Studies warn women are bearing the hidden cost of workplace automation through adoption gaps and evaluation bias

Recent data from research centres and workplace surveys reveals a widening structural divide in how women interact with generative AI, extending beyond individual hesitation to disproportionate risks spanning performance evaluations, job security, and privacy. While a Pew Research Center study showed that women are more skeptical than men, less likely to expect the technology to improve their quality of life, and more likely to warn of its societal harms, a Randstad survey of 12,000 employees found that 71 percent of men have practical experience with AI tools compared to only 29 percent of women, with male employees receiving training opportunities and advanced tools at significantly higher rates.

The dilemma extends beyond access and training to the standards of judgment within organisations.Research evaluating CVs and software code shows that women receive lower competence ratings than their male peers when completing identical tasks with AI assistance, imposing a double penalty that impedes their professional advancement. In addition, large language models, by their predictive nature, reproduce historical patterns and biases embedded in their massive training datasets, associating women with domestic and secondary roles while attributing leadership and professional roles to men, and attempts to correct these biases frequently lead to new stereotypical distortions.

These burdens are compounded by the concentration of the female workforce in roles most vulnerable to administrative automation and customer service compared to industrial sectors, alongside their declining representation in intelligent systems development, where their share of US hires did not exceed 26 percent in 2025. Female employees also bear an invisible cost known as emotional labour, as frontline human staff shoulder the responsibility of correcting chatbot and voice assistant failures and de-escalating customer frustration to rebuild lost trust, in addition to concerns over privacy, model retention of sensitive personal data, and the proliferation of non-consensual deepfakes aimed at intimidating women, with a global study showing that 45 percent of female journalists practice self-censorship as a result of digital violence.

This reality requires institutions and companies across the Gulf, Egypt, and the Levant to readjust their digital transformation plans. Relying on indiscriminate deployment of intelligent tools without consistent disclosure policies risks burdening customer service and operational support teams with the errors of automated systems, while depriving junior talent of career progression pathways as entry-level positions are eliminated. Avoiding these divides requires comprehensive corporate training that explains model mechanics and biases, clear guidelines for technology use, and rewarding employees for human oversight and critical verification rather than merely promoting speed of adoption.

Heeding professional warnings and research data is an essential step to protect organisations from unintended discriminatory decisions and operational errors.Success in deploying AI is not measured by the scale of human workforce replacement, but by establishing oversight governance that ensures transparency and fairness in evaluationand gives teams sufficient room to guide the technology and review its outputs with precision and accountability.