A Thinking System

There comes a point in every doctoral journey when research stops feeling like a collection of theories, methods and papers, and starts becoming a way of thinking.

For me, that moment happened recently.

For months, I have been reading about Generative Artificial Intelligence, Organisational change, sense making, organisational imprinting, techno stress and techno – eustress. I understood many of the definitions, but I sometimes struggled to connect them in a meaningful way. I knew these concepts were important, but I was still trying to understand how they spoke one to another within my own research.

Then, while working through the literature and discussing my ideas critically, something began to change.

I realised that people do not simply experience technology. They experience their interpretation of technology.

That insight transformed how I viewed my research.

Have you noticed that two people can look at the same thing and see it differently? That simple question now sits at the centre of my study. The same technology can be introduced to employees in the same organisation, yet each person may respond differently. For one employee GenAI may feel like support. For another, it may create uncertainty, pressure or strain.

The differences are not random. They may be shaped by previous experiences, organisational history, workplace routine, assumptions, structures, values and the meaning people attach to change.

Today, many organisations are introducing GenAI because of its potential benefits. The literature often discusses efficiency, productivity, innovation, trust, and organisational performance. These are important, but I became interested in something more human: what happens to employees when this technology is introduced?

My research explores how employees in a long established manufacturing organisation make sense of GenAI. I am interested in how organisations’s history, routines, assumptions and values may shape whether employees experience GenAI as support, stress or an opportunity to learn.

This matters because employees do not encounter GenAI in isolation. They come into contact with it through their previous experiences, their workplace environment and the way things have traditionally been done. In a legacy manufacturing organisation, established routines and values may influence how employees interpret technological change.

This is where my research story begins. I want to understand not only whether GenAI is useful, but how employees experience it. I want to explore how they make sense of it, how they respond to it and how organisations can support them through technological change.

My study brings together three ideas. Organisational imprinting helps me understand how the past of organisations may continue to shape the present. Sense making helps me understand how employees interpret GenAI. Techno stress and Techno – eustress helps me explore whether employees experience the technology as strain or as a positive challenge that supports learning and growth.

I have discovered something about qualitative research. Themes should not be forced by the research. They should emerge from careful engagement with the literature and, later, from what participants consistently tell us. My role is not to squeeze people’s experiences into a theory, but to listen carefully, identify patterns and connect those patterns to existing research.

Perhaps the biggest lesson that becoming a researcher is not only about reading more papers. It is about learning to think differently.

Every paper I read, every conversation I have, every conference I attend and every discussion with my supervisor helps me develop that way of thinking.

For me, this research is not just about technology. It is about people. It is about how employees adjust, learn, question, resist, accept and collaborate with new tools.

I am still developing clarity, but I know one thing: human – AI collaboration cannot be understood only by looking at the technology. We also need to understand the people, the history of the organisation and the meaning employees attach to change.

I am still learning, but I am beginning to find my own research voice. That, to me, is one of the most rewarding parts of this journey.

That is my research story. Does it make sense?