The next chapter: GTM, AI and automation.

For a long time, I thought my career would be about choosing one skill and getting better at it.

First, I explored design. Then I learned about websites and no-code tools. I started an agency, moved towards lead generation and spent more time understanding how businesses find clients. Later, I joined a branding and marketing agency to work in business development.

Each step introduced me to a different part of the same bigger picture: how a business creates value, communicates it and finds the people who need it.

Now, I find myself increasingly curious about GTM, AI and automation.

Why these areas interest me

My interest in automation did not appear out of nowhere. It connects with questions I had already been asking through my work.

How do you find the right prospects? How do you research a business properly? How do you organise outreach without losing context? How can a team spend less time repeating manual tasks and more time doing work that needs judgement?

These are practical business questions, and technology can help answer some of them.

I have been spending more time learning about AI tools, Claude and Claude Code, as well as how automation can support go-to-market work. I am also getting closer to the world of Nex Automations, which is giving me another environment in which to explore these ideas.

I am particularly interested in the connection between GTM and automation: using data, workflows and AI to make prospecting, research and business operations more structured.

From using tools to designing systems

Earlier in my journey, I often approached learning by asking, “How does this tool work?”

Now I find myself asking a different question: “What problem can I solve by combining these tools?”

That is a meaningful shift.

A tool on its own is not a strategy. An automated workflow is not valuable just because it runs. It needs to solve a real problem, fit into the way a business works and produce an outcome that matters.

That means understanding the business process before trying to automate it.

For example, lead generation is not just collecting names and sending messages. It involves understanding an ideal customer, researching the company, identifying a relevant reason to reach out, managing the conversation and knowing what should happen next.

AI and automation may help with parts of that process, but they do not remove the need for good judgement. In fact, the more powerful the tools become, the more important it is to understand the problem clearly.

I am still at the stage of learning, experimenting and connecting the pieces. I do not want to pretend that I have everything figured out. I would rather document what I learn and improve through actual work.

Looking back without treating the story as finished

When I look at the path from wanting to become an architect to exploring GTM and AI automation, it does not look like a straight line.

There were changes in direction, experiments, skills that led to other skills and moments when I was unsure what to do next.

But I can see a pattern now.

I have always been interested in creating things and understanding how they work. Over time, that interest expanded from physical spaces to digital experiences, from digital experiences to businesses, and from business development to the systems that support growth.

I do not know exactly where this direction will lead. That is part of what makes it interesting.

I am writing this series not because I have reached the finish line, but because I want to remember the decisions, mistakes and lessons that brought me here. There is still a lot to learn, and I expect my thinking to change as I get more experience.

The story is not complete. This is simply the point I have reached so far.

And the next part will be written through the work I do from here.