As regulatory shifts come forth for organisations, the Finance department finds its way juggling priorities to deliver value for the company the best way possible.
The EU AI Act which lays the foundations for the regulation of artificial intelligence, seeks to improve the functioning of the internal market and promote the uptake of human-centric and trustworthy AI.
The regulation also aims to ensure a high level of protection of health, safety, fundamental rights, including democracy, the rule of law and environmental protection, against the harmful effects of AI systems in the European Union and supporting innovation.
In Asia, it is anticipated that the EU AI Act will influence AI regulation as the region navigate around evaluating similar policies and regulations to ensure AI systems respect fundamental rights, safety, and ethical principles.
Asian businesses, particularly those relying heavily on AI technologies, are in it for some hurdles in compliance with the Act's strict regulations, which could require significant investments in compliance and data management.
Leslie Joseph, principal analyst at Forrester, believes companies must go beyond compliance checkboxes and implement robust data governance in connection with the release of the EU AI Act, as regulations will evolve, but finance teams that embed strong data and AI governance today will gain a long-term competitive edge.
Digital transformation
With the Finance team continuing its navigation around the whole digital transformation journey, Joseph observes that they have made notable progress in automating transaction processing, financial planning & analysis (FP&A), and compliance reporting.
"Over the last several years, technologies like Robotic Process Automation and Intelligent Document Processing have streamlined invoice processing, expense management, and reconciliation tasks, resulting in reducing errors and freeing up finance professionals to focus on strategic decision support rather than administrative work," he says.
He notes that AI-driven predictive analytics has also improved forecasting accuracy, particularly in demand planning, working capital management, and revenue projections.
"Many CFOs are now leveraging AI-powered spend analytics to detect cost inefficiencies and identify savings opportunities across procurement, logistics, and operational expenditures."
Joseph thinks that another area of success for the Finance department over the past months is real-time financial reporting, where cloud-based AI platforms automate variance analysis and generate management reports that previously took days to compile.
According to him, CFOs increasingly rely on these tools to provide up-to-the-minute financial insights, helping executives make faster, data-driven decisions.
Joseph points out that AI doesn't just crunch numbers, but rather it actively helps finance leaders see around corners.
"It's fair to say that AI has redefined the role of corporate finance teams, shifting their function from retrospective financial reporting to real-time financial intelligence," he says. "AI-driven audit and risk assessment tools are continuously scanning transactions and financial statements for irregularities, helping finance teams detect fraud risks or compliance breaches early."
He adds that AI-powered scenario planning tools have allowed CFOs to model various macroeconomic conditions, such as interest rate shifts, supply chain disruptions, or new regulatory policies, and assess their financial impact before making key decisions.
"With new LLM-based and agentic tools emerging, AI will further transform how finance teams interact with data. Instead of manually sifting through spreadsheets, CFOs can now ask AI-powered assistants questions like: 'How did our Q4 OPEX compare to forecast?' and receive instant, AI-generated insights, conversing with their data just as they might converse with an experienced analyst, in business lingo."
EU AI Act and Asian businesses
"The EU AI Act will impact Asian enterprises using AI in financial decision-making, requiring auditability, transparency, and explainability of AI models," Joseph reveals.
While compliance will be a challenge, Joseph says some countries are better positioned than others.
"Existing regulatory guidelines in Asia such as the Monetary Authority of Singapore’s FEAT Principles and Japan’s risk-based AI approach provide a structured starting point, making adaptation easier."
However, the Forrester principal analyst thinks these frameworks are guidelines, not regulations, which means that businesses must still align with the EU’s stricter, legally binding rules.
"In contrast, enterprises in countries with less developed AI governance may face a steeper compliance burden. Key hurdles include data localisation, AI model explainability, and real-time risk assessments."
He believes AI-driven financial tools, such as credit scoring or risk analysis models, may require significant reconfiguration to meet EU transparency and fairness requirements.
Joseph says Asia's AI regulatory landscape is uneven, with some countries ahead of the curve and others playing catch-up. For finance teams, the real challenge is not just regulatory uncertainty, but data integrity in AI-driven decision-making.
"AI models are only as good as the data they ingest, and bias, drift, or errors can lead to faulty financial insights."
He believes Finance teams must venture beyond adherence to regulations, and this would include data lineage tracking to ensure transparency in financial models, automated anomaly and bias detection to prevent errors from skewing decisions, and AI model validation protocols to maintain accuracy over time.
Leveraging AI
To enhance the team’s financial analysis and decision-making processes, leveraging AI and other technological advances is now an imperative in the business world.
Joseph believes that AI isn't just about automating finance tasks, but about transforming how finance teams think and operate.
"Instead of static, backward-looking reports, CFOs can now generate real-time, forward-looking insights that evolve dynamically as new data emerges," he points out.
"One of the biggest shifts is AI-powered scenario planning. Rather than relying on fixed assumptions, finance teams can use AI to model multiple economic scenarios, such as interest rate hikes, supply chain disruptions, or regulatory shifts, in seconds, allowing leaders to see risks before they materialise."
Another game-changer, according to Joseph, is decision intelligence, where AI not only flags financial anomalies but suggests actions, such as recommending cost-cutting measures that minimise business impact or identifying strategic investment opportunities based on predictive analytics.
He explains that AI can also help decode market sentiment by analysing financial reports, news, and social signals, offering CFOs a broader, context-aware decision-making framework.
"As of today, AI is most powerful when it augments human expertise, not replaces it. Teams that integrate AI into finance workflows, not just reporting tools, will gain a true strategic advantage. But this also opens an important conversation about how finance teams must learn the skills and create the culture needed to bridge the gap between them and, say, the IT or data science teams."
Advice
In Joseph's view, the best finance teams will not just adopt AI--they will embed it into their culture, workflows, and mindset, while ensuring they move faster, think bigger, and drive more strategic value for the business.
"Maximising AI's potential isn't just about tools, it's about people, culture, and ways of working."
Finance leaders must rethink how teams collaborate, learn, and make decisions in an AI-augmented world.
Leslie Joseph, principal analyst, Forrester
He lists steps for finance leaders to follow amid the changing regulatory landscape and continuing digital transformation:
- First, build AI literacy across finance teams. AI does not replace financial expertise, it amplifies it. CFOs should invest in upskilling programs so teams can interpret AI-driven insights, challenge outputs, and use AI to enhance decision-making.
- Second, reimagine collaboration models. Finance can no longer operate in a silo; cross-functional collaboration with data scientists, engineers, and external AI vendors is essential. Firms should consider building ‘AI finance labs’, dedicated spaces where finance professionals work alongside technologists to experiment with AI-driven forecasting, risk modeling, and automation.
- Third, adapt working rhythms to AI’s strengths. Traditional finance cycles such as monthly closings, quarterly forecasts etc. must evolve into continuous, real-time financial analysis. AI enables a shift from reactive reporting to proactive scenario planning, allowing finance teams to guide strategy in the moment, not after the fact.
