Despite $173M in new funding, critics say Canada's Women Entrepreneurship Strategy ignores AI's role in capital allocation. The gender gap remains stagnant at 18%.
Canada is pouring another $173 million into its Women Entrepreneurship Strategy. The money breaks down into three chunks: $59 million for a loan fund that caps individual loans at $50,000, $100 million for the WES Ecosystem Fund, and $7 million for the Knowledge Hub that tracks best practices. The government announced the package Monday.
April Hicke, founder of Toast, a talent collective focused on gender diversity in tech, called the funding misaligned. "This is a strategy built for the barriers women faced in 2018, announced in 2026, and the single biggest force reshaping who gets capital and which businesses survive isn't anywhere in it," she said. Hicke pointed to artificial intelligence as that missing force. AI, she argued, now determines who receives investment and who does not. She praised the loan and ecosystem funds but said she wanted to see an AI fluency stream added. The Knowledge Hub, she added, should track AI adoption and displacement along gender lines.
The numbers show progress has been slow. A 2025 report from the WES Knowledge Hub reported that women majority-owned small and medium-sized businesses accounted for under 16% of the total in 2017. By 2023-2024 that figure had crept to just under 18%. Men majority-owned more than 60% of such businesses. The 2018 mandate behind the WES was to double the rate of women-owned businesses by 2025. That target was not met.
The stagnation comes with a measurable cost. Isabelle Hudon, president and CEO of BDC, told the Canadian Chamber of Commerce in 2025 that closing the gender gap could have added $150 billion to $180 billion to Canada's GDP.
Hicke's critique lands at a moment when venture capital deployment is increasingly tied to AI startups. Absent a deliberate AI component, she said, the strategy risks funding businesses that operate on yesterday's capital-access rules. The Knowledge Hub's $7 million allocation, she added, should be steering toward gender-disaggregated data on AI adoption and displacement, not general best-practice templates from 2018.
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