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November 30, 2025

WIL Impact Report 2025

The 2025 report on the Training-to-Outcome Framework. 790 participants across 5 cohorts and 2 programs. 82% interview rate. 8.03x government ROI over 10 years.

Executive Summary

World Innovation League is a Canadian nonprofit. We built and validated an AI-powered Training-to-Outcome Framework that moves people into Canadian tech jobs. Across two programs (DTTP 1.0 and 2.0) and five cohorts, WIL has trained 790 participants, delivered 532 work-integrated learning placements, and tracked 192 verified jobs within 12 months of DTTP 2.0 graduation.

82% interview rate with AI-powered job placement tooling, against a 2 to 5 percent industry baseline. That is a 16 to 40 times improvement over the typical unaided tech job search.

This report documents the outcomes, methodology, and economics of the framework, and makes the case for replication with government and philanthropic funders.

Participant Impact

The WIL cohorts were designed to reach the talent the Canadian tech industry has historically missed, and then actually get them hired. Across 5 cohorts and 2 programs:

  • 790 participants trained across DTTP 1.0 and DTTP 2.0
  • 612 completions (75 percent completion rate)
  • 590 mentorship participants with 87 to 94 percent weekly attendance
  • 532 work experience placements (103 percent of the 518 target)
  • 160+ hackathon projects shipped by participants

Mentorship is the strongest single predictor of outcomes in the WIL data. Every participant is paired with an industry mentor and a peer cohort for the duration of the program. Alumni keep access to the network for life.

Cohort composition

DTTP 1.0 ran three cohorts from April 2023 to March 2024: Product Management, Web Development, and UX/UI Design. DTTP 2.0 ran two cohorts in 2025: AI Product Management (January to March) and No-Code & Web Development (May to August). In both programs, curriculum is co-validated by employer partners and taught by working industry practitioners.

Employment Outcomes

Workforce programs are usually measured by enrollment, not employment. WIL measures employment at 4 months, 12 months, and beyond.

  • 192 jobs tracked within 12 months (DTTP 2.0, verification ongoing)
  • 50% job placement within 4 months (DTTP 1.0)
  • 82% interview rate with AI tools (vs. a 2 to 5 percent industry baseline)
  • 87 employer partners (55 in DTTP 1.0, 32 in DTTP 2.0)
  • 15+ repeat employer partners

Interview rate is the single most important leading indicator of a workforce program. DTTP 2.0's 82% interview rate is an order-of-magnitude shift in what is achievable for candidates who don't have a traditional on-ramp.

Featured employer partners include the University of Toronto, SE Health, Critical Mass, and Neo Financial.

Who We Reach

WIL's cohorts are among the most demographically representative in Canadian tech training. Across both programs:

  • 82% inclusive cohort participation
  • 450 Black Canadians trained (the Canadian tech workforce is 2.6 percent Black)
  • 220+ new immigrants
  • 49% women participants (DTTP 2.0)

These numbers are not a byproduct of the program. They are the intent. Phase 1 of the framework (Targeted Recruitment and Assessment) is designed to reach community networks the tech industry has historically under-served, and to admit on the basis of readiness rather than prior credentials. The mechanism is merit: good training, real work experience, and AI tools that level the job search.

AI Innovation

DTTP 2.0 introduced AI tooling across the job-placement phase of the framework. The delta in outcomes is the headline result of the whole program.

  • AI resume and profile tooling. Every participant ships an AI-optimised resume, LinkedIn, and portfolio before the first application goes out.
  • AI-coached interview prep. Mock interviews with real-time feedback on content, delivery, and confidence.
  • Targeted application workflow. AI matches participants to real openings at WIL employer partners, replacing the spray-and-pray approach with fewer, better-fitting applications.

82% vs. 2 to 5 percent. The AI-powered job placement phase produced a 16 to 40 times improvement over the unaided baseline. No other single intervention in the WIL data produced a comparable lift.

Economic Impact

The economic case is the most compelling element of the framework for government and philanthropic funders.

  • $2,178,579 total program investment
  • $3,559 cost per completion
  • $4,095 cost per work placement
  • $36.7M+ estimated lifetime earnings uplift across the 790 participants
  • $11M+ annual tax revenue generated by WIL graduates
  • 8.03x government return on investment over 10 years
  • ~$1.37M (63%) of total investment came from the Canadian Digital Supercluster

$2.18M invested. $36.7M+ in lifetime earnings uplift. 8.03x government ROI over 10 years. By any standard of workforce economics, this is an outlier.

The cost per completion of $3,559 is a fraction of what comparable Canadian workforce training programs report per participant, and their outcomes are usually weaker.

Replicability

The WIL Training-to-Outcome Framework was designed to be replicated. All five phases (Targeted Recruitment, Cohort-Based Training, Mentorship and Community, Work-Integrated Learning, and AI-Powered Job Placement) have defined activities, outcomes, and metrics. Full documentation, templates, and playbooks are published at framework.worldinnovationleague.com. The source repository is on GitHub.

WIL is looking for replication partners. Governments, philanthropic funders, training organisations, and employer consortia can run the framework in new regions and for new populations. Funders looking at Google.org's AI for Government Innovation initiative or comparable programs will find a ready operating model, documented outcomes, and a team prepared to run it.

What we're asking for

  1. Government and philanthropic funders to underwrite replication in new Canadian regions and internationally.
  2. Employer partners to sponsor cohorts, run work-integrated learning projects, and hire from future WIL graduations.
  3. Training organisations to adopt the framework or integrate the AI-powered placement phase into an existing curriculum.

Methodology Note

All numbers in this report are cohort-verified. Employment outcomes are validated through direct employer confirmation or participant attestation with documentation. Lifetime earnings uplift and tax revenue estimates are derived from Statistics Canada wage data applied to the verified 12-month employment cohort over a 10-year horizon. The 8.03x ROI figure is the ratio of projected tax revenue to total program investment.

WIL welcomes independent review and will share raw methodology with researchers, funders, and governments on request. Contact the team via worldinnovationleague.com/contact.

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