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In Bangladesh, women’s participation in the workforce stands at 36.9%, compared to the global average of 49% (World Bank Data, 2024). Furthermore, women represented 8.9% of those employed in senior and middle management in 2022 (World Bank, Gender Data). Compared to global trends, the lower levels of women’s workforce participation highlight the need to reflect on how inclusive the current recruitment and working environment of firms truly are. Additionally, the underrepresentation of women in leadership positions calls for a closer examination of the career progression pipeline within corporate environments. This creates an opportunity to explore how AI can be integrated into HR policies to promote inclusivity and reduce bias.
Despite notable educational progress among girls, significant gender disparities persist in Bangladesh’s labor market. While girls have outpaced boys in general educational attainment, they remain underrepresented in higher education, particularly in STEM fields. In Bangladesh, we only have 21% female STEM graduates (Barriers to Bridges, 2024). This educational segregation translates into occupational segregation in the workforce, with women concentrated in lower paying, traditionally “female” sectors such as RMG, tailoring, handicrafts, and food industries (World Bank, 2021).
Source: ILO, ILOSTAT database. Data retrieved on June 15, 2021.
Female labor force participation (FLFP) remains significantly lower than male labor force participation (MLFP), with Bangladesh ranking 140 out of 156 countries globally (World Economic Forum, 2021). This low participation raises the need to reflect on recruitment-stage biases, where sociocultural norms, male-dominated recruitment networks, and limited access to high-growth STEM sectors constrain women’s entry into more profitable and high-growth employment opportunities.
Source: World Economic Forum 2021
Even when employed, women experience substantial wage disparities, earning only 40 percent of men’s incomes on average, with wider gaps for ethnic minority women (World Economic Forum, 2021). This wage gap is closely associated with retention-stage biases, including occupational segregation, slower promotions, and limited access to high-value roles within industries.
Women occupy only 8–9 percent of managerial roles (World Bank Gender data portal, 2022) while in our neighboring countries women participation in leadership position is much higher. This underrepresentation is also a consequence of retention-stage biases, where systemic workplace norms, limited mentorship, and the “glass ceiling” hinder career advancement for women.
Recruitment Biases: Female candidates face obstacles such as gendered perceptions, male-dominated recruitment networks, and limited access to high-growth sectors. Research in Bangladesh’s banking sector shows that women encounter barriers to professional advancement due to stereotypes, workplace bullying, and societal expectations, even when formal structures such as training and promotion opportunities exist (Ahmed & Akter, 2024). These recruitment biases directly contribute to the low female employment rate, limiting women’s initial entry into the workforce.
Retention Biases: Women encounter wage disparities, slower career progression, and restricted leadership opportunities due to systemic workplace norms and the “glass ceiling.” Studies show women in corporate settings face sexual harassment, delayed or denied promotions, long working hours, and insufficient supportive policies (Kamal & Sabrin, 2014). These retention biases are directly linked to the large gender pay gap and low female representation in leadership and managerial positions, as they prevent women from advancing and receiving fair compensation.
AI offers promising solutions to mitigate both recruitment and retention biases in Bangladesh:
Recruitment: AI-driven recruitment tools can anonymize candidate profiles, evaluate skills objectively, and track diversity metrics. Companies like Cisco and Oracle use AI to detect biases, automate processes, and monitor gender representation, ensuring fairer hiring decisions. Research shows that resumes with feminine names are less likely to receive callbacks even when qualifications are identical (PNAS), highlighting the potential of AI to level the playing field and increase female workforce participation.
In Bangladesh, Unilever Bangladesh uses AI-driven online games to assess traits like resilience and problem-solving at the first stage of recruitment. Candidates then submit video interviews, which are analyzed by machine learning to evaluate communication and behavior. These AI tools reduce manual screening and help ensure fairer, data-driven hiring decisions.
Retention and Career Progression: AI-enhanced KPI systems, like Unilever’s diginexLUMEN, track employee performance, conduct gender-gap analyses, and identify opportunities for promotion. Predictive insights improve collaboration, optimize productivity, and ensure fair evaluations, enabling women to overcome barriers such as the “glass ceiling” and access leadership roles.
These tools can help reduce the gender pay gap and improve female representation in managerial and leadership positions (BCG, 2024). Moreover, flexible, AI-focused skill development programs are helping smoothen women’s career paths by allowing learning at their own pace and time (AWS, 2024; Women in Cloud, 2024). Initiatives like Microsoft’s AI Skills for Women shows that accessible, industry-specific training empowers women to upskill while balancing work and personal commitments (Microsoft, 2025; ICAIRE, 2025). Such inclusive models enable more women to transition into high-value roles, reducing barriers to career advancement (Technovation, 2024).
Unilever Bangladesh integrates AI through data-driven dashboards that analyze employee performance and skills to identify women for promotion and leadership roles. AI tracks gender-related metrics and predicts areas needing intervention to meet 50:50 targets. It also suggests personalized learning and mentorship opportunities to help women advance. Unilever Bangladesh integrates AI through data-driven dashboards that analyze employee performance and skills to identify women for promotion and leadership roles. AI tracks gender-related metrics and predicts areas needing intervention to meet 50:50 targets. It also suggests personalized learning and mentorship opportunities to help women advance.
Although AI holds the potential to reduce gender biases in the workplace in Bangladesh, there are significant risks if not properly implemented. A key challenge is the lack of clean, structured data, which is crucial for building an effective AI system. In Bangladesh, obtaining such data is difficult, and this poses a barrier to successful AI implementation. Moreover, it is essential to ensure transparency regarding the data used to train the AI, as any biases in this data can impact the results. If biases are not addressed during the AI development process, they can be amplified over time, reinforcing the inherited biases of the developers (Forbes, 2023).
Additionally, Bangladesh faces a shortage of skilled professionals trained in AI, making it even more challenging to integrate AI into HR practices. Consequently, AI solutions are likely to benefit formal corporate sectors, such as banks and multinational companies, where structured processes and digital systems are in place. However, their immediate impact on the ready-made garment (RMG) sector and other informal economy workplaces—where most women are employed, remains limited. Policymakers and practitioners should therefore adopt AI as a complementary tool, rather than a stand-alone solution, and pair it with broader structural and social interventions to improve women’s workforce participation.
To conclude, AI has the potential to support gender-inclusive recruitment, particularly in formal corporate sectors like banks and multinational companies. However, it is not a universal solution: AI can replicate biases present in training data, and anonymized recruitment may clash with network-driven hiring practices prevalent in Bangladesh’s informal economy.
As most women work in the RMG sector and informal sectors, AI’s immediate impact there is limited. Therefore, AI should be viewed as a complementary tool, to be implemented alongside broader social and structural measures that address gender disparities in the workforce.
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