Inside our journey to transform the office of the CFO

Before I joined Airwallex, I spoke to more than 20 private-equity backed CFOs. I wanted to understand where and how they and their Finance teams were using AI.

Progress was slower than I expected. Lean teams, risk aversion (particularly in Controllership) and data hygiene were the most frequently cited blockers. Where experimentation was happening, it tended to be in FP&A and in use cases like budget variance analysis and slide creation for regular management reporting.

Reflecting on these conversations, I began to wonder whether Finance adoption of AI is also being hampered by something else. Are CFOs being ambitious enough in their adoption of AI? Or are they bolting it on to existing processes and workflows, without taking a fundamental approach to redesigning how Finance operates?

I think the first step in answering these questions is to understand deeply what drives work in the office of the CFO today: the health of the Finance data foundation, jobs to be done, talent, and operating model. These topics help determine where to spend time and energy as we transform Finance at Airwallex.

1. Can we trust the foundations?

A successful Finance team is built on a strong data foundation. With AI, this is more true than ever: there is no point having a conversational interface to allow analysts to query bad data. Airwallex is an unusually complex business: we are growing top-line at scale, globally and across multiple products and segments. Our transactional costs are evolving with growth in volume and changes to scheme rules. Our product footprint is constantly expanding. This has been an area of focus for the business for several years now. I want to understand (and share) what we have done already and how we will maintain a high-quality foundation as we scale.

2. Where are we spending time today?

It is impossible to transform the Finance function without deeply understanding the jobs it has to do. This means fully mapping the work each team does: the key processes, the complexity, the amount of time they take and how often they happen. Where automation has happened already, what have we achieved? Where work is still manual, why? All of this will paint a picture of where people are spending time and where AI should have the biggest impact. In an upcoming post, I will share what we are learning and how it shapes our transformation priorities.

3. How effectively does Finance operate?

People, not processes and data, are what drive Finance functions today. Getting the most out of people requires an operating model that scales. The barrier to writing code has never been lower but citizen development doesn’t come without challenges. Organisational clarity is more critical than ever: roles and responsibilities, what is done centrally and what is distributed, who designs and who implements. 

Over the coming months, my team and I will share openly about what we’re learning as we work through these questions, including what our process mapping exercise reveals, how we’re evolving our team and operating model, and what’s on our AI roadmap. I want to give other finance leaders a transparent view of the journey and some useful lessons they can take into their own organisations.