By Sarah Dziuk, Sr. Director, SAP Insights
Even though finance departments look different from one organization to the next, they all share a common purpose of how to best manage resources to achieve company goals. However, the critical decisions finance departments make, often create long-term effects. This begs the question: How can finance teams stay one step ahead of the latest industry disruptions that impact their decisions? Is AI a double-edged sword that helps democratize access and improve efficiencies for the financial line of business, or does it sideline human intuition and create potential risks?
As we stand at this crossroad, it’s essential to examine and understand the true impact of AI on our financial landscapes, and by extension, on our lives. New research commissioned by SAP Insights identifies macro trends affecting finance organizations. One of the primary goals of this research was to gain a deeper understanding of AI’s impact on the transformation of businesses, including how it affects people’s roles and responsibilities, especially in the area of finance.
Common themes span many of these broader trends. For example, data privacy and cybersecurity were top concerns, as were AI model accuracy and transparency, data management, compliance, and employee experience. Even areas where AI currently is less mature – such as strategic thinking – are nevertheless implicated.
“We are at an inflection point right now, where cloud and AI converge, and the technology is mature enough to deliver what our customers need,” said Jan Gilg, SAP. “As we use AI tools to handle more basic tasks and to provide us with crucial data, people are migrating toward the roles they are still better at than the AI tools. Through our main guidelines to build relevant, responsible and reliable AI features, we are set to support our customers, where it has the biggest impact.”
AI use cases in finance
In many cases, AI tools are used for more mundane tasks, which frees up people to perform the next-level analysis on those transactions and reports, allowing them to make strategic decisions.
Tasks that can now be automated by AI systems include financial report generation, intelligent document processing and review, compliance monitoring, anomaly detection, reconciliations, and tax form automation, among others. AI systems can streamline invoice management by extracting and integrating relevant data directly into ERP systems, minimizing manual entry errors.
AI systems improve communications, too, by personalizing content and better supporting multilingual financial communications. LLMs aid in the creation of clear and coherent reports, summaries, and other materials.
“They also make complex financial information easily understandable for a diverse range of stakeholders,” said Gilg. “Representatives across different finance functions, for example, can use AI to provide immediate, accurate responses to inquiries. By combining AI data retrieval with human problem-solving skills, service levels greatly improve.”
Analysis functions benefit from AI systems, too. For example, generative AI can analyze vast amounts of environmental, social, and governance (ESG) data—often times non-financial performance data—to uncover insights that might not be apparent through traditional analysis. AI systems can gauge market sentiment by analyzing vast amounts of data from news, social media, and other sources, and gauge public opinion about finance and adjust communications accordingly.
Risk management systems take on new autonomous functionality, with security dashboards for monitoring financial health, systems that can assess investment risk, credit risk, supplier risk, or even the compliance with ESG regulations and initiatives.
“In the face of financial crises or conflicts, AI systems can assist in managing and resolving issues by providing real-time data analysis and recommended courses of action,” said Gilg.
Other use cases include simulation and prediction, informing strategy and planning, and market and regulatory compliance research.
Technology enablers
Along with exploring the most popular ways AI is being used, SAP Insights research identified the motivators behind its adoption. They include automating processes and content generation, of course, but it’s more complicated than that.
“Personalization is a primary driver,” said Gilg. “We can use AI to synthesize vast amounts of financial data into personalized content, like financial visualizations and data interpretations, that is tailored to each user’s specific needs. And in addition to learning material being customized just for us, the content itself can learn from us and adapt accordingly.”
Privacy, security, and risk management are drivers, too. AI systems can help with compliance and privacy assurance, as well as secure collaborative data handling; AI capabilities ensure that sensitive data is shared and used in a manner that maintains privacy and security.
“Advanced AI tools can identify, evaluate, and even predict organizational risks,” said SAP’s Jan Gilg, President, Chief Product Officer, Cloud ERP. “Real-time, dynamic data allows for immediate analysis and strategic advice, enabling better decision-making processes that adhere to regulatory standards. In the end it is technology that can enable different business outcomes, ones that help you grow and simplify your business”
The path to adoption
As businesses contemplate and invest in AI solutions, it is worth noting some potential challenges. While a lot of excitement and experimentation is happening, a lack of attention to the social and cultural implications may undermine the decision-making processes and lead to suboptimal outcomes. A failure to consider the implications for employees could result in increased risks and redundancies.
Conversely, taking a wait-and-see approach and delaying a strategic approach to adoption carries the risk that shadow AI projects will ultimately cause inefficiencies. A fragmented path risks lack of common standards. There is a need for recalibration in industries and individual’s expectations from AI. The future will likely require a combination of AI’s computational prowess and human judgement, but the present is about how we decode how AI is shaping our world, one algorithm at a time.
To learn more, you can find the full findings of the SAP Insights research here.
