Airwallex: “We’re Increasing AI Investment, but Expanding Cautiously” ... 90% of Finance Leaders Say They’re Raising Spend, 35% Say They’re Not Broadening Use Cases

Airwallex released a report titled "Building a Finance Organization Ready for the AI Era." The report shows that while AI adoption in finance teams is accelerating in terms of investment, constraints remain when it comes to broadening real-world use cases. In the title’s subtitle figures, 90% of finance leaders said they are increasing AI investment, while 35% said they are not expanding the jobs it is used for.

The report confirms the trend of rising AI investment among companies, while also showing a cautious stance on expanding AI use across finance operations. The findings suggest that even as AI adoption widens, companies are adjusting the pace as they move toward broader deployment across finance work.

The report said the reasons for this cautious rollout include finance data scattered across multiple systems, complex systems integration, a lack of experience in operating AI-based finance functions, and insufficient capabilities in AI-based finance operations teams. It noted that these factors are limiting wider AI adoption.

The report was based on a global survey commissioned by Forrester Consulting. The survey was conducted across North America, Europe, the Middle East and Africa, and Asia-Pacific, targeting 1,279 finance decision-makers in 11 markets worldwide, and examined the current state of AI use in global finance organizations, key challenges in the rollout process, and future operating directions.

According to the report, 90% of global finance decision-makers plan to increase AI-related spending over the next 12 months. However, 35% said they are not expanding AI into additional finance tasks, indicating strong intent to raise AI investment but slower progress in actual business expansion.

Airwallex said this shows that companies are prioritizing improving the use of systems they have already adopted and validating real-world performance and reliability, rather than rapidly extending AI into new areas. The main criteria for expanding AI, the company said, were measurable ROI at 55%, proven accuracy at 54%, and clear governance and explainability at 52%.

The report analyzed that companies' AI investment strategies are shifting away from a focus on rapid expansion and toward validating measurable value, reliability, and real operational readiness.

The survey identified distributed finance data and complex systems integration as the main technical hurdles to AI adoption. Among respondents, 65% pointed to finance data spread across systems, entities, and regions.

Even as finance platforms are being integrated, manual work remains necessary. 84% of respondents said manual processes are still required to complete finance workflows. The share of finance tasks carried out autonomously by AI with minimal human intervention averaged 11%.

Along with technical issues, organizational execution capabilities were also cited as a challenge. 53% said a lack of experience operating AI-based finance functions was a major factor hindering AI adoption.

Airwallex said that what matters more than AI adoption itself is connecting data and systems and securing a foundation for real-world operations. Accordingly, the key issue is not simply whether to adopt AI, but how to connect finance data and systems and build a framework for operating AI in day-to-day work.

In the survey on implementation methods, 57% of all companies were found to have adopted a hybrid model that combines in-house development with external solutions.

The report said that for companies to continue expanding AI use, what is needed is not just the introduction of new AI technologies, but also the organic connection of data, systems, and work processes, along with an operational foundation. It explained that the prerequisite for AI rollout is not simply adding technology, but building a structure that can connect and operate the company’s internal data, systems, and processes.

For organizations with low AI readiness, the report said the priority should be strengthening data connectivity between core finance systems rather than rapidly expanding autonomy. It also called for systematic management of data quality and access rights, and emphasized a phased expansion of AI use within controllable boundaries.

This trend was also reflected in criteria for choosing external partners. The importance of "connectivity" was confirmed in external partner selection criteria, and 66% of respondents planning to use external vendors in the future said the ability to connect and coordinate systems across the finance ecosystem would be a key criterion. Another 65% of respondents planning to use external vendors in the future said a flexible platform architecture that supports phased introduction and expansion of AI use cases would be a key criterion.

Based on the survey results, Airwallex said the key to AI performance in finance lies not in adding individual AI solutions, but in connecting existing finance systems, data, and workflows. The company said it is responding to this shift.

Kwon Yoon-ah, Airwallex's head of SME in Korea, said many companies are expanding AI investment. However, in actual finance operations, she said, data fragmentation across systems, manual work, and a lack of operating experience are posing challenges, making it difficult to expand the scope of AI use.

Kwon said the key takeaway from the survey is that creating an environment where AI can be applied to real work is more important than adoption itself. She added that for AI to function in finance, what is needed is not the addition of new features, but an organic foundation that connects data, systems, and business processes.

In response, Airwallex said it is supporting cross-border payments and financial operations through a platform built on an API-first, AI-native architecture. It added that, based on a connected global financial infrastructure, it aims to support effective AI use and help finance teams reduce repetitive work and focus on strategic decision-making.

Source: TECHWORLD · Lee Kwang-jae
Original: https://www.epnc.co.kr/news/articleView.html?idxno=406737

References

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