AI excels at routine, repetitive and data-intensive tasks \u2013 scanning through thousands of records, scheduling logistics or identifying errors. Yet it still struggles with what we might call \u201cthe human edge\u201d. That is, creativity, empathy, judgement and collaboration. <\/p>\n
AI systems depend on people to train and evaluate their outputs. My research<\/a> found that when humans and AI collaborate, productivity rises \u2013 but when humans are excluded or fearful, the benefits collapse.<\/p>\n At cloud software company Workday, for example, nearly 60% of employees use AI tools to automate repetitive tasks. But far from reducing headcount, the company found<\/a> that AI freed people up to focus on the more thoughtful and creative parts of their job, as well as nurturing relationships with clients.<\/p>\n These findings align with my own research, which demonstrates that worker\u2013AI coexistence makes an organisation more resilient than automation alone.<\/p>\n So why are so many workers still afraid? Part of the reason lies in uncertainty. Organisations might implement AI systems without communicating clearly how they will affect jobs or performance evaluation. This lack of clarity breeds fear, rumours and resistance.<\/p>\n My studies show that when companies are transparent about how and why AI is being adopted \u2013 and when they involve employees in shaping its use \u2013 workers become more confident. They\u2019re even proud of their contribution to \u201cteaching the machines\u201d. But when employees are left in the dark, they tend to hoard information or disengage \u2013 the opposite of what innovation requires.<\/p>\n It\u2019s true that AI will disrupt many traditional roles. But the real challenge is not mass unemployment \u2013 it\u2019s misalignment<\/a>, that is, having the wrong skillsets for the AI age. The labour market must evolve faster to match emerging technological realities.<\/p>\n My previous study<\/a> on AI and the future of work was cited in a US government policy document. In the study, I described a \u201cperpetual race\u201d between human skills and machine capabilities. As AI automates certain functions, workers must continuously develop new abilities to stay relevant. <\/p>\n In effect, this is a strategic opportunity. The workers who thrive in the AI economy will be those who can interpret, guide and collaborate with intelligent systems.<\/p>\n That means companies must take responsibility for reskilling and upskilling. The UN\u2019s Sustainable Development Goal 8<\/a> makes it very clear that AI should benefit workers. If AI becomes a permanent treadmill rather than a partnership for shared progress, there is a risk of deepening inequality.<\/p>\n Social mobility in the age of AI<\/p>\n I recently shared research<\/a> with social mobility experts on how AI can be a catalyst for inclusion \u2013 if managed responsibly. By analysing skills rather than titles, AI-enabled hiring platforms can identify talent in overlooked communities \u2013 people who may not have formal qualifications but possess the right competencies to succeed.<\/p>\n Yet this promise comes with a warning. If the same systems are trained on biased data, they risk replicating social inequalities at scale. Responsible AI must embed fairness and human oversight from the start.<\/p>\n Ultimately, the companies that will lead the next decade are those that move from a technology-first to a people-and-purpose-first mindset.<\/p>\n
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