How I became an AI Analyst
2026-06-08
"What I do have are a very particular set of skills. Skills I have acquired over a very long career."
— Bryan Mills, Taken (2008)
Five years ago, if you asked anyone what an AI Analyst was - they couldn't answer.
Five minutes ago if you asked me what an AI Analyst was, I probably wouldn't have had an answer.
Today, I work as an AI Analyst.
The path to get here wasn't built on one path but three pillars that came together over several years: business skills, technical skills and AI skills.
Pillar One: Business Skills
Before technology, I spent nearly twenty years working in hospitality. Like many career changers entering IT, I initially viewed this experience as largely unrelated to software development.
I was wrong.
Hospitality taught skills that are difficult to learn from books or courses:
- how to communicate with different types of people
- how to manage competing priorities
- how to solve problems when there isn't a documented solution
- how businesses actually operate
- how to understand customer needs, stakeholder expectations, operational constraints, and the realities of delivering services day after day
Openining new venues and hotels also exposed me to project management long before I entered IT. While the environment was different, many of the underlying skills would later prove directly applicable to technology projects.
This included:
- translating strategic goals into executable plans
- coordinating cross-functional teams and stakeholders
- managing timelines, dependencies, and critical milestones
- balancing scope, quality, and operational constraints
- identifying risks and resolving blockers
- adapting to changing requirements and priorities
- delivering succesful outcomes against fixed deadlines
The goal is helping people achieve an outcome whatever the environment. As I progressed through my technology journey these skills became increasingly valuable.
Pillar Two: Technical Skills
These began in 2021 after enrolling in a Diploma of Web and Software Development.
Like many aspiring developers I started with the fundamentals.
- Systems and networking
- Web Development
- Databases
- UI / UX Design
- Version Control
- Programming
As my studies progressed I completed a Bachelor of Applied IT: Web and Software Development.
During that journey I built websites, software applications, automation toolls, dashboards, prototypes, api's and countless learning projects.
Some more succesful than others, but every project taught something.
The biggest lesson beyond writing code was how much broader sotware development is, in areas such as:
- Business analysis
- Systems thinking
- Process design
- Automation
- User Experience
- Solution Architecture
- Product Development
My previous Business skills meshed very well with SDLC and I began asking:
- Why does this process exist?
- Why are users doing it this way?
- Could this be automated?
- What problem are we actually solving?
Pillar Three: AI Skills
When ChatGPT appeared I was a very quick adapter just 3 months in and far ahead before it hit the mainstream zeitgeist.
What started as curiosity quickly became part of my daily workflow.
As it developed it became a major area of focus while trying to remain committed to learning the fundamentals and not taking shortcuts.
I spent countless hours experimenting with emerging AI technologies, testing capabilities, exploring limitations, and trying to understand the genuine value these tools could provide.
It was not the technology itself that interested me most but how it can enhance capabilities.
As my undstanding grew, so did the questions I was asking. Not what AI could do today but where it was heading tomorrow.
I became increasingly interested in:
- How llms actually work
- where AI genuinely improves productivity
- which tasks are suited to automation
- strengths and limitaions of different AI systems
- how to evaluate AI outputs critically
- where human expertise remains essential
- how people and organisations can use AI effectively
This led me into areas such as:
- prompt engineering
- AI tooling and workflows
- AI agents and automation
- retrieval-augmented generation (RAG)
- AI governance and risk management
- knowledge management
- workflow optimisation
- organisational adoption
- agentic orchestration
The more I learned, the more I realised that AI is not simply another software tool.
It represents a fundamental shift in how people interact with information, knowledge, and technology.
Succesful AI adoption requires more than technical understanding. It requires understanding how people work, how systems connect, and where technology can create meaningful value.
That combination of interests sat directly at the intersection of my business and technical experience.
Bringing the Pillars Together
When I finally stepped into my first IT role as an AI Analyst, it didn't feel like I had abandoned the path towards becoming a developer.
It felt like everything I had learned was converging.
The hospitality experience taught me how businesses operate, how to work with people, and how to deliver outcomes.
The software development journey taught me how technology works, how systems connect, and how solutions are built.
The AI journey taight me how emerging technologies can augment human capability and reshape the way we interact with information and knowledge.
Every previous experience, every project, every sucess, every failure and every new skill and technology explored has helped shape a unique combination of skills.
Five years after starting the journey, that combination has led me somewhere I never expected.
The intersection of business, technology and AI and this feels like only the beginning of the journey.