How AI can empower women leaders in multigenerational workplaces

Women remain significantly underrepresented in senior leadership roles worldwide, even as artificial intelligence (AI) transforms how organizations train executives and manage talent. A new academic review argues that AI, if deployed responsibly, could help dismantle long-standing structural barriers and equip women to lead across increasingly complex, multigenerational workplaces.

The study, Empowering Women Across Generations: AI-Enhanced Learning for Inclusive Leadership Development, published in Administrative Sciences, outlines a conceptual framework showing how AI-driven learning tools can strengthen women's leadership development while bridging generational divides.

AI as a strategic lever for women's leadership

Despite global diversity efforts, women continue to hold fewer than 30 percent of senior leadership roles worldwide. Structural barriers persist across industries, including gender bias in recruitment and promotion, unequal access to leadership development programs, limited mentorship, and long-standing pay gaps.

These barriers are often embedded in organizational systems that were historically designed around male-dominated norms. Performance metrics, promotion pipelines and informal sponsorship networks frequently favor men. As a result, women aspiring to leadership roles face both overt discrimination and subtle second-generation bias.

The nature of leadership itself is changing. Digitalization has altered how decisions are made, how data is processed and how teams collaborate. AI-powered analytics now support strategic planning, talent management and operational forecasting. This shift creates both risk and opportunity for women leaders.

According to the study, AI-enhanced learning can serve as a corrective mechanism if implemented thoughtfully. Artificial intelligence can personalize leadership development pathways, identify skill gaps, support data-driven decision-making and expand access to training. In particular, AI-driven analytics can help organizations detect overlooked competencies such as emotional intelligence, ethical reasoning and relational leadership, skills that research increasingly links to effective leadership in uncertain environments.

The authors note that AI should not replace human judgment. Instead, it should function as a supportive partner. Women leaders, in this framework, are positioned as relational mediators who interpret algorithmic outputs, contextualize data and ensure that empathy and ethics remain central to decision-making. By doing so, they can challenge biased systems rather than reinforce them.

However, the study also warns that AI is not inherently neutral. Algorithms reflect the data on which they are trained. Without proper oversight, AI systems can reproduce gender bias in hiring, evaluation and promotion. The authors cite growing evidence that female-gendered AI managers are perceived more negatively than male counterparts, even when delivering identical outcomes. This underscores the need for transparent governance and ethical auditing.

Bridging generational divides in a digital workplace

The modern workplace is increasingly shaped by generational diversity. Organizations now bring together Baby Boomers, Generation X, Millennials, Generation Z and even emerging Generation Alpha employees. Each cohort holds different expectations about leadership, technology and work-life balance.

Older workers may prioritize job security and stability, while younger employees often value flexibility, digital fluency and rapid career growth. Millennials and Gen Z are generally more comfortable with digital tools, whereas older generations may experience technological anxiety or resistance. These differences can create tension and misalignment within teams.

The study argues that women leaders are uniquely positioned to manage this complexity, particularly when supported by AI-enhanced learning tools. Drawing on Social Identity Theory, the authors explain that generational divisions can create in-group and out-group dynamics. AI, when used transparently, can serve as a neutral platform that reduces identity-based conflict by focusing attention on shared data and collective goals.

The researchers introduce the concept of an AI–intergenerational tandem. In this model, AI-enabled learning environments are designed to address the specific needs, skills and expectations of different age groups. Rather than imposing uniform automation, leaders adapt AI deployment to generational preferences.

For example, AI-driven platforms can facilitate knowledge transfer between experienced senior employees and digitally fluent younger workers. Adaptive learning systems can personalize content according to career stage and technological comfort. In doing so, AI becomes a bridge rather than a divider.

The Technology Acceptance Model plays a major role in the framework. The model suggests that employees adopt technology based on perceived usefulness and ease of use. These perceptions vary across generations. Younger employees may demand seamless digital integration, while older employees require reassurance, training and clear value demonstration. Women leaders who understand these differences can tailor communication and training strategies accordingly.

Importantly, the study frames intergenerational collaboration not as a demographic challenge but as a relational opportunity. When managed well, diverse age groups enhance innovation, resilience and knowledge exchange. AI can support this process by reducing cognitive workload, improving data transparency and enabling reciprocal learning.

A conceptual framework for inclusive AI leadership

The article does not test hypotheses using primary data. Instead, it proposes a theoretical framework that integrates three established leadership and technology theories: Path-Goal Theory, Social Identity Theory and the Technology Acceptance Model.

Path-Goal Theory suggests that leaders are effective when they clarify pathways to goal achievement. AI tools can assist women leaders in identifying development needs, aligning training with organizational objectives and customizing support for diverse teams. In this sense, AI acts as a path clarifier.

Social Identity Theory explains how individuals derive identity from group membership, which can lead to bias and exclusion. AI, if ethically governed, can reduce reliance on subjective judgment and foster shared understanding across gender and generational lines.

The Technology Acceptance Model focuses on the factors that drive technology adoption. By enhancing perceived usefulness and ease of use, women leaders can increase trust in AI systems and encourage long-term engagement across age groups.

To demonstrate how the framework might function in practice, the study draws on three peer-reviewed case examples. One case highlights how AI-based text analytics can identify leadership skill gaps, particularly in areas such as AI literacy, ethical leadership and emotional intelligence. Another examines the risks of algorithmic bias and digital exclusion, stressing the importance of inclusive governance. A third explores how generational differences shape leadership style and technology acceptance, reinforcing the need for adaptive rather than standardized implementation.

Together, these examples show that AI can both empower and marginalize. The outcome depends on how leaders design, communicate and regulate its use.

The study outlines several managerial and policy implications. Organizations are encouraged to integrate AI-enhanced learning into women-focused leadership development programs. This includes AI-driven simulations that replicate workplace challenges, analytics that monitor leadership skill progression and intergenerational learning hubs that foster mentoring and knowledge exchange.

At the policy level, the authors call for ethical AI governance frameworks that prevent age and gender bias. They recommend regular AI auditing, transparent decision-making processes and mechanisms that allow employees to question AI-supported outcomes. They also advocate for intergenerational AI literacy funding to ensure equitable access to digital leadership training.

The researchers acknowledge limitations too. Because the study is conceptual, its framework requires empirical validation. Cultural norms, industry context and power distance may influence how AI-enhanced leadership functions in different regions. For instance, in high-power-distance cultures, hierarchical expectations may complicate participatory AI adoption. Future research should test the framework across industries and geographies, including comparative studies between regions such as the Middle East and Western economies.