Steering Organizational Change in an AI-Driven World: An Adaptive Leadership Framework for Digital Transformation
Bibliographic record
Abstract
Abstract: In today’s fast-paced and technologically advanced business environment, organizations are under considerable pressure to integrate artificial intelligence (AI) tools to maintain competitiveness and enhance performance (Vial, 2019; Haenlein and Kaplan, 2021). However, such integration often encounters resistance, primarily due to poor communication, cultural misalignment, and lack of employee involvement (Tabrizi et al., 2019; Hiatt, 2020). This paper investigates the case of a mid-sized firm facing these very challenges during its AI adoption journey. Using the Leading Complex Change framework, the study explores how environmental conditions, internal structures, and leadership behavior interact to either enable or hinder transformation (Kane et al., 2021). Through a synthesis of scholarly research and practitioner insights, the paper proposes a hybrid change management strategy that combines structured models like ADKAR with adaptive leadership techniques. This blended approach emphasizes continuous learning, inclusive engagement, and psychological safety, which together foster higher levels of employee commitment and organizational agility. The findings offer a pragmatic blueprint with broad applicability for organizations navigating similar AI-driven transformations (Nguyen et al., 2022; Garvin and Malhotra, 2023). Keywords: Artificial Intelligence (AI), ADKAR Model, Mid-Sized Enterprises, Digital Capability Building, Organizational Agility, AI Implementation Challenges
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".