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By Saba Rudsari and Stephen Carradini

“Will AI take my job?” It’s unclear, but labor conditions were challenging in 2025. 
In this post, Saba Rudsari, assistant professor of project management in the School of Applied Professional Studies, and Stephen Carradini, associate professor of technical communication in the School of Applied Professional Studies, discuss the current impact of AI on the workforce.

Both scholars work at the intersection of AI and the workforce. Saba Rudsari works at the intersection of artificial intelligence and project management, focusing on the responsible integration of AI with particular attention to adoption behavior, risk, ethics and human-centered leadership. In addition to leading the Arizona Water Chatbot team, Stephen Carradini researches the effective and ethical implementation of emerging technologies in workplaces, teaches digital ethics and helps train public sector employees to use workplace AI tools. Below, they discuss the current effects of AI on the workforce and what that might mean for new employees.

There has been an increase in U.S. unemployment since the arrival of ChatGPT onto workplace radars in early 2023: Official Bureau of Labor Statistics data reports a rise from 3.7% to 4.0% in early 2023 to approximately 4.6% by November 2025. It is unclear how AI plays into this unemployment rise. Prominent voices in the pro- and anti-AI spaces predicted and continue to predict oncoming mass layoffs; headline-grabbing companies (such as Amazon, Klarna, Shopify and Salesforce) reporting firings or staff rearrangements due to AI efficiencies seem to prove the point about oncoming labor struggles. However, some analysts have suggested that AI is being used as a rhetorical cover for other macroeconomic forces that are driving the labor market.

Beyond firings, many companies hired less frequently for early-career roles, potentially as existing employees with AI tools did the work of entry-level white-collar employees. Those existing employees switched roles less frequently, resulting in the term “job-hugging,” or employees keeping their jobs at all costs in fear of not getting another.

This lack of entry-level positions and use by existing employees potentially dovetails with the idea that AI will make existing workers more efficient. One survey conducted by the Federal Reserve Bank of St. Louis found that the application of generative AI helped workers save an average of 5.4% of their work hours. Research indicates that AI tools are also capable of improving the way employees perform, especially if the tools are used for augmentation rather than substitution. AI skills are highly in demand in hiring as well; knowing AI tools may help set job seekers apart from the pack.

While individual employees’ use of AI tools can produce strong positive outcomes, whole-organization use of AI has run into implementation problems. The promise of organizational AI is that AI tools are capable of processing larger amounts of data faster, thus helping organizations make better decisions. Yet some sources have reported stagnation or abandonment of uptake of AI in large organizations due to failed pilots and lack of institutional buy-in. Evidence from employee surveys suggests that many workers are still skeptical about the benefits of AI and fear job loss; a minority are actively using AI in their jobs. Thus, overall growth in professional AI use has not met analyst expectations by some metrics.

AI critics have grown very concerned about the potential of AI to replace humans, as they have seen AI invoked as a reason for a lack of entry-level white-collar positions. While social science studies do recognize that AI can improve efficiency, they also warn that irresponsible implementation of AI may impair job security, fairness, transparency and mental health — features that converge to ensure worker well-being. Philosophical and ethical analyses argue even further that AI at work raises serious concerns regarding autonomy, meaningful employment and social justice in issues like hidden labor, lost agency of workers and broadened socioeconomic inequality. Erosion of these aspects of labor can produce political and economic complications.

Critics further caution that over-reliance on AI could erode human cognitive skills and cut down meaningful training opportunities, especially for the less experienced. In addition, the inclusion of AI in university education has led to some concerns about what an AI-trained workforce will look like and what skills AI-trained students will possess.

The impact of AI on labor depends on how it is implemented, regulated and aligned with human values. When developed and deployed responsibly, AI can increase productivity and enhance decision-making in organizations. Without appropriate oversight, it can harden existing biases and reinforce inequality. To address these risks, some states have begun introducing regulatory measures governing the use of AI in employment contexts, such as subjecting automated hiring and decision-making tools to civil rights and anti-discrimination laws. The impact of these rules on employers, the practical experiences of workers implementing AI and whether employers will hire more often in 2026 remain important elements to watch regarding labor this year.

Those training new potential employees should prepare students to use AI tools in their individual professional capacities, as individual AI use can be an efficiency/productivity boon; being able to use AI is an in-demand skill that may also set job seekers apart from those who do not have AI skills in spaces where fewer early-career jobs are available than before (if 2025 trends hold). Students should also know that organizational AI use is highly variable at this time. They should be prepared to respond to a variety of possible conditions surrounding AI (acceptance, rejection or mixed) in a workplace.

Investors expect AI use to soar. That’s not happening 
The Economist (Nov. 26, 2025)
Recent surveys have shown a minority of respondents using AI in their professional work. Potential reasons include economic uncertainties, employees’ concerns over being put out of a job and negative perceptions about the organizational payoff for adopting AI.

What’s really going on with AI and jobs? 
Blood in the Machine (Nov. 6, 2025)
Amid record-breaking layoff reports, Amazon's mass firings and a slump in entry-level employment, this article asks: Is AI behind it all? This data-driven article finds that “management is using [AI] in various ways, both as a buzzy ideological framework and an actual automation technology, to achieve various ends, including but far from limited to job replacement.”

The Impact of Generative AI on Work Productivity 
Federal Reserve Bank of St. Louis (Feb. 27, 2025)
The analysis presents empirical evidence on how generative AI tools affect worker productivity, finding modest efficiency gains when AI tools are used to augment existing work rather than replace workers. The report helps contextualize claims about AI-driven efficiency with measured data.

Beware the AI Experimentation Trap 
Harvard Business Review (Aug. 29, 2025)
The authors caution that uncoordinated AI experimentation can limit organizational value and undermine trust. They emphasize the importance of governance, ethical oversight and alignment with human-centered leadership when deploying AI in workplace settings.