The Skills Mirror: An Analysis of Trends, Tensions, and Opportunities of Proposed Projects to FSC

The Issue

Canada’s skills development ecosystem is operating in a period of rapid change shaped by technological disruption, demographic shifts, climate commitments, and economic uncertainty. Funders like the Future Skills Centre receive hundreds of proposals aimed at addressing these pressures, yet there has been limited systematic analysis of what these proposals collectively reveal about ecosystem priorities, gaps, and approaches.

While individual proposals are assessed on their own merits, taken together they represent a rich, largely untapped source of insight into how researchers, service providers, employers, and educators conceptualize the future of work. Without synthesis, opportunities are missed to understand patterns in innovation, identify blind spots, and assess how funding structures shape project design and ambition.

The Future Skills Centre invested in this project to address that gap.  By analyzing nearly 900 proposals submitted to the Centre in late 2024, the project sought to generate a data-driven, ecosystem-level perspective on the challenges being prioritized, the interventions being proposed, and the methods being used to design and deliver those interventions. This insight is critical for informing future funding strategies, strengthening alignment across the ecosystem, and supporting more effective labour market innovation.

What We Investigated

The Behavioural Insights Team (BIT) analyzed approximately 894 proposals received by the Future Skills Centre in late 2024. The proposals that form the basis of the analysis come from a series of funding calls issued by the Future Skills Centre from September to October 2024. Collectively, these proposals represent a rich set of largely unstructured data that offers insight into:

• The central issues that the skills development sector is identifying;
• The interventions the sector believes are most valuable in addressing those issues; and
• How the sector develops and implements interventions. 

The project used a mixed-methods approach combining machine-assisted analysis and manual methods to conduct a content analysis of the proposals. The methods included topic modelling, artificial intelligence (AI)-assisted summaries of selected content using software called NotebookLM, and traditional (manual) thematic analysis. 

Topic modelling techniques were applied to proposal summaries to identify clusters of similar projects, while large language model tools were used to explore cross-cutting themes related to challenges, interventions, and methodologies. All machine-assisted outputs were manually reviewed using traditional qualitative thematic analysis for validation and sense-making to ensure accuracy and contextual understanding.

The analysis covered proposals from approximately 300 organizations across Canada and spanned multiple sectors, populations, and regions. This approach was chosen to make sense of a large and diverse dataset that would be challenging to analyze using traditional qualitative methods alone, while still grounding findings in expert judgment and sector knowledge.

What We’re Learning

The proposals are not a perfect representation of the entire ecosystem
The timelines, the Future Skills Centre’s focus areas, budget guidance, and other requirements set out in the funding calls shape the proposals and provide important context and limitations in the analysis. For example, issues that cannot be even partially addressed and interventions that cannot be advanced within a one-year project timeline would not be represented in the data. The proposals are also not fully representative; while a diverse set of about 300 Canadian organizations applied for funding, this is not comprehensive of all relevant organizations. Further, they are proposals, documents aimed at securing funding – not objectively describing future skills needs and opportunities. 

The proposals demonstrated broad and consistent engagement with issues related to equity, digital skills (particularly AI), and addressing sector-specific skills gaps. Conversely, there was less consistent focus on system navigation, foundational skills, and employer or managerial practices.

Equity and inclusion are central organizing priorities across sectors
Proposals consistently focused on addressing inequitable access to skills development and job opportunities among Indigenous peoples, racialized youth, newcomers, people with disabilities, and women. Many of the projects focused on addressing systemic barriers through culturally relevant training and support resources, access to mentorship and networking, and improving policies and practices that actively constrain labour market access for underserved groups.

Sector-specific skills gaps are tightly linked to economic transitions
Many projects addressed industry-specific skills gaps arising from the net-zero transition, skilled trades shortages, healthcare pressures, and emerging bio-economy sectors, emphasizing the link between training design and employer needs. 

AI and digital skills are recognized as a transformative force in innovation
Although there is some concern about understanding and responding to the disruptive effects of AI on specific sectors, proponents were generally optimistic about the potential for AI to increase productivity and solve a range of issues across almost all industries and types of organizations. There was a strong focus on building confidence and increasing resource capacity to effectively build AI adoption and skills among workers and businesses. 

Engagement strategies and stakeholder gaps
Across the board, proponents prioritized engagement strategies designed to understand problems, develop solutions, and evaluate interventions. However, participatory approaches are common but uneven. While many projects engaged end users and community organizations, fewer involved policymakers, regulators, or large employers, limiting system-level leverage. An opportunity was identified to deepen engagement with users and communities by moving beyond mere consultation toward co-design.

Systemic Ambition vs. Implementation
The analysis revealed a tension between ambitious, systemic change-focused projects (such as new models for skills assessment) and implementation-focused training programs. Systemic projects often lacked concrete implementation plans beyond the funding period. Implementation projects tended to follow traditional design and delivery models. This tension is likely attributable to the parameters of the funding calls, specifically the one-year delivery timeline. These tight windows also appeared to hinder long-term planning; many proponents lacked systematic sustainability plans and robust theories of change. This was especially evident in innovative projects, which frequently lacked a clear vision for institutionalization or scaling.

Why It Matters

This project offers a new and important set of data for the skills ecosystem of funders, researchers and service providers to engage with. The analysis surfaces both strengths and gaps that are not visible when projects are viewed in isolation.

For policy and funding design, the findings highlight how call structure – especially timelines and eligibility criteria – directly shapes both the challenges organizations choose to address and the ambition of their proposed solutions. Short timelines tend to favour concrete, implementation-ready programming, but can constrain projects aimed at systemic change and long-term evaluation. This tension reflects the design of funding calls themselves and has important implications for the durability, scalability, and long-term impact of the initiatives they support.

To address this imbalance, funders can consider a “portfolio-based” approach that delineates long-term funding streams for innovation projects that address structural and systemic changes in the labour market; and shorter-term funding for projects that address urgent needs.

State of Skills:
Working Collaboratively to Close Skills-Related LMI Gaps

Labour market information (LMI) is a key pillar of a well-functioning workforce development system, yet considerable gaps remain in the generation, dissemination and uptake of skills-related LMI in Canada.

Read Thematic Report

For ecosystem partners, the analysis highlights where innovation is already strong, particularly in equity-centred programming, employer-aligned training, and digital skills development. At the same time, it points to opportunities to deepen work on foundational skills, system navigation, job quality, and management practices – areas that are critical to long-term labour market resilience but less visible in current proposal patterns.

More broadly, this work supports the Future Skills Centre’s mandate to generate and mobilize actionable insights about what works, for whom, and under what conditions. By turning proposal data into shared learning, the project helps align policy, funding, and practice around the skills challenges that matter most for Canada’s economic and social future.

What’s Next

This analysis establishes a foundation for ongoing, iterative use of proposal data to inform funding strategy and ecosystem learning. The Future Skills Centre may build on this work by applying similar methods to future funding rounds, using insights to shape targeted calls, and sharing findings with partners to strengthen collective impact. At this stage, activities are focused on dissemination and discussion of the findings broadly.