For many finance chiefs, the first wave of AI was about testing tools: automating reports, speeding up reconciliation, or asking software to spot anomalies in spreadsheets. In Singapore, that phase is quickly giving way to a more difficult question: how to make AI work across the messy reality of regional finance operations.
A new Forrester Consulting study commissioned by Airwallex suggests Singapore is among the more advanced markets globally in operationalising AI within finance functions. But it also points to a constraint that will be familiar to many Southeast Asian companies expanding across borders: fragmented systems, inconsistent data, and legacy workflows are now bigger obstacles than access to AI models themselves.
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The study surveyed more than 1,200 finance decision-makers across 11 markets, including Singapore, and was launched as part of Airwallex’s Business Builders programme, a Singapore initiative featuring founders and finance leaders from companies such as Endowus, StaffAny, CardUp, GlobalTix, Peakflo, Polybee, Chronos Agency, Stryv and Pitstop Tyres.
The findings capture a shift in the region’s AI conversation. Adoption is no longer the headline. Execution is.
The AI budget is still rising
Singapore finance leaders are not pulling back from AI. According to the study, 96 per cent expect investment in AI-powered finance to increase over the next 12 months. That figure reflects how quickly AI has moved from a side experiment to a core operating priority.
In finance teams, AI is being used for tasks such as bookkeeping, reporting, forecasting, fraud detection, compliance checks and scenario modelling. These are areas where speed and accuracy matter, but where human teams are often slowed down by manual processes and scattered data.
The appeal is clear. In Southeast Asia, even relatively young companies often operate across multiple markets, currencies, banks, payment rails and regulatory regimes. A Singapore-headquartered SaaS, fintech, travel or e-commerce startup may collect revenue in Indonesia, pay vendors in Vietnam, hire teams in the Philippines and raise capital from overseas investors. That creates a level of financial complexity that spreadsheets and disconnected tools struggle to manage.
AI can help, but only if it can see the full picture.
That is where many finance teams are getting stuck.
Fragmented data is the real bottleneck
While AI adoption is high, scaling it remains harder. In Singapore, 64 per cent of finance leaders identified fragmented or inconsistent data across disconnected systems as a core barrier to scaling AI. Globally, 65 per cent cited the same issue.
This matters because AI systems depend on clean, timely and connected information. If transaction data sits in one system, procurement in another, payroll somewhere else, and regional subsidiaries use different reporting formats, AI can only produce partial insights. In some cases, it may automate bad assumptions faster.
The study also found that 68 per cent of Singapore respondents said their finance workflows are only partially digitalised, while 53 per cent said data either flows inconsistently across finance platforms or remains largely siloed.
For a region such as Southeast Asia, this is not a minor operational issue. Many companies expand market by market, often adding tools as they go. A payment provider may be chosen for one country, an accounting platform for another, and a separate expense tool for a newly opened office. What works in the early stages can become a constraint as the business grows.
Arnold Chan, General Manager for Asia Pacific at Airwallex, framed the challenge directly: “Businesses are no longer asking whether to invest in AI. They’re asking how to make AI work at scale.”
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He added that the biggest obstacle is not access to AI models, but the financial systems beneath them. “Businesses that connect their financial data, workflows and operations will be far better positioned to move beyond isolated AI use cases towards more intelligent, autonomous finance.”
Singapore is ahead, but not fully autonomous
The study suggests Singapore finance teams are further along than their global peers in allowing AI to run parts of finance operations with limited human involvement.
Eighteen per cent of Singapore respondents said AI already runs autonomously with minimal human input across finance workflows, compared with 11 per cent globally. In record-to-report processes, which include bookkeeping, closing and reporting, 27 per cent of Singapore finance leaders said AI runs autonomously, compared with 14 per cent globally.
That does not mean finance departments are handing over decision-making wholesale. Much of the near-term opportunity remains practical rather than futuristic.
In Singapore, 64 per cent of respondents expect AI to generate cash-flow forecasts and what-if analyses that recommend actions for humans to decide on within the next year. Globally, the figure is 51 per cent. Another 43 per cent of Singapore finance leaders expect AI to identify patterns, trends and root causes while leaving decisions to people.
This distinction is important. In finance, especially in regulated sectors such as fintech, wealth management and payments, full automation carries risks. AI-generated forecasts may be useful, but companies still need accountability, audit trails and human judgement when decisions affect cash, compliance or customers.
For Southeast Asian startups, where capital efficiency has become a sharper priority since the funding slowdown, better forecasting can still be valuable. Knowing earlier when working capital will tighten, when supplier payments may clash with payroll, or when regional revenue is drifting from plan can give management teams more room to act.
Talent becomes part of the infrastructure
The study also points to a second layer of readiness: people.
Singapore appears ahead here too. Twenty-seven per cent of finance leaders said their organisations have implemented structured, enterprise-wide AI talent strategies covering role redesign, certifications and hiring, compared with 15 per cent globally. Another 25 per cent said they have in-house AI development capabilities within or closely aligned to finance, versus 16 per cent globally.
This is significant because AI in finance is not simply a technology upgrade. It changes how teams work. Finance professionals may need to understand how to validate AI outputs, design workflows, question recommendations and work with engineering or data teams. The role moves from compiling information to interpreting and governing it.
At the same time, the study suggests not every company wants to build everything internally. Sixty-six per cent of Singapore finance leaders expect to use a hybrid model over the next year, combining in-house expertise with external providers. The proportion planning to build AI entirely in-house is expected to fall from 32 per cent today to 17 per cent.
That reflects a pragmatic reality. Even well-funded companies may not want to maintain large internal AI teams for finance alone. The more likely model is a mix of finance platforms, internal data capability and external specialists.
What this means for Southeast Asian companies
The broader lesson is that AI advantage in finance may depend less on who adopts the newest tool and more on who fixes the foundations first.
For startups and growth companies in Southeast Asia, this can be uncomfortable. Infrastructure work rarely attracts the same attention as product launches or fundraising rounds. But connected finance systems can determine whether AI becomes useful in daily decision-making or remains trapped in isolated pilots.
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Singapore’s position as a regional headquarters market gives it a natural lead. Many companies base finance, strategy and investor relations teams in the city-state while operating across the rest of Southeast Asia. That makes Singapore a testing ground for AI-enabled finance models that may later be applied across more fragmented regional markets.
The next year will show whether companies can turn AI investment into operational change. The money is flowing, and the tools are improving. The harder task is making sure the data, systems and people are ready for them.
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