–16 weeks

    Industry-aligned curriculum delivered by expert instructors and validated by employer partners. AI-assisted learning accelerates competency.

    Key activities
    • Track-specific curriculum (AI PM, Engineering, Data, UX, Cloud)
    • AI-augmented practice, feedback, and code review
    • Weekly employer talks and project critiques
    Outcomes
    • Portfolio-ready technical skills
    • Demonstrated capstone artifacts
    Validated metrics
    Completion rate
    75%
    Completions
    612
  3. 3

    Phase 3 · Mentorship & Community

    Throughout program

    Every participant is paired with industry mentors and a peer cohort. Mentorship is the strongest predictor of outcomes.

    Key activities
    • 1:1 industry mentor pairings
    • Weekly group coaching
    • Alumni network access
    Outcomes
    • Professional networks in target employers
    • Confidence and role clarity
    Validated metrics
    Mentorship participants
    590
    Attendance
    87–94%
  4. 4

    Phase 4 · Work-Integrated Learning

    4–12 weeks

    Real client projects and hackathons give participants employer-validated work experience before they apply for jobs.

    Key activities
    • Employer-sponsored capstone projects
    • Hackathons with judged deliverables
    • Portfolio and case-study writing
    Outcomes
    • Verifiable work artifacts
    • Direct employer exposure
    Validated metrics
    Work placements
    532
    Placement rate
    97%
    Hackathon projects
    160+
  5. 5

    Phase 5 · AI-Powered Job Placement

    Up to 12 months post-program

    AI tooling for resume, interview prep, and targeted applications produces an 82% interview rate (16 to 40 times the industry baseline) and 192 jobs tracked within 12 months.

    Key activities
    • AI resume and profile tooling
    • Mock interviews with AI coaching
    • Targeted employer introductions
    • 12-month outcome verification
    Outcomes
    • Verified jobs at WIL employer partners
    • Long-term career support via alumni network
    Validated metrics
    Interview rate
    82%
    Jobs verified (DTTP 2.0)
    192+
    Government ROI (10yr)
    8.03×
Evidence

The results that validate the framework

These numbers are cohort-verified across DTTP 1.0 and DTTP 2.0.

82%
Interview rate with AI tools
vs. 2–5% industry baseline (16–40× improvement)
612
Completions
75 percent completion rate
532
Work experience placements
103% of the 518 target
50%
Job placement within 4 months (DTTP 1.0)
Who it's for

Built for anyone trying to close the tech opportunity gap

Employers

Hire job-ready tech talent with verified work artifacts. Use the framework to design internal apprenticeships, reskilling tracks, or partner-led cohorts.

Training organisations

Adopt the framework as your operating model, or drop in the AI-powered placement phase on top of your existing curriculum to lift interview rates.

Governments and funders

Fund a workforce model with measured 8.03x ROI, verified 12-month employment outcomes, and transparent cost-per-completion economics.

Dive deeper

Read the full framework documentation

Every phase, template, and assessment rubric is open and documented. Fork the repo or read the live docs.

Want to run this where you are?

If you are a government, funder, or training organisation ready to deploy the framework, we will share everything we have built.