Research led by Chris Benner, Professor of Sociology, examines how artificial intelligence (AI) is changing the workplace and highlights the importance of equitable technological transitions. While much public discussion centers on job loss or productivity increases due to generative AI, Benner’s work draws from past technological changes and current developments in AI to suggest ways innovation can benefit workers now and in the future.
Benner notes that for sectors such as food service, agriculture, and personal services, there are significant gaps in understanding how technology can be used positively and how workers can participate in AI implementation. In an interview, he addressed concerns about AI replacing jobs: “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 emphasized that the impact of AI depends largely on who shapes its use: “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, immediate risks include algorithmic management and electronic monitoring leading to increased work intensity and reduced autonomy for employees. He explained that these issues echo earlier waves of automation but with new challenges posed by the scale and opacity of managerial power through AI.
Discussing sector-specific patterns and opportunities for implementing AI across industries such as service work and blue-collar jobs as well as professional occupations affected by large language models (LLMs), Benner said: “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, technical, and white-collar occupations.” He also pointed out that some essential roles—such as childcare or elder care—are difficult to automate because they rely heavily on relational skills.
Benner argued that generative AI could help make invisible skills more visible: “The problem is not that these jobs lack skill; it’s that we are bad at recognizing…quality in relational and interactive work. Ironically…AI could help here…by making tacit knowledge more visible without replacing human judgment.”
On broader social impacts tied to employment structures like health insurance or retirement benefits—which often depend on having a job—Benner stated: “AI underscores the need to rethink social supports tied to employment. This transition could serve as a catalyst to question why key social supports are linked to one’s job…” He suggested considering options such as an “AI universal dividend,” stronger social wages or universal access to healthcare decoupled from employment.
To ensure positive outcomes from workplace AI adoption Benner called for worker-centered innovation: “We need to focus on worker-centered innovation…using AI to support training…better scheduling…reducing administrative burden so workers can focus on relational or creative work.” He stressed the necessity of involving workers’ voices along with industry standards and public-interest governance rather than relying solely on market forces.
“Right now,” he added,“we should focus on expanding worker participation in AI design and deployment decisions… updating labor standards… investing in learning infrastructure… including community-based and employer-embedded learning.”
Benner concluded by noting that technology alone does not dictate the future of work: “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… around work in ways that center dignity, equity,and learning.”
