Artificial intelligence is increasingly heralded as a key part of humanity’s future. But we need to recognise that, as things currently stand, AI risks leaving women behind. Right now, men far outstrip women in terms of AI industry talent (especially at the senior management level), AI research and even consumer AI adoption.

This is concerning in light of the hopes that so many women have for AI to serve as a levelling – if not empowering – force for them in the marketplace. Will AI accelerate women’s rise to leadership, or will it reinforce the very barriers we seek to dismantle? The answer, as with most transformative technologies, is that it depends entirely on the choices we make today.

The opportunities AI can present to level the playing field for women in leadership are clear. Consider the recruitment process – traditionally a gatekeeping mechanism where unconscious bias has long limited women’s advancement. AI-driven screening tools, when properly designed, can evaluate candidates based purely on qualifications and potential, bypassing the subtle prejudices that have kept talented women from corner offices and boardrooms.

Khalifa University in Abu Dhabi. Educational programmes must actively recruit and support women in technical fields. Victor Besa / The National

The numbers are encouraging. Studies show that well-designed AI recruitment systems have successfully increased the hiring of female managers and reduced gender discrimination in leadership selection. By focusing on competencies rather than stereotypes, these tools can identify leadership potential that human recruiters might overlook.

Beyond hiring, AI offers women flexible ways to skill development through personalised learning platforms, virtual mentorship programmes and global networking opportunities. Yet, perhaps most powerfully, AI platforms can amplify women’s voices in ways previously unimaginable. Data-driven insights can illuminate once-invisible workplace inequities, providing the evidence needed to drive policy change. Advocacy becomes more effective when supported by irrefutable patterns and trends.

However, we must be clear-eyed about the risks. AI is not neutral; it reflects the biases embedded in its training data and the assumptions of its creators. When AI systems learn from historical data that reflects decades of gender inequality, they can perpetuate – and even amplify – those same biases. An algorithm trained on past promotion decisions may learn to replicate discriminatory patterns, presenting them as objective truth.

Ida Rhodes, the American mathematician, effectively provided the springboard for natural language processing. Alamy

As the nature of work is transformed due to new technologies, gender-based stereotypes can also pose a risk to women’s advancement.