Much of the current discussion about generative artificial intelligence (AI) in the workplace focuses on concerns over job loss or increased productivity. However, Professor Chris Benner, a sociologist, emphasizes that lessons from past technological changes and recent research suggest innovation can improve work if managed equitably.
Benner highlights that sectors such as food service, agriculture, and personal services lack crucial frameworks for effectively integrating AI while ensuring worker involvement. In an interview, he stated: “It is unlikely that we’ll see a rapid loss of jobs in any single occupational category. Most new technologies, like in previous rounds of rapid technological change, are changing tasks, not complete jobs, allowing job activities to shift over time. And technologies don’t determine outcomes on their own, but institutional choices, business models, policies, governance, and power relations do.”
He further noted: “The key question is who gets to shape AI use in the workforce. The same AI tools can produce very different outcomes depending on who is involved in decisions about design, deployment, and governance. For example, we see instances where AI can either deskill work, intensify surveillance, and hollow out jobs, or it can augment workers, reduce drudgery, and improve job quality.”
According to Benner: “The most immediate risks aren’t mass layoffs but algorithmic management and electronic monitoring; increased work intensity and loss of autonomy; racialized and gendered bias in scheduling; evaluation; and discipline. These dynamics echo earlier waves of automation but AI scales and obscures managerial power in new ways.”
He observes that automation will likely increase for certain tasks within service and blue-collar jobs while large language models (LLMs) will have more impact on professional occupations involving writing or analysis. He explained: “I think we will see automation in service and blue-collar work for many tasks… However… many of the current changes will focus on professional… white-collar occupations.” Benner points out these roles may adapt more easily due to greater flexibility.
He also stresses that important social roles such as childcare or education cannot be fully automated but are often undervalued because their skills are hard to measure or reward. “Ironically,” Benner says,”generative AI could help here by helping us better understand communication… by supporting training…and by making tacit knowledge more visible without replacing human judgment.”
Benner argues that the rise of AI calls into question why essential social supports like health insurance remain tied to employment status: “AI underscores the need to rethink social supports tied to employment…. If AI produces broad productivity gains from that collective inheritance it raises a basic question of fairness: Why don’t we treat some of those gains as a shared social return? This opens the door to ideas like an AI universal dividend…”
On ensuring positive outcomes from workplace AI adoption he says: “We need to focus on worker-centered innovation…using AI to support training…better scheduling safety…and reducing administrative burden so workers can focus on relational or creative work.” He adds this requires industry standards public-interest governance—and expanded worker participation—not just market forces.
Benner concludes: “AI will not determine the future of work on its own. The real question is whether we treat this as another extractive technological transition — or as an opportunity to rebuild institutions norms and governance around work in ways that center dignity equity and learning.”
