The Impact of Artificial Intelligence Implementation on the Commercial Success of Startups

Marat Ressin

doi:10.34021/ve.2026.09.01(4)

Authors

Marat Ressin York Entrepreneurship Development Institute, Toronto, Canada https://orcid.org/0009-0008-5676-6730

DOI:

https://doi.org/10.34021/ve.2026.09.01(4)

Keywords:

artificial intelligence, Canada, early entrepreneurship, financial sustainability, innovative idea

Abstract

This article examines the relationship between artificial intelligence (AI) implementation and the commercial success of startups, using Canada as an empirical case. While the role of AI in entrepreneurship and business innovation has been widely discussed in recent academic literature, there remains a noticeable gap in country-specific empirical studies that integrate macro-level statistical indicators with firm-level qualitative evidence on startup performance. Consequently, this study addresses the challenges and opportunities associated with AI adoption in early-stage entrepreneurial ventures operating within a highly developed innovation ecosystem. The primary objective of the research is to assess how investments in AI and research and development (R&D) are associated with the financial performance and sustainability of startups in Canada. To achieve this objective, the study employs a mixed-methods research design combining secondary statistical data analysis with survey evidence and semi-structured interviews conducted among AI-integrating startups across multiple sectors. Quantitative analysis is based on descriptive statistics, correlation analysis, and regression modeling, while qualitative insights are derived from surveys and interviews with startup founders and executives. The findings reveal a strong positive association between internal R&D expenditures – particularly those related to AI – and operating profit among Canadian startups. Qualitative evidence further indicates that AI adoption contributes to business process optimization, improved investment attraction, and enhanced forecasting accuracy. At the same time, the results highlight the importance of contextual factors, including regional innovation clusters, access to skilled talent, and institutional support mechanisms. Overall, the study contributes empirical insights into the role of AI in startup development and offers policy-relevant implications for strengthening innovation ecosystems and supporting sustainable entrepreneurial growth.

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