Finance operations are not as simple and clear-cut as it was decades ago. Given the imperative to accelerate digital transformation across industries, CFOs and finance teams need to get better at adopting new technologies and driving the integration needed to make these work optimally.
Based on findings from Deloitte’s Asia-Pacific CFO Survey, striking the right balance between broad accounting proficiency and specialised technological skills has become a paramount concern for CFOs in the region: 59% of Asia-Pacific CFOs are already directing their efforts towards offering practical on-the-job experience to their workforce, with the aim of develop essential capabilities.
New regulatory requirements around ESG reporting are only adding further pressure to the CFO offices. For instance, in Singapore, while public companies will be required to disclose climate-related financial information starting in 2025, several organisations still have a limited understanding of the intricate regulatory framework underpinning ESG reporting as well as unreliable data collection and report writing processes.
Based on a recent study, 56% of leaders in Singapore report inconsistent and irregular data collection procedures – all of which can impact ESG reporting.
Amid these challenges, advancements in AI and automation can help propel finance teams to unparalleled levels of proactivity, intuition, and productivity. New computer vision and generative AI capabilities can empower finance teams with the ability to transfer between documents, spreadsheets, and apps; understand content; and automatically insert data in the right places for better invoice management. Meanwhile, process mining capabilities provide deeper insights into daily finance operations to drive workflow improvements.
Intel is a prime example of an organisation effectively leveraging AI and automation to streamline the intricate process of updating product codes for global shipments. Within just four months, Intel classified over 56,000 products with 99% accuracy.
The company also mitigated significant trade compliance risks by reducing shipments on hold and global customs fees, saving hundreds of thousands of dollars.
Amid the constant pressure to increase efficiency in finance operations, how can a combination of AI and automation empower CFOs and their teams to innovate and improve business outcomes? Here are five ways.
1. Democratising innovation
Integrating and training staff in new technology can pose challenges, especially for those who aren't tech-savvy. New automation tools have a user-friendly interface and simple drag-and-drop applications which do not require coding or technical skills.
For finance teams, this streamlines and simplifies the process of onboarding new staff. It also expedites completing time-consuming tasks such as data entry or data transfer, freeing up employees’ bandwidth for more analytical and strategic activities.
2. Cross-platform Integration
One of the biggest challenges finance teams face is navigating across various software platforms. A combination of AI and automation helps ensure fluid data movement and consistency across systems, from Microsoft Excel to many popular enterprise resource planning (ERP) tools including SAP Fiori, Oracle NetSuite, and beyond.
For example, new tools can instantly extract invoice details received via email and populate them in an Excel spreadsheet or platforms like NetSuite. They can also seamlessly copy data from Excel and input it into ERP systems such as SAP Fiori, creating comprehensive customer profiles without manual data entry.
3. Unlocking the potential of unstructured data
Business environments today require finance teams to consolidate and analyse unstructured data from multiple disparate sources such as emails, informal transaction records, or customer requests.
New automation tools can distil data from any source, interpret it with accuracy, and seamlessly transfer it across systems to its intended destination. These can even convert paper documents into digital records and applications in one click.
Finance teams can quickly copy data from Excel spreadsheets and input it into a form on another screen. Innovative software can automate the creation of text prompts for tasks such as generating finance expense reports, with users only needing to issue a ‘run’ command to initiate the process.
This not only saves finance teams a substantial amount of time and administrative costs but also significantly mitigates the risk of human error.
4. Mitigating risks of misinterpretation and misclassification
With advanced cognitive capabilities, new automation tools can complete tasks such as classification, summarisation, content reasoning, and contextual interpretation. Put simply, these can grasp the meaning of a word and assign it to the appropriate category.
For example, if a document mentions “violet” but the input form only offers a “purple” option, the technology recognises the similarity between the two colours and categorises it accordingly. These capabilities eliminate the risk of human error in data interpretation and inadvertent misclassification, thereby reducing administrative time spent rectifying misplaced information.
New context-grounding capabilities are also helping finance teams educate generative AI models by feeding a foundation of business context when users submit their prompts. By leveraging retrieval augmented generation (RAG), the system extracts relevant information from large datasets, such as a company's knowledge base, and utilises it to craft precise and insightful responses. Context grounding makes data LLM-ready by converting it to an optimised format that can easily be indexed, searched, and injected into prompts to improve generative AI predictions. As a result, AI responses are more accurate, domain-specific, and significantly less prone to producing misleading outputs, or ‘hallucinations’--empowering finance teams with more trustworthy AI models.
5. Customising workflows with adaptive AI
As finance teams increasingly utilise a combination of AI and automation, the technology becomes more attuned to their specific needs and evolves with each interaction. As a result, the technology becomes more incorporated into an organisation’s unique workflows and automated tasks can run more smoothly over time.
All of these capabilities make it much easier for finance teams to create and manage entire workflows for diverse tasks, from booking employee travels to generating their complete travel expense reports. Automation can also flag anomalous transactions, leading to more efficient fraud detection and prevention.
At the end of the day, while the synergy of AI and automation does hold the promise of optimised workflows, finance teams must transcend mere surface-level adoption to realise its true potential. There should be a firm commitment towards fostering the right mindset to ensure that finance teams are using innovation right and can understand how to work alongside it to create new value.
