Analyzing the role of family socio-economic status, education and work characteristics in times of generative artificial intelligence and digital divide

<jats:sec> <jats:title>Purpose</jats:title> <jats:p>This study aims to explore the current state of generative artificial intelligence (genAI) in the workplace and discuss a potential digital divide in relation to genAI.</jats:p> </jats:sec> <jats:sec> <jats:title>Design/methodology/approach</jats:title> <jats:p>Using a quantitative approach, we study career-relevant predictors – family socio-economic status, education and work characteristics – and their relationship with different indicators of digital divide – access, genAI use, attitude toward AI and perceived AI literacy. To test our hypothesis, we used logistic and linear regression analyses. Additionally, latent profile analysis was conducted to identify patterns regarding work characteristics within the sample.</jats:p> </jats:sec> <jats:sec> <jats:title>Findings</jats:title> <jats:p>Among the 1,341 participants, 326 individuals were genAI users. Our results show that higher family socio-economic status, education and enriched and demanding work can be linked to a more positive attitude toward AI and higher perceived AI literacy. In the case of access and frequency of use, the results were mixed.</jats:p> </jats:sec> <jats:sec> <jats:title>Originality/value</jats:title> <jats:p>Our findings offer a novel contribution by examining a potentially upcoming digital divide in the case of genAI. We focus on how the career adaptation of the workforce might develop in the age of genAI. Importantly, we highlight that not all individuals may have an equal opportunity to adapt to genAI, which could hinder their future career development and reinforce patterns of inequality. Future research should address how to promote inclusivity and consider individual differences in adapting to genAI.</jats:p> </jats:sec>