Gender bias in artificial intelligence systems remains a critical challenge, particularly in workplace applications where AI increasingly influences decision-making processes. Through an integrative literature review, the study examines the multifaceted role of AI in addressing recruitment practices and systemic bias, while critically evaluating the limitations and potential consequences of technological interventions. To complement the theoretical analysis with empirical data, a semi-structured questionnaire was administered to 47 professionals (23 from Brazil and 24 from Portugal) working in startup environments. The survey explores how gender influences AI tool adoption, trust levels, and perceptions of workplace equality. Findings highlight the importance of intersectional and inclusive design as a key strategy for mitigating bias and provide concrete examples of AI-driven solutions aimed at reducing gender inequalities in the workplace. By offering empirical evidence from startups and practical recommendations for organizations, this research contributes to the growing discourse on AI bias. The results underscore the urgent need for diverse stakeholder involvement throughout the AI development lifecycle to foster more equitable systems and prevent the reinforcement of gender bias and stereotypes.