Artificial intelligence and adaptive governance in the technical and vocational education and training sector in Bhutan: A case study

Date of Award


Document Type



Edith Cowan University

Degree Name

Master of Business by Research


School of Business and Law

First Supervisor

Ferry Jie

Second Supervisor

Hadrian Djajadikerta

Third Supervisor

Kerry Brown


Artificial Intelligence (AI) is an emerging area of research on organisational change. Due to its self-learning ability, AI can drive and create changes in the existing operating models of organisations that are hard to predict and could lead to disruptive consequences for organisations and societies. Further, disruptions arising from emerging technologies such as AI are amplified due to the cross-interactions and complementary effects of different technologies in the era of the Fourth Industrial Revolution. This rapid and yet unpredictable advancement in many emerging technologies has contributed to the rapid change in skill sets required for employment. Research has estimated that almost most of today’s young people will face jobs that do not yet exist and that will demand radically different technical and soft skills. By 2025, around 85 million jobs could become redundant due to the shifting division of labour between humans and machines, and 97 million new job profiles may emerge suitable to the evolving labour dichotomy between humans, machines and algorithms.

This type of shift in the labour market has implications for technical and vocational education and training (TVET) regarding how relevant skills training and education are delivered to ensure the continued employability of job seekers. Currently, limited research exists exploring effective responses to the unique disruption associated with AI in the skilling sector. Therefore, this research seeks to understand how the TVET sector can respond to disruptions in training and education delivery due to AI. Understanding this is critical as many of the occupations most likely to be affected by AI are associated with the TVET sector.

This research proposes adaptive governance (AG) as one appropriate organisational response to deal with the disruption that explores beyond the existing value network optimisation driven by top-down decision-making. In this research, AG provides an underlying theoretical framework, which taken with the theory of disruptive innovation proposed to lie at the intersection of the organisation’s Structure, Processes and Digital Competency, may suitably meet the AI implementation challenges within an organisation.

The research methodology involves a case study of the TVET sector of Bhutan. The method employed in the data collection was semi-structured interviews, and hence the data comprised solicited documents of audio recordings for the final data analysis and interpretation.

From the findings, the research concludes that there is a lack of proper understanding of AI within the TVET sector of Bhutan in general. There are neither policies nor official guidelines regarding AI technology, and there is little training or experimentation on AI application development within the TVET sector. Therefore, there is immense scope in implementing the AG conceptual framework in overcoming AI implementation challenges for the training and skills education providers in Bhutan. Further, this novel AG conceptual framework offers a similar mitigation strategy for the TVET sectors in other countries; AG provides a unifying idea and language for effectively addressing unique challenges arising from disruptive AI technology. Finally, the research contributes to the existing body of literature by demonstrating that AI is a different type of disruptor and how AG addresses the inadequate top-down paradigm of responding to disruption as the innovation from AI is not a predictable trajectory. On the contrary, AI applications are often complex and less scientifically understood and require more holistic governance rather than just efficient management zealously implemented to upend the existing market order.



Access Note

Access to this thesis is embargoed until 9th August 2024.

Access to this thesis is restricted. Please see the Access Note below for access details.