My research highlights that alienation resulting from AI integration can significantly diminish employee engagement, job satisfaction, and productivity. These findings have clear parallels in business contexts where digital transformation and automation are prevalent. Understanding teacher alienation helps businesses anticipate similar employee experiences, allowing managers to proactively design technology implementations that preserve employee morale and productivity, thereby improving overall organisational performance.

Yes, they can. The dynamics of alienation explored in education, such as loss of autonomy, creativity, and reduced interpersonal engagement, mirror those found in many organisational contexts undergoing digital transformations. Businesses implementing AI-driven processes similarly face risks of employee disengagement and resistance if the human aspects of work are not carefully managed. Thus, my research provides insights broadly applicable to managing technological transitions in various industries.

The study recommends adopting ethical governance frameworks that prioritise transparency, participatory decision-making, human oversight, and professional autonomy. For businesses, these strategies translate into inclusive technology adoption processes, ongoing employee involvement in decision-making, clear communication about technological changes, and preservation of meaningful human roles within digital workflows.

Practically, organisations should:

• Engage employees actively in technology selection and implementation.

• Develop transparent governance models clearly outlining AI’s role and limitations.

• Provide comprehensive training that addresses both technical skills and ethical implications.

• Regularly assess employee experiences to swiftly address emerging alienation or resistance issues, thus fostering a healthy, productive organisational culture.

The core principles of ethical governance outlined in my research, i.e. transparency, human-centric decision-making, and participatory design, are directly transferable to corporate contexts. Businesses can adapt these frameworks by establishing cross-departmental ethics committees, creating clear policies around AI use, ensuring human oversight of critical AI-driven decisions, and fostering a culture where employees feel empowered rather than threatened by technology.

My findings suggest that efficiency and employee well-being are not mutually exclusive; rather, sustainable long-term efficiency is contingent on maintaining employee morale and engagement. By prioritising ethical and human-centred integration of AI (such as maintaining meaningful work roles and professional autonomy) organisations can achieve lasting efficiency improvements without sacrificing employee well-being or engagement.

Marx’s theory of alienation provides a robust conceptual lens for examining human experiences and workplace transformations triggered by technological change, making it highly relevant for exploring contemporary AI-induced disruptions. Alternative complementary theories from organisational behaviour, such as Self-Determination Theory (which emphasises autonomy, competence, and relatedness), and Socio-Technical Systems Theory (emphasising optimal human-technology interaction), could further enrich the analysis from a managerial standpoint. These viewpoints could make interesting future work.

I did consider prominent organisational theories such as the Job Demands-Resources (JD-R) Model, Technology Acceptance Model (TAM), and Psychological Contract Theory, as these frameworks are highly relevant for examining workplace dynamics, employee attitudes, and organisational responses to technological change. However, I ultimately selected Marx’s theory of alienation because it uniquely addresses the fundamental shift in human relationships to work, identity, and autonomy driven by systemic technological and managerial changes.

My findings indicate the necessity for proactive organisational strategies, such as employee participation in technology design and implementation, regular monitoring of employee sentiment, and maintaining meaningful human roles amidst technological automation. Businesses in various sectors can apply these strategies to effectively manage workforce disruptions and resistance, thereby optimising the transition to AI-driven processes.

The research clearly shows that preserving employee autonomy, creativity, and relational engagement is crucial for maintaining high levels of motivation, satisfaction, and productivity during digital transformation. Businesses that prioritise these human-centric elements when integrating AI will likely see improved employee engagement, reduced resistance to technological change, and sustained productivity gains.