The strength of the dissertation lies in its methodology. Rather than relying on speculation or surveys of opinion, Musazade grounded his analysis in two complementary types of evidence. The first is a longitudinal examination of job advertisements spanning several years, a technique that allows researchers to trace how the language of skill requirements evolves in real time as employers respond to technological change. The second is a set of experiments investigating how people actually use AI in practice, moving beyond what organisations say they want to observe what happens when human workers and machine systems interact on genuine tasks. Together, these approaches provide a rare dual perspective on the AI transition: one looking at the demand side of the labour market, the other at the behavioural reality of human-AI collaboration.

One of the dissertation’s central findings concerns the role that generative AI currently plays in working life. Despite the breathless rhetoric that often surrounds these systems, Musazade’s research shows that generative AI functions primarily as a support tool rather than a replacement for human workers. Even when AI is embedded in workflows, human oversight, analytical thinking and problem-solving skills remain essential. This finding aligns with a growing understanding in the information systems field that large language models and similar technologies are powerful pattern completers and content generators, but they lack the contextual judgement, accountability and domain reasoning that professional work demands. The machine can draft, summarise and suggest; the human must verify, interpret and decide.

Perhaps the most practically significant result comes from an exploratory experiment comparing different styles of AI use. Participants who treated AI as a collaborative tool, combining its input with their own judgement, performed better than those who relied more heavily on AI-generated responses. The implication is subtle but profound: the value of AI is not fixed by the quality of the model alone, but by the mode of engagement that the human user brings to the interaction. Passive acceptance of machine output can flatten performance, while active, critical integration of AI suggestions with independent reasoning amplifies it. In other words, AI alone does not guarantee better outcomes, and the difference between augmentation and substitution may be the difference between success and failure.

“The future of work is not about humans or AI. It is about how effectively people can collaborate with AI. Those who can combine the capabilities of technology with their own judgement will have a clear advantage,” says Musazade. This framing reframes the popular debate in an important way. The binary question of whether machines will replace people has dominated public discourse since the earliest automation scares, yet the empirical picture emerging from studies like this one is far more nuanced. The competitive frontier is not human versus machine but skilled collaborators versus unskilled ones, and the skills in question are deeply human: the capacity to evaluate evidence, to sense when a model’s confident answer is wrong, and to weave technological output into organisational and ethical context.

The dissertation also documents a rapid evolution in what employers actually ask for. Skill requirements in data- and AI-related fields have shifted noticeably in a short period, and the direction of that shift is telling. The focus has moved from developing AI systems to applying them in practice. A few years ago, the scarce and celebrated expertise centred on building models, training algorithms and engineering the underlying infrastructure. Today, employers are increasingly seeking professionals who can use AI tools effectively, integrate them with other systems and adapt them to organisational needs. The technology has matured from an experimental artefact into a general-purpose instrument, and the labour market now rewards those who can deploy it well rather than merely those who can construct it.

Notably, this shift has not rendered traditional technical skills obsolete. The research finds that more conventional competencies, such as programming in Python, remain highly valued by employers. This coexistence of old and new skill demands paints a more complicated picture than the simple narrative of disruption. Organisations still need people who understand how the machinery works beneath the interface, both to build custom solutions and to exercise informed oversight of what the tools produce. The ideal profile emerging from the job advertisement analysis is therefore hybrid: fluent in the practical use of AI applications, capable of integrating them into broader technical architectures, and grounded in the foundational programming and analytical skills that make such fluency trustworthy rather than superficial.

From a theoretical standpoint, the dissertation’s subtitle, A Human-Task-Technology Perspective, signals its intellectual anchoring. Information systems research has long recognised that technology does not act upon organisations in isolation; outcomes emerge from the interplay between people, the tasks they perform and the tools they use. By applying this lens to the AI transition, Musazade avoids the technological determinism that plagues much commentary on the subject. The same AI system can substitute for a worker on one task and augment another, depending on how the task is structured and how the human engages with it. Skill requirements, seen through this framework, are not dictated by the technology itself but negotiated at the intersection of what machines can do, what tasks demand and what organisations need.

The practical implications extend well beyond individual career advice. Musazade argues that the findings can help educational institutions and employers respond to changing skill requirements during a period of rapid technological change. For universities and professional training providers, the message is that curricula must balance foundational technical knowledge with instruction in critical evaluation, systems thinking and effective human-AI collaboration. For employers, the research suggests that hiring strategies and workplace design should reward judgement-rich engagement with AI rather than raw reliance on it, and that training programmes should cultivate the collaborative habits that the experiments show lead to better performance. Policy makers, too, face a labour market in which the half-life of specific technical skills appears to be shortening even as the demand for durable human capabilities persists.

What makes this research resonate beyond academia is its timing and its tone. Arriving at a moment when generative AI has become a household presence and anxiety about technological unemployment runs high, the dissertation offers neither complacency nor alarm. It confirms that the labour market is changing quickly, that the composition of valued skills is shifting from building AI to applying it, and that the ability to work alongside intelligent systems is becoming a defining professional competency. At the same time, it demonstrates with empirical rigour that human creativity, analytical thinking and systems thinking are not casualties of that transition but its essential partners. The machines are getting better, but the evidence suggests that the people who thrive will be those who never stop thinking for themselves. Musazade defended his dissertation at Åbo Akademi University on 25 September 2026, and its findings arrive as a timely reminder that in the story of AI and work, the human role is being rewritten rather than erased.

Subject of Research: How artificial intelligence is transforming skill requirements in data analytics and AI-related occupations

Article Title: AI is reshaping the workplace, but not replacing human judgement

Article References: AI is reshaping the workplace, but not replacing human judgement. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, future of work, generative AI, skill requirements, labour market, human-AI collaboration, information systems, data analytics, analytical thinking, job advertisements, doctoral dissertation, workplace automation