This chapter examines the unequal adoption of AI technologies in the workplace, focusing on the challenges faced by women and marginalized groups. It delves into systemic biases within AI systems and the disparity in training opportunities based on demographics and income levels. The discussion highlights the need for inclusive practices and realistic expectations in integrating AI tools, urging organizations to consider the broader cultural context in their implementation efforts.
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While AI tools promise to revolutionise work, the reality is far more complex – and unequal. Guest Susi O’Neill shares her research into “inclusive AI,” revealing a stark 25% gender gap in workplace AI adoption, with women significantly less likely to use these tools than men. Income plays a massive role too: those earning over £100,000 are far more likely to receive proper AI training, creating a widening skills gap that reinforces existing inequalities. The problem isn’t just access – it’s deeply rooted in social conditioning, with “good girl conditioning” meaning women feel pressured to prove their worth through hard work rather than AI shortcuts.
Matt and Lisa probe deeper into the hype around AI productivity gains, which deserves serious scrutiny. Despite grand promises of saving 20 hours per week, many organisations are struggling to see real returns on their AI investments. Ironically, while leaders demand AI adoption, only 6% actually use these tools themselves daily. The consulting industry is making a fortune selling AI transformation strategies, but the fundamental question remains: are we optimising for genuine innovation or just doing inefficient work faster?
Perhaps most concerning is the risk of “brain rot” that Susie highlights – students using AI to write essays can’t remember what they wrote months later, and AI hallucinations remain a persistent problem. As AI systems reflect historical biases and cultural assumptions, we’re at risk of embedding inequality even deeper into our work processes.
