Beyond the Org Chart: AI and the Transformation of Invisible Work
Pith reviewed 2026-05-22 05:09 UTC · model grok-4.3
The pith
AI adoption changes formal roles and informal mentoring practices in tech product teams.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Interviews indicate that AI is not only changing formal role responsibilities and collaborations between roles but also transforming informal cultural practices like professional mentoring, which help professionals settle in their positions, stay engaged with their work, and grow their careers, producing both smoother peer collaboration and risks to typical career growth opportunities such as receiving feedback and promoting leadership.
What carries the argument
Invisible work, specifically informal professional mentoring and cultural interactions that support career integration and growth, as revealed through self-reported changes after AI adoption.
If this is right
- AI blurs and extends formal corporate role boundaries for product professionals.
- Collaborations between different roles within product teams become altered by AI assistance.
- Informal mentoring practices decline or change in ways that affect how professionals integrate and advance.
- Peer collaboration becomes smoother in some cases due to AI-supported interactions.
- Opportunities for feedback from professional networks and for demonstrating leadership through mentorship are placed at risk.
Where Pith is reading between the lines
- Reduced informal mentoring could slow the integration of new team members and limit knowledge transfer across experience levels.
- Companies may need to create explicit recognition systems for mentoring contributions that AI tools are displacing.
- Similar shifts in invisible work could appear in non-tech sectors once AI adoption reaches comparable intensity.
- Preserving diverse thinking may require deliberate scheduling of informal interactions that AI workflows tend to compress.
Load-bearing premise
The self-reported experiences of 24 individuals at a single large technology firm reflect general transformations across AI-forward companies and stem primarily from AI rather than other concurrent organizational factors.
What would settle it
A multi-company study or longitudinal observation showing no meaningful decline or shift in mentoring frequency and quality after AI tool rollout, or attributing observed changes mainly to remote work policies or reorganization.
read the original abstract
An increasing number of news and research articles report that AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles. With the goal of understanding how work processes might be changing in an AI-forward company, we interviewed 24 product-focused individuals at a large technology firm about how AI has impacted their own work, their work within their product team, and their professional interactions. Our conversations suggest that AI is not only changing formal role responsibilities and collaborations between those roles, but also changing informal cultural practices like professional mentoring that are key to helping professionals settle in their positions, stay engaged with their work, and grow their careers. Some of these changes are positive, such as smoother collaboration between peers, but other changes are more nuanced and put the typical career growth opportunities, like receiving feedback from professional networks and promoting leadership and mentorship, at risk. We propose steps that AI companies can take to make the invisible work more visible. Additionally, we propose efforts that individuals and leaders can take to support their colleagues through AI transformation while preserving healthy company cultures that support diverse thinking, collaboration, and informal interactions.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents a qualitative interview study with 24 product-focused individuals at one large technology firm. It claims that AI adoption is reshaping not only formal role responsibilities and team collaborations but also informal cultural practices such as professional mentoring, with mixed effects: smoother peer collaboration alongside risks to career feedback, leadership visibility, and mentorship opportunities. The authors propose organizational and individual steps to make invisible work more visible and preserve healthy cultures during AI transformation.
Significance. If the attribution to AI holds, the work would usefully extend discussions of AI's organizational effects beyond automation to the transformation of invisible work and career-supporting practices. The practical recommendations for companies and leaders could inform AI-forward firms managing cultural change. The single-firm qualitative design inherently limits generalizability, but the focus on nuanced, self-reported shifts in mentoring and collaboration offers timely empirical grounding for the field.
major comments (2)
- [Methods] Methods: The selection criteria, interview protocol, and thematic analysis process are described only at a high level. Without explicit details on recruitment, question guides, coding procedures, or steps taken to mitigate interviewer bias, it is difficult to evaluate the interpretive rigor behind claims that AI specifically drives changes in mentoring practices.
- [Findings] Findings/Discussion: The central attribution—that observed shifts in informal mentoring and collaboration stem primarily from AI adoption rather than concurrent factors such as hybrid-work policies, reorganizations, or general cultural drift—lacks supporting evidence. The manuscript should either provide interview excerpts or analytic steps that isolate AI as the causal mechanism or explicitly discuss the confounding risks and how they were addressed.
minor comments (2)
- [Abstract] Abstract: The limitation of the single-company, product-role sample should be stated more explicitly to set appropriate expectations for generalizability.
- [Introduction] The manuscript would benefit from a short related-work subsection situating the findings against prior qualitative studies of AI and work practices.
Simulated Author's Rebuttal
We thank the referee for their constructive and detailed review of our manuscript. The comments highlight important areas for strengthening methodological transparency and clarifying the attribution of observed changes to AI adoption. We address each major comment below and indicate where revisions will be made to the manuscript.
read point-by-point responses
-
Referee: [Methods] Methods: The selection criteria, interview protocol, and thematic analysis process are described only at a high level. Without explicit details on recruitment, question guides, coding procedures, or steps taken to mitigate interviewer bias, it is difficult to evaluate the interpretive rigor behind claims that AI specifically drives changes in mentoring practices.
Authors: We agree that additional detail on the methods will strengthen the manuscript. In the revised version, we will expand the Methods section to include more specific information on recruitment (including how participants were identified and selected within the firm), the structure of the semi-structured interview protocol with examples of key questions, the thematic analysis process (including how codes were developed, refined, and grouped into themes), and steps taken to address potential bias such as reflexive memoing and discussion of findings among the research team. These additions will provide greater transparency without altering the core study design. revision: yes
-
Referee: [Findings] Findings/Discussion: The central attribution—that observed shifts in informal mentoring and collaboration stem primarily from AI adoption rather than concurrent factors such as hybrid-work policies, reorganizations, or general cultural drift—lacks supporting evidence. The manuscript should either provide interview excerpts or analytic steps that isolate AI as the causal mechanism or explicitly discuss the confounding risks and how they were addressed.
Authors: We acknowledge that qualitative studies cannot fully isolate causal mechanisms, and our claims are grounded in participants' self-reported experiences rather than experimental controls. To address this, the revised manuscript will include additional interview excerpts where participants explicitly attribute changes in mentoring and collaboration practices to AI tools (as opposed to other organizational shifts). We will also add a dedicated paragraph in the Discussion section that directly addresses potential confounding factors such as hybrid-work policies and reorganizations, explaining how the interview questions were framed to focus on AI impacts and noting the limitations of inferring causality from retrospective accounts in a single-firm study. This will make the evidential basis and its boundaries clearer. revision: partial
Circularity Check
No circularity: empirical qualitative study with no derivational chain
full rationale
The paper reports findings from semi-structured interviews with 24 product-focused employees at one firm. It advances no mathematical derivations, fitted parameters, predictions, or first-principles results that could be inspected for reduction to inputs by construction. Claims about changes in mentoring and collaboration are presented as interpretive summaries of participant accounts rather than outputs of any model or self-referential equation. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results appear in the provided text. The study is therefore self-contained as an empirical report.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Self-reported interview data from 24 participants accurately reflects real changes in work practices attributable to AI.
Reference graph
Works this paper leans on
-
[1]
In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1–21
Reactive Writers: How Co-Writing with AI Changes How We Engage with Ideas. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1–21. Borgatti; and Cross. 2003. A Relational View of Information Seeking and Learning in Social Networks. 49. Brown, J. S.; and Duguid, P . 1991. Organizational Learn- ing and Communities-of-Practice:...
-
[2]
Government In- formation Quarterly, 36(4): 101393
Characterizing the importance of clarity of roles and responsibilities in government inter-organizational collabo- ration and information sharing initiatives. Government In- formation Quarterly, 36(4): 101393. Goerisch, D.; Basiliere, J.; Rosener, A.; McKee, K.; Hunt, J.; and Parker, T. M. 2019. Mentoring with: reimagining mentoring across the university....
work page 2019
-
[3]
Who is Responsible, the Advisor or the AI? Under- standing the Effects of Advisors Disclosing Their AI Use on Their Perceived Responsibility and AI Reliance. Proc. ACM Hum.-Comput. Interact., 9(7). Meske, C.; Hermanns, T.; V on der Weiden, E.; Loser, K.-U.; and Berger, T. 2025. Vibe coding as a reconfiguration of intent mediation in software development: D...
work page 2025
-
[4]
A systematic literature review on the impact of arti- ficial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review , 33(1): 100857. Poquet, O.; and De Laat, M. 2021. Developing capabilities: Lifelong learning in the age of AI. British Journal of Edu- cational Technology, 52(4): 1695–1708. Post, C.; De Lia, E.; Di...
-
[5]
The Rise of AI Companions: Interaction with AI Companions and Psychological Well-being
Generative Design and Vibe Coding: Rethinking The Design-Development Divide for UI Prototyping. In Pro- ceedings of the Extended Abstracts of the 2026 CHI Con- ference on Human Factors in Computing Systems , 1–3. Zhang, Y .; Zhao, D.; Hancock, J. T.; Kraut, R.; and Y ang, D. 2025b. The rise of AI companions: how human- chatbot relationships influence well-...
work page internal anchor Pith review Pith/arXiv arXiv 2026
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.