No one is announcing AI-driven restructuring. But look at what is actually happening. Reporting lines are shifting. Junior roles are being quietly absorbed or redefined. Competency models that mattered eighteen months ago are already outdated. Accenture cut 11,000 employees in a single quarter, announcing it would exit people on a compressed timeline where reskilling is not a viable path. Salesforce eliminated 4,000 customer support roles, citing AI efficiency. These are not isolated events. They are the visible edge of a restructuring that is happening across enterprises whether or not anyone has named it.
The hypothesis: AI adoption is producing organizational restructuring that proceeds substantially through informal role redefinition, task reallocation, and status renegotiation alongside formal workforce reductions, and the informal dimension of this restructuring is largely unnamed and undesigned.
Three Takeaways
First, informal role redefinition is a well-documented organizational phenomenon, and AI is accelerating it beyond the pace at which organizations can respond deliberately. Ilgen and Hollenbeck (1991) described how roles emerge through role sending, role receiving, and role making. AI accelerates this by changing what tasks are available, what skills are valued, and what contributions are visible. When a team lead starts using AI to generate analysis that a junior analyst used to produce, the junior role has been informally redefined whether or not anyone updated a job description. Accenture's CEO acknowledged this explicitly: the company found that strategy and structure were not aligned, and that reversing five decades of operating norms was required. Most organizations are experiencing the same misalignment but have not yet named it.
Second, the absorption of junior roles creates a knowledge pipeline problem that will not become visible for years. Ericsson, Krampe, and Tesch-Römer's (1993) research on deliberate practice demonstrates that expertise develops through structured engagement with progressively complex challenges. Remove the early rungs and you create a gap that compounds silently. Forrester found that only 16% of workers had high AI readiness in 2025, yet Gen Z, the cohort most capable of working with AI, is being disproportionately shut out as entry-level positions disappear. Part of setting effective boundaries between human and AI work is preserving the developmental pathways that grow human capability over time. An organization that automates away its apprenticeship structure has consumed its own future talent supply.
Third, competency models are lagging indicators, and the gap between what they describe and what work actually requires is widening faster than organizations recognize. McClelland (1973) pioneered competency-based assessment as a forward-looking predictor of performance. In practice, competency models codify the past. The WEF reports that skill gaps remain the primary barrier to transformation for 63% of employers, yet the dominant organizational response is education, not role or workflow redesign. When task environments shift faster than role definitions, you get dysfunction disguised as normalcy: people performing well against outdated criteria while actual work drifts in directions no one is measuring.
The Longer View
Labor economics, specifically skill-biased technological change (Autor, Levy, & Murnane, 2003), provides the structural frame. New technologies complement high-skill work while substituting for routine work, producing labor market polarization. AI extends this into cognitive work with a critical difference: the substitution is happening at the task level within roles, not at the role level. A single role may see some tasks augmented and others automated, making the restructuring invisible to traditional workforce planning that operates at the role level.
Organizational sociology, specifically DiMaggio and Powell's (1983) institutional isomorphism, explains why the restructuring stays unnamed. Organizations adopt similar structures because they are perceived as legitimate, not because they are optimal. Few organizations want to be seen as conducting AI-driven layoffs, so changes happen through attrition, absorption, and quiet redefinition. Informal restructuring bypasses the deliberate design process that determines whether human and AI capabilities complement each other or collide.
Developmental psychology, specifically Ericsson's deliberate practice research, grounds the pipeline concern. Expertise is acquired through structured, effortful engagement with tasks at the edge of current capability. When AI absorbs the tasks that once served as developmental scaffolding, the organization loses its internal mechanism for growing the expertise it will need in three to five years.
My Two Cents
The most dangerous aspect of an invisible reorg is that no one is designing it. When restructuring happens formally, there are conversations about role design, transition support, capability gaps. When it happens informally, those conversations never occur. Accenture's Julie Sweet said something remarkably honest: it is not the technology that is the biggest barrier, it is being able to get the mindset reorganized around how best to use it. Most organizations are experiencing the same realization without acting on it. The restructuring proceeds anyway, just without anyone at the controls.
Run an audit. Compare your current role descriptions and competency models to what people actually do day-to-day. Ask managers where tasks have migrated, which roles feel different, and where capability gaps are emerging. Make the invisible restructuring visible. Then decide whether the reorg that is happening is the one you would have chosen.
Read to Learn More
Academic: Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333.
Industry: World Economic Forum. (2025). The future of jobs report 2025.
References
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333.
Davenport, T. H. (2026, January). Companies are laying off workers because of AI's potential, not its performance. Harvard Business Review.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
Ilgen, D. R., & Hollenbeck, J. R. (1991). The structure of work: Job design and roles. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 2). Consulting Psychologists Press.
McClelland, D. C. (1973). Testing for competence rather than for intelligence. American Psychologist, 28(1), 1–14.
World Economic Forum. (2025). The future of jobs report 2025.