AI is creating extra work for middle managers and women

It’s a common refrain that AI is changing how we work, but new research shows that AI is actually creating new forms of work and reconfiguring employees’ lived experience of work itself.

In July 2026, the the Notre Dame-IBM Tech Ethics Lab, housed in the Institute for Ethics and the Common Good, convened two workshops in collaboration with All Tech is Human, a nonprofit organization dedicated to bringing people together to tackle difficult technological questions and advance a technological future aligned with overall public interest. One workshop brought together mid-level professionals tasked with implementing AI on their teams, while the second convening featured senior and executive leaders making decisions about AI adoption across the organization. Both groups were asked a mirrored set of questions to assess understandings of how AI is reshaping their work or workplace.

The findings illuminated disparities in overall views and experiences of AI adoption based both on individuals’ positions within organizations and their identities and roles beyond the workplace. These findings, along with key recommendations for organizations looking to support employees in these transitions, are published in a new white paper released this week.

“While we continue funding longer-term research on AI’s impacts on workers and workplace dynamics, Sara Berger [IBM director of the Lab] and I felt a sense of urgency to understand how people are experiencing these transitions right now,” says Megan McDermott, Notre Dame director of the ND-IBM Tech Ethics Lab.

“What we learned about things like grief, invisible labor, and this fundamental gap between how leaders and workers describe what’s going on in their organizations are insights that matter and are important to illuminate in the present moment.”

With the advent of generative and agentic AI, both cohorts reported speed and productivity increasingly becoming the most important values at work, with overall standards decreasing and an attitude of “good enough” work becoming the norm across the organization. At the same time, workshop attendees reported that rolling out AI across the organization created a new subset of tasks essential to AI transformation, which were often not tracked in formal job descriptions, performance reviews, or key organizational performance metrics.

Examples of these hidden tasks include validating and reviewing AI outputs, translating high-level strategy into practice, cross-functional coordination, and employee training. Collectively, researchers referred to this as “glue work.”

Middle managers frequently reported taking on this extra work, with some participants describing new labor falling disproportionately to women across the organization.

Simultaneously, executive leaders questioned whether AI-enabled organizations would need the middle management tier in the future, while also worrying about “distributed deskilling,” the loss of formal and informal avenues where workplace expertise develops, like mentoring, knowledge transfer, and training. The workshops exposed a growing tension between the two viewpoints: the skillsets executives feared losing were already being performed, often invisibly, by the middle management tier whose utility was being questioned.

“A technology’s existence or capabilities doesn’t automatically determine what our AI future looks like—that’s a convenient, but powerful narrative created to offload responsibility,” says Berger.

“Nothing is decided for us. There are many futures in front of us, as a collective. Leaders, however, do shape these futures, whether they acknowledge it or not. The real questions are whose voices get incorporated into these visions, and what (or who) we are actually optimizing for when decisions get made about what work matters and why.”

Participants in the workshops were not all pessimistic, with some offering a vision of what better AI adoption could look like, ideally centered around greater organizational coherence and policies supporting human agency. Participants suggested that increased deliberation before adoption was key, including discussion on which aspects of work should remain human, protecting opportunities to fail and learn from mistakes, gains in efficiency returned to workers as time for higher-level work and more work-life balance, and quality standards that stay high regardless of AI use.

The final list of recommendations for business leaders that emerged centered two core themes: leaders must be more deliberate and transparent about what they measure and recognize, and they must be more thoughtful and intentional about who has a voice in the AI decisionmaking process. If and how leaders act on these themes, the findings suggest, may meaningfully shape what AI adoption at work looks like in practice, and how it’s experienced by the people it impacts the most.

To learn more about the findings, recommendations for leaders navigating AI transitions, and ideas for further research, read the full white paper.

Source: University of Notre Dame