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What it really takes to build a long-term career in AI
Artificial intelligence and analytics continue to evolve at pace, with new tools emerging, techniques shifting, and job titles changing. But what does it actually take to build a career in AI that lasts?
Stuart, Director of Technology at Liberty IT, helps us answer precisely that by sharing his journey and perspective on the skills, mindsets and career pathways that stand the test of time in an ever-changing field.
From engineering to AI & analytics
With nearly two decades of experience across the organisation and with a career that started in software engineering, what first sparked your interest in AI and analytics?
My interest really took shape around seven years ago when I stepped into a senior portfolio architect role. What became clear quite quickly was that software engineering alone wasn’t enough to solve some of the more complex problems we were tackling at Liberty Mutual. That led me deeper into AI and analytics.
How has the approach to AI, analytics and emerging tech evolved since those early days for you and for Liberty IT?
Initially, we used more commodity AI for things like document intelligence and cognitive experiences. Over time, we built dedicated data science teams working on predictive modelling, computer vision and natural language processing, alongside a strong machine learning engineering discipline with a focus on MLOps.
In the last few years, my focus has shifted again, this time towards generative AI and Agentic AI and how we responsibly apply it at enterprise scale to create real business value.
AI careers are more than nuilding models
Stuart, what’s one thing people often underestimate about working in AI?
A lot of people think AI is mostly about building machine learning models, but that’s only part of the story. One of the biggest surprises for me was just how many challenges exist around the model.
Getting access to the right data can be difficult. Labelling it correctly is another challenge. Then there’s tracking experiments, managing feature engineering, operationalising models, monitoring performance, watching for data drift, and knowing when to refit. At Liberty IT, we’ve spent years building out strong MLOps practices to address these challenges.
The goal is to reduce the time it takes to get models into production - and to let our data scientists focus more on creating new value, rather than constantly maintaining existing models. With Generative AI there is a different set of problems for example we need to provision and lifecycle access to Frontier models, manage prompts and evaluations and engineer the right context.
And Agentic AI, which is AI that can reason and take action we have another set of problems again like controlling access to tools and data and managing the state of long horizon agents.
The foundations that still matter
What core skills do you believe are essential for anyone starting out in AI and analytics?
Strong foundations really matter. I have a computer science and engineering background and the understanding that has given me about how systems are built and run is fundamental to building solutions with AI.
For building models and evaluating AI, that means a solid grounding in mathematics and statistics, alongside programming skills, typically Python or R, and a clear understanding of machine learning concepts. As problems become more open-ended, statistical reasoning and data analysis become critical.
Without that foundation, it’s very difficult to evaluate whether a model or AI is actually delivering value. These fundamentals don’t go out of style, even as tooling changes.
Communication, a skill that separates good from great
You’ve spoken before about communication being vital in AI. Why is that?
Solutions are rarely created in isolation. You need to be able to communicate effectively to understand the problem you’re trying to solve in the first place.
AI and analytics sit at the intersection of business, technology and data. If you can’t translate between those worlds, if you can’t explain insights clearly or ask the right questions, even the most technically impressive solution can miss the mark. Strong communication skills help ensure that AI work is grounded in real needs and delivers real outcomes.
Career progression in AI & Analytics
What does career progression look like in this space today?
AI and analytics offer a huge range of pathways. At Liberty IT, we’ve seen engineers with strong scientific backgrounds transition into data science roles, and data scientists develop deep MLOps and engineering expertise.
We have a strong learn-it-all mindset. Continuous learning is part of our culture, and we actively support people as they evolve their careers, whether that’s deepening technical expertise, broadening into architecture, or moving into leadership roles. The field is constantly changing, which means there’s always opportunity for growth if you’re open to learning.
Mentorship and learning from others
Was mentorship important in your own career journey?
Absolutely, and not just one mentor. I’ve been fortunate to learn from many people who helped me build both my technical and professional skills. Mentorship is something I value deeply, both as a mentee and as a mentor.
Having someone challenge your thinking, offer perspective, and help you stay accountable makes a real difference over time. Like more careers in technology, AI careers aren’t built overnight; they’re shaped through guidance, feedback and shared learning.
Staying relevant in a fast-changing field
Stuart, how can people future-proof their careers in AI and analytics?
Curiosity is key. Things change quickly in this space, so adopting a continuous learning mindset is essential. That doesn’t mean chasing every new tool or trend. It’s about spotting new skills, practices or approaches that genuinely help you solve problems better, and being willing to adapt how you work. Strong fundamentals, paired with curiosity and a willingness to evolve, are what sustain long-term careers.
Advice for those considering a career in AI
What advice would you give to someone thinking about moving into AI and analytics?
Focus on building strong technical foundations first – software engineering, statistics, programming and machine learning concepts. At the same time, don’t underestimate the importance of communication and collaboration.
Find mentors, learn from people with more experience than you, and stay curious. AI is one of the most exciting areas in technology right now, but the people who thrive long term are those who combine technical depth with empathy, adaptability and a genuine desire to learn.
Building careers that last at Liberty IT
At Liberty IT, we believe sustainable careers in AI and analytics are built on strong foundations, continuous learning and a people-first culture. Whether you’re starting out, changing direction, or deepening your expertise, we’re committed to supporting pathways that grow with you.
