When my 10‑year‑old son asked me why I never build the games he loves to play, I laughed and told him that I am a finance professional, not a game developer. But days later, that question was still in my mind. Why can’t a finance professional build a game, especially in the age of AI, where creativity and technical execution are no longer limited by coding skills?
It started as a simple idea. I wanted to help my son enjoy learning touch typing through a small ‘adventure running’ game – something fun, educational and created just for him. Many parents worry about children between 10 and 12 becoming addicted to games; I wanted to turn that fascination into a learning advantage. So I began designing a mini‑game that combines the joy of play with skill training – a real example of joyful learning.
But in the process, something unexpected happened. With no IT background, no coding experience and no game development knowledge, I found myself using AI tools – ChatGPT, interpreters and no‑code builders – to turn my imagination into something executable. This small project, born from a parent’s intention, became a transformative learning journey for me as a finance professional and ACCA member.
What AI truly changes is not so much our jobs as our possibilities
It made me realise something profound: in a world where AI is accelerating intelligent automation, finance professionals cannot afford only to do finance. We must learn to design, create and test ideas, and use AI as an amplifier of our professional judgment. And sometimes, the best way to learn is not through a finance problem, but through a tiny game built for a 10‑year‑old, because it shows what AI truly changes – not our jobs in the first instance, but our possibilities.
As I built this simple typing game, I was struck by how similar the process is to the work we do in finance, and how AI fundamentally changes the way we approach problem‑solving.
Structured thinking
What turned my game concept into a system was structured thinking. To build a level, I needed objectives, constraints, feedback loops, resource allocation, measurable outcomes. In other words, exactly the same mental model we use for budgeting, closing cycles, internal controls and scenario planning.
AI made the translation easier. I described the outcome, and the system helped generate the logic, scripts, rules and alternatives. For the first time, I saw clearly that AI does not replace structured thinking but amplifies it.
Rapid iteration
Future finance teams must master the skill of rapid iteration. In the past, building anything digital required long planning cycles, technical experts, multiple approvals. But with AI, I could rewrite mechanics in minutes, redesign levels, test logic, identify errors and release a new version immediately.
This process mirrors the new expectation for finance: faster month‑ends, dynamic forecasts, continuous performance monitoring, agile business partnering. AI trains us to think in iterations, not perfection – a mindset finance professionals urgently need.
Decision modelling
At the core of both gaming and finance is decision modelling. Every rule, action and tool in game mechanics is a decision model. If a player jumps too early, they fail. If speed increases, risk rises. If resources are collected, it will build future capacity.
Finance is the same. Invest too early and liquidity risk rises. If costs rise, there is pressure on margin. If accruals align poorly, actuals are misleading.
Using AI to build decision logic in a game sharpened my ability to build financial scenarios, map assumptions, define risk boundaries and simulate outcomes. The surprising part? AI helped me externalise my mental models. It turned invisible decision flows into explicit logic – something CFOs wish analysts could do on day one.
Creativity
As a finance skill, creativity is no longer optional. For decades, finance roles rewarded precision, procedure, consistency and control frameworks. AI now automates these. Future finance must show new sources of value: innovation, design, interpretation, imagination.
Game development taught me to design player pathways (like business processes), to balance risk and reward (like financial risk) and to make learning fun (like creating value for stakeholders). In business, creative intelligence helps finance design more intuitive dashboards, tell better stories with data, discover new opportunities, rethink the user experience from CFO to operations. In the AI era, creativity is not a nice to have, it is a core competitive skill.
Our future lies in designing intelligent systems and enabling better decisions
Capability framework
Through this small game experiment, I began to see a new blueprint emerging, one that redefines what finance professionals must become in the age of AI. We have to be not just analysts and guardians of compliance, but also designers of intelligent systems, interpreters of insight and creators of value.
Building an educational game for my son did not make me a developer. It made me something far more relevant for the future of finance: a designer of logic, a collaborator with AI and a creator of value.
AI enhances our uniquely human strengths: judgment, creativity, structure and curiosity. The profession’s next evolution lies not in processing numbers, but in designing intelligent systems and enabling better decisions. And sometimes, the journey toward that future begins with something as small as a game built for a child.
The route to AI success
The successful use of AI in finance rests on the following four pillars.
- Cognitive design skills. This involves structuring ambiguity into logic: inputs, rules, interactions, outputs, feedback cycles. Tools execute, but humans design.
- AI collaboration literacy. There is a fourfold process: specify, generate, evaluate, iterate. The future skill lies in orchestrating AI, not coding.
- Creative intelligence. AI automates routine tasks; creativity differentiates. Storytelling, insight framing and user‑centric thinking become financial leadership skills.
- Execution agility. AI lowers execution barriers. Finance must learn rapid prototyping: test, refine, improve.
For a four‑week pathway to AI success, try the following:
- Week 1. Deconstruct a simple problem using AI.
- Week 2. Practise iterative collaboration with AI.
- Week 3. Integrate components into a system.
- Week 4. Build a prototype: dashboard, automation or microtool.
