Why Generative AI Isn’t Formalized (Yet): Socio-Technical Barriers to Top Down Organizational Implementation
Bibliographic record
Abstract
The rapid adoption of generative artificial intelligence tools, such as ChatGPT, has disrupted traditional technology deployment processes. Unlike established models, where formal plans precede user adaptation, generative AI is often taken up without governance or managerial oversight. This paper investigates the socio-technical features that inhibit organizational formalization of generative AI. Based on an interpretive field study at a large technology company, we identify a set of socio-technical features—openness, contextualization, functional generality, rapid evolution, and invisibility—that privilege individual use, adaptation, and situated interaction over centralized control. Together, these features constitute what we describe as a personal-first orientation that fosters decentralized, user-driven adoption while resisting integration into formalized organizational practices. By highlighting these barriers, we contribute to information systems research on how general-purpose AI tools challenge established models of adoption and governance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".