Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!

A bit about Li:

Li Fu is a Senior Data & AI Scientist specialising in Generative AI and Machine Learning, with experience driving large-scale AI adoption at one of the UK’s largest banks. She works on how large language models (LLMs) are deployed, governed, and scaled in highly regulated enterprise environments, with a focus on turning emerging AI capabilities into practical, real-world impact.

How did you start out in your tech career?

I started as a Data Science Graduate at Lloyds Banking Group, rotating across Data Science, Machine Learning Engineering, and Data Engineering roles in different business teams.
This gave me a full-stack view of how data products are built and deployed, along with early exposure to innovation initiatives, particularly around Generative AI.
Overall, it was an environment that encouraged learning, experimentation, and staying up to date with the latest developments in the field.

What are the signs of success in your field?

Signs of success in this field include being able to proactively identify where technology can genuinely add value, assess feasibility early from both technical and operational perspectives, and take ideas through to real implementation. Ultimately, success means bridging technical solutions with business outcomes so the work gets adopted, scaled, and delivers real impact.

What is the best and worst thing about your job role? 

Best: I get to work with cutting-edge technology every day. The pace of innovation in AI is exciting, and there’s constant exposure to new ideas, tools, and capabilities.
Worst: The speed of innovation means you’re always adapting — solutions evolve quickly, and you rarely get a “finished” state.

What can you advise someone just starting out to be successful?

Always think about how to reimagine the existing ways of doing things. We’re in a period where new technologies are emerging and evolving very fast, and approaches that worked well before may no longer be the best ones.

If you’re working in this era, you have an opportunity to rethink, redesign, and improve how things are done — whether that’s processes, products, or workflows. Making the most of new technology means being open to re-imagining systems rather than just maintaining them.

How do you switch off?

Travelling and spending time in nature. Stepping away from my usual environment helps me switch off and reset.

What advice would you give your younger self?

I’d tell my younger self not to doubt myself so much. I used to hesitate when applying for opportunities because I felt I wasn’t experienced or senior enough yet. Looking back, that was mostly imposter syndrome and unnecessary self-doubt.
If something interests you, just go for it. Even if it doesn’t work out, you’ll learn, gain feedback, and grow. Whether it’s a new role, a new field, or a career change, taking the step is always worth it.

What is next for you?

Career-wise, I want to take more ownership in defining problems, not just executing solutions. That means working more closely with business stakeholders to understand their journeys, constraints, and decision-making processes.
I believe that for technology to work well, it’s critical to understand where it truly adds value. The best solution isn’t always the most advanced or sophisticated one — it’s often the one that fits the use case and works reliably in practice.

If you could do anything now, career-wise or personally what would you do? Why?

I’d like to deepen my understanding of the fundamentals behind large language models, including the mathematics and theory. I think this would help me better understand how these models work, how they might evolve, and where their real limitations are.
I’m particularly interested in their evolution path — what they’re likely to become good at, and what they fundamentally won’t be good at. I want to be able to form my own conclusions based on first-hand understanding, rather than relying on second-hand information from blog posts. Direct experience and understanding are what really support good judgment.

What are your top 5 predictions in tech for the next 5 years?

  1. AI will become part of almost every job. Even people who don’t work directly on AI will use it in daily work. Basic AI literacy – knowing what it can and can’t do – will become essential

  2. Value will shift from models to real-world applications. The differentiator won’t be who has the best model, but who can integrate AI effectively into products and workflows.
  3. AI governance and reliability will matter as much as performance. As AI scales, especially in regulated environments, monitoring, control, and risk management will become core engineering concerns.
  4. Human judgment will become more important. AI will handle more execution, while humans focus on problem framing, decision-making, and critical evaluation.
  5. Continuous learning will outweigh fixed skill sets. Tools will change fast. The ability to adapt and learn will matter more than mastering any single technology.

Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!

Want to become a DSF Speaker? Apply here!