Three questions for Asia-Pacific finance teams using AI today (or not)

New EY research suggests finance functions in Asia-Pacific are ahead of the curve in using artificial intelligence (AI). But the region’s finance leaders are underestimating both the opportunity and the governance challenges as AI becomes embedded in financial and nonfinancial reporting.

The recent EY Global Corporate Reporting Survey found that 20% of Asia-Pacific finance leaders surveyed are making significant use of AI today, compared with 15% of finance leaders surveyed globally. Almost two in five (38%) of Asia-Pacific finance leaders surveyed (compared with 32% globally) said they use a high-grade technology suite to manage and analyze data.

The region’s finance leaders are also more likely to be advocates of AI than their global peers and to believe that AI will lead to more confidence in reporting. Almost half (48%) of respondents said they are “enthusiastic about the use of AI in corporate reporting and can see significant benefits from using these technologies,” 10% more than the global average.

There’s no doubt that AI is already a game changer for finance teams, especially as AI capabilities are becoming increasingly augmented in ERP systems and standard software. However, not all finance leaders are focused on the biggest upsides – or downsides – of the coming AI transformation.

In my conversations with clients across Asia-Pacific, the following three questions often help to open up internal thinking about both AI risk and opportunity.

How are you monetizing the data you have?

Right now, many finance leaders are focused on the efficiencies AI could deliver – rather than its potential to create new value. Clearly, Generative AI can drive significant productivity improvements in the finance team. However, a bigger prize could lie in monetizing the data the business is already collecting.

For example, a logistics company could use its historical delivery route data to help third-party delivery businesses optimize their fleet efficiency. A manufacturer could develop AI algorithms trained on its production data and license them to those looking to improve their assembly lines. Or a bank may examine the value of aggregated (anonymous) spending trends to consumer brands looking to understand economic activity by sector and geography.

The finance function is ideally positioned to understand what data is being collected and how it could be turned into additional revenue streams.

How are you using AI to reinvent processes?

The other big innovation opportunity is reinventing processes. Already, in some leading finance teams, forecasting is being turbo-charged, as overlays of new data sets (some suggested by Gen AI) ramp up predictive performance. For example, AI applied to real-time geospatial data from satellites is revolutionizing the way the agricultural sector can predict and optimize crop yields.

The move from sampling to being able to interrogate whole populations (e.g., hundreds of thousands of expense claim invoices) is also hugely transformational for finance as it can provide a mechanism for the identification of anomalies, a particular useful capability in the identification of fraudulent transactions.

Equally, rather than just relying on people to handle the new nonfinancial reporting compliance requirements, finance leaders are using AI capabilities to extract information from disparate datasets for efficient management and compliance monitoring and reporting.

Have you got the appropriate guardrails in place?

When our survey asked the region’s finance leaders about the challenges of using AI solutions to develop trusted and credible disclosures, only 35% of respondents ranked “designing effective governance and accountability frameworks,” as one of their top three key concerns. This is not because governance is already in place. The EY Global Integrity Report found that roughly two in five (40%) organisations using or planning to use AI have measures, like ethical standards or controls around privacy and fraud risk, to manage its deployment and use.

As finance teams explore AI, governance and controls should be a top priority across the region. Providing trustworthy, reliable and accurate results will be fundamental for finance teams to continue as a trusted business partner to the business, while also creating new value through higher-quality insights.

When considering AI initiatives, finance teams should be prepared to answer questions such as:

• How do we know AI-generated data is reliable and free of bias?
• What processes do we use to assess and monitor AI risk?
• What training data has been used to train our AI tools?
• How do we know the data is not out of date, that we have the right permissions and it complies with privacy laws?
• What AI training have we provided for our people?

Some teams resist implementing AI governance on the basis that putting processes and controls in place will slow them down. In fact, the opposite is true. Once guardrails are in place, teams can move a lot faster with AI adoption, including confidently upgrading AI tools as new capabilities become available.

For this, and so many other reasons, there’s nothing to be gained from waiting to put guardrails around AI tools for finance. The opportunities are vast and those who appropriately manage risk early stand to reap the benefits faster.

See the EY Global Corporate Reporting Survey and EY Global Integrity Report for more information and actionable insights. Reach out if you would like to learn more.