TAKEAWAYS
Managing double the workload without doubling the workforce might seem like an equation that cannot balance. But this is precisely the challenge Changi Airport Group (CAG) is working to solve.
With Terminal 5 expected to match the combined scale of Terminals 1 through 4, the group’s transaction volumes, contractual commitments, credit assessments and tax computations will roughly double. The headcount, however, will not.
“There is just no way we can double headcount and resources,” says Chua Siew Hwi, FCA (Singapore), Senior Vice President of Enterprise Performance at CAG. “So, the question becomes: how do we fundamentally change the way work gets done?”
Speaking at a professional sharing session organised by the Chief Financial Officer Committee (CFOC) of ISCA, Ms Chua shared how CAG arrived at an answer – not through a grand technology roadmap, but through a ground-up approach anchored in real operational friction.
CAG’s AI journey did not begin in a boardroom with a technology strategy deck. It began with the finance team sitting down and identifying about 10 genuine pain points – process bottlenecks, repetitive analysis cycles, and judgement-heavy tasks that consumed disproportionate professional hours.
These pain points were refined, in collaboration with the Infocomm Media Development Authority (IMDA) through its Open Innovation Platform (OIP), into focused proof-of-concept challenges. Through IMDA, CAG connected with startups keen to trial their capabilities in a live enterprise environment – an arrangement that gave CAG access to emerging talent and technology, while giving startups a rigorous proving ground.
Two use cases were selected for prototyping: one centred on financial performance analysis, the other on financial indicator analysis.
The first use case addressed a challenge every management accountant will recognise: the sheer time consumed by data gathering, cleansing, and variance analysis. Important work but, as Ms Chua puts it, “not work that requires the expertise of a Chartered Accountant”. By deploying AI to automate the foundational layers of this analysis and generate first-level inferences, CAG’s finance professionals were freed to focus on interpreting results, challenging assumptions, and advising the business. The early journey was not without bumps – refining model outputs required iterative human oversight – but that, Ms Chua notes, was itself instructive. “It taught my team not just how to use AI, but how to interrogate it. That critical faculty is what separates a professional who uses AI well from one who is misled by it.”
The second use case tackled a higher-stakes area: evaluating the financial health of hundreds of counterparties each year to inform credit terms and contractual decisions. Given the volume and the consistency required, AI proved a natural fit – delivering structured assessments at a fraction of the time. Full development was subsequently continued in-house, ensuring the knowledge, solution and any sensitive underlying frameworks were retained within the organisation.
In both cases, the principle was the same: AI handles the heavy lifting; humans provide the judgement.
“AI can give you a very good first-cut analysis,” Ms Chua stresses, “but you still need human experience to refine, interpret, and challenge the insights.”
If the technology is the engine, the culture is the fuel. Ms Chua is unequivocal that CAG’s AI adoption succeeded because the groundwork was laid long before AI entered the conversation.
During the COVID-19 pandemic, finance staff were trained in Power BI, Power Automate and Robotic Process Automation. Crucially, they were then given the space to build their own tools – dashboards, automated workflows, small solutions to problems they found personally tedious. The wins were modest, but they were real, and they belonged to the people who created them.
“When people see the benefits and become the beneficiaries of the tools they built, confidence grows and resistance falls away,” Ms Chua highlights.
By the time AI arrived, it was not an imposition from above – it was the next logical step for a team that had already experienced what technology could do for them. “When we first modelled the finance workload for Terminals 1 through 5, the implied headcount was 1.5 times what we have today. We have set ourselves the target of achieving that with roughly 1.1 times our current team – a goal that rests not on doing more with less, but on enabling our people to operate at a fundamentally higher level,” Ms Chua shares.
Yet, Ms Chua is candid about what technology cannot replicate. The instinct of a veteran who knows which contract clauses deserve extra scrutiny, which patterns signal aggressive revenue recognition, which tax positions have been challenged before – this institutional wisdom is hard to codify and invaluable to preserve. CAG’s current approach is to pair digitally adept younger staff with experienced domain experts, combining energy with judgement. “But it is a challenge that the whole profession will need to confront,” she acknowledges.
Ms Chua closes with a message that reaches well beyond CAG’s walls.
Singapore’s high cost of labour must be matched by a correspondingly high standard of professional capability. If accountants continue to occupy themselves with work that AI – or lower-cost labour markets – can perform equally well, the premium that Singapore professionals command will erode.
“The profession’s continued relevance, and its continued premium, hinges on our collective ability to move up the value chain.”
CAG’s transformation journey demonstrates exactly how that can happen: elevating finance professionals from processors of information to interpreters of meaning and partners in strategy. Technology capabilities help organisations move towards insight-led, touch-less finance, empowering teams to focus on strategic thinking and operate with the confidence and clarity of a “mini CFO”. It is more than an encouraging anecdote – it is a call for the profession to harness technological change not as a threat to be managed, but as an opportunity to reach higher professional standards.
Two further projects illustrate the breadth of CAG’s adoption of AI.
The first targets a process every employee touches: staff claims. Currently in development, the system reads images of submitted receipts, checks them against policy, flags gaps or ineligibility and, when everything is in order, processes the claim without human intervention. The ambition is to compress what has traditionally been a multi-step, manual review cycle into seconds.
The second is more technically ambitious. As a highly infrastructure-intensive organisation, CAG must classify large volumes of fixed assets and determine their tax deductibility – work that involves reading thousands of pages of contracts, applying tax legislation, and exercising professional judgement on qualifying line items. An agentic AI model now performs the initial heavy lifting: reading contracts, classifying assets with reasoning, and inferring tax treatment.
Ms Chua is clear-eyed about where the model stands. “It does not need to be perfect,” she notes. “It needs to free up our tax specialists so they can focus on the exceptions and the edge cases – the work where their expertise genuinely matters.”
The principle that underpins CAG’s transformation holds firm throughout: embrace AI as an indispensable ally but never lose sight of the fact that professional judgement – the kind built through years of experience, scrutiny, and accountability – is the one capability that remains irreplaceable. It is precisely this pairing of human expertise with AI that charts the path forward: not just to stay relevant, but to redefine what relevance looks like for the profession.
