# World Innovation League > World Innovation League (WIL) is a Canadian nonprofit. We run the Training-to-Outcome Framework, an open, AI-powered program that moves people into verified tech jobs. 790 participants across 5 cohorts and 2 programs. 82% interview rate. 8.03x government ROI over 10 years. ## Table of contents 1. What WIL is 2. The Training-to-Outcome Framework 3. Programs: DTTP 1.0 and DTTP 2.0 4. Impact and economics 5. Who we reach 6. Partners 7. For governments and funders 8. Origin story 9. Links --- ## 1. What WIL is World Innovation League is a Canadian federally incorporated nonprofit. Public name: World Innovation League. Short name: WIL. Primary domain: worldinnovationleague.com. Program domain: dttp.ca. Founder: Uchi Uchibeke. Federal corporation record: https://ised-isde.canada.ca/cc/lgcy/fdrlCrpDtls.html?p=0&corpId=12379481&crpNm=World%20Innovation%20League WIL runs the Training-to-Outcome Framework, an operating model for workforce development that has been validated across two programs and five cohorts in Canada. The framework is open source. The full documentation is at https://framework.worldinnovationleague.com/ and the repository is at https://github.com/World-Innovation-League/training-to-outcome-framework-and-best-practices. ## 2. The Training-to-Outcome Framework Most workforce programs measure enrollments and hours. 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. The framework has five phases: 1. **Targeted recruitment and assessment.** Outreach through inclusive community networks. Structured readiness and role-fit assessment. Admission on the basis of readiness rather than prior credentials. 2. **Cohort-based training.** Industry-aligned curriculum delivered by working practitioners. Portfolio-building assignments. Weekly employer talks and critiques. 3. **Mentorship and community.** Every participant is paired with an industry mentor and a peer cohort for the duration of the program. Alumni keep access for life. Mentorship is the strongest single predictor of outcomes in WIL data. 4. **Work-integrated learning.** Employer-sponsored capstone projects and hackathons with judged deliverables. Participants have real, verifiable work experience before the first job application goes out. 5. **AI-powered job placement.** Resume and profile tooling, mock interviews with real-time feedback, and AI matching applications to real openings at WIL employer partners. 12-month outcome tracking against employer records. What makes the framework 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 produces an 82% interview rate, against an industry baseline of 2 to 5 percent. ## 3. Programs: DTTP 1.0 and DTTP 2.0 The Diverse Tech Talent Program (DTTP) is WIL's flagship. It has run twice across five cohorts. ### DTTP 1.0 (April 2023 to March 2024) Three cohorts: - Product Management (October to December 2023) - Web Development (January to March 2024) - UX/UI Design (January to March 2024) Numbers: 500 participants trained, 420 completions, 55 employer partners. 50 percent of graduates had verified jobs within four months. This is the program where the Training-to-Outcome Framework was built and tested. ### DTTP 2.0 (September 2024 to August 2025) Two cohorts: - AI Product Management (January to March 2025) - No-Code & Web Development (May to August 2025) Numbers: 290 participants trained, 192 completions, 32 employer partners. 49 percent women participants. 94 percent inclusive cohort participation. DTTP 2.0 was WIL's first fully AI-powered program. Every participant used AI tools for learning, portfolio work, and job search. The 82% interview rate and the 16 to 40 times lift over the industry baseline came from DTTP 2.0. ## 4. Impact and economics - 790 participants trained across 5 cohorts and 2 programs - 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 across both programs - 192 jobs tracked within 12 months (DTTP 2.0, verification ongoing) - 50 percent job placement within 4 months (DTTP 1.0) - 82 percent interview rate with AI tools, against a 2 to 5 percent industry baseline (a 16 to 40 times lift) - 87 employer partners (55 in DTTP 1.0, 32 in DTTP 2.0) - 15+ repeat employer partners - $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 (about 63 percent) of total investment came from the Canadian Digital Supercluster 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. Featured employer partners include the University of Toronto, SE Health, Critical Mass, and Neo Financial. ## 5. Who we reach The framework reaches people the tech industry has historically missed. Across both programs: - 82 percent inclusive cohort participation - 450 Black Canadians trained, against a Canadian tech workforce that is 2.6 percent Black - 220+ new immigrants - 49 percent women participants (DTTP 2.0) These numbers are not a byproduct of the program. They are the intent. Phase 1 of the framework is designed to reach community networks the tech industry has 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. ## 6. Partners Training partners that co-deliver curriculum, mentorship, and work-integrated learning: Co.Lab, Skillhat, Atila, Riipen, Flidais, and the Founders Institute. Featured employer partners from the network of 87: University of Toronto, SE Health, Critical Mass, Neo Financial. Primary funder: Canadian Digital Supercluster (DIGITAL), which contributed about $1.37M (63 percent of total investment) across DTTP 1.0 and 2.0. ## 7. For governments and funders Public workforce programs typically measure enrollments and hours. They rarely measure employment. When they do, the data arrives 12 to 18 months late, long after a funder can do anything about it. Interview rates for most applicants sit at 2 to 5 percent. Funders cannot see whether their dollars produced jobs. WIL's Training-to-Outcome Framework closes that gap. It is open source. Governments and training organizations can deploy the whole framework or drop in individual components. WIL offers four modular components for government deployment: 1. **AI Talent Navigator.** Agentic AI that matches residents to the right training pathway, then to real openings at partner employers. Runs at population scale. 2. **Framework deployment.** End-to-end deployment of the five-phase Training-to-Outcome Framework. 3. **AI Job Preparation Toolkit.** Resume, cover letter, interview practice, and application matching. The same stack that produced the 82% interview rate. 4. **Outcomes dashboard.** Live visibility into enrollment, completion, interview, and placement, with cost per outcome and ROI built in. WIL is actively seeking replication partners: governments, philanthropic funders, training organizations, and employer consortia. The framework fits the Economy stream of Google.org's AI for Government Innovation initiative. Contact: info@worldinnovationleague.com ## 8. Origin story WIL started as NaijaHacks and then AfricaHacks, a pair of hackathon communities the founder ran to get more builders into tech. The hackathons worked. The harder problem sat one step downstream: talented people still could not find a clear, supported path from learning to a real job. World Innovation League was then incorporated in Canada to build that path. ## 9. Links - Home: https://worldinnovationleague.com/ - Programs: https://worldinnovationleague.com/programs/ - DTTP 1.0: https://worldinnovationleague.com/programs/dttp-1/ - DTTP 2.0: https://worldinnovationleague.com/programs/dttp-2/ - The Framework (summary): https://worldinnovationleague.com/framework/ - The Framework (full live docs): https://framework.worldinnovationleague.com/ - The Framework (GitHub): https://github.com/World-Innovation-League/training-to-outcome-framework-and-best-practices - Impact: https://worldinnovationleague.com/impact/ - 2025 Impact Report: https://worldinnovationleague.com/reports/impact-report-2025/ - For governments and funders: https://worldinnovationleague.com/for-governments/ - Partners: https://worldinnovationleague.com/partners/ - About: https://worldinnovationleague.com/about/ - Contact: https://worldinnovationleague.com/contact/ - Short summary: https://worldinnovationleague.com/llms.txt Contact: info@worldinnovationleague.com