How to leverage AI in finance processes to improve forward-looking insights

“While AI holds tremendous potential for finance, we will likely have to wait a couple more years before it becomes a real-time decision agent. Finance demands precision and compliance, standards that AI still hasn’t fully achieved yet”, says Stéphanie Struelens Leader Pragmatic Advisory and Implementation for Financial Institutions.

Today, Artificial intelligence in finance can automate routine tasks, enhance data analysis, and empower financial professionals with predictive capabilities to anticipate trends and make more informed decisions. For example, Optical Character Recognition (OCR), a type of AI in Computer Vision and Natural Language Processing, has long been used in bookkeeping to automate document processing and metadata extraction. Though finance may not be the fastest adopter of AI, the gradual yet steady evolution in this area is already reshaping how financial operations are conducted.

This article explores how AI is transforming the finance function, from automating everyday processes to predicting future financial trends, and offers practical advice for businesses looking to implement AI in their finance departments.

The role of AI in the finance department of the future

Instead of an abrupt shift, we are likely to see a gradual evolution where AI will help finance teams work smarter, not harder.

One of the key areas where AI is making an impact is in the automation of routine, transactional tasks. Finance departments typically spend significant amounts of time on manual processes like data entry, reconciliation, invoice matching, and general ledger postings. AI-powered automation can streamline these tasks, saving time and reducing the risk of human error.

Moreover, AI offers advanced capabilities for financial analytics. Predictive analytics can help spot patterns, outliers, and anomalies, enabling better forecasting and budgeting. AI models can analyze large datasets and generate insights that inform decision-making, improving both accuracy and timeliness. For example, AI can predict cash flow patterns, offering better foresight into future liquidity needs, and help organizations plan with a greater degree of certainty.

Only advantages?

With AI, finance departments can operate more efficiently, with a more motivated workforce within the team, as they move away from mundane tasks and focus on strategic and analytical responsibilities. This shift allows more "oxygen" to be allocated to innovation and improvement, beyond just business-as-usual activities and compliance requirements. However, the influx of data and the availability of more insights can also introduce new challenges. 

“With AI generating more information and insights, there is an increased risk of stress and ad hoc requests as teams struggle to reconcile and make sense of complex data sets”, says Johan Reunis, Expert Practice Lead Business Integration. 

The need for constant validation and interpretation of AI-driven outputs can also add pressure to finance teams, requiring new processes for managing this wealth of information while ensuring accuracy and consistency in decision-making.

The Impact of AI on the workforce in Finance

A common concern with AI adoption is the potential for job displacement. In the finance function, the introduction of AI-driven automation will certainly result in the elimination of many routine, operational roles. However, the flip side is that AI will create new opportunities for finance professionals who are skilled in working with data, AI models, and advanced analytics tools. This finance process transformation will require a shift in the workforce, including changes in recruitment strategies and ongoing investment in employee development.

While AI is certainly poised to enhance the role of finance teams, it is unlikely to replace them entirely. As AI becomes increasingly integrated into finance processes, finance teams will need individuals who can interpret the results produced by AI systems and provide critical analysis based on that data. Professionals with data literacy - those who understand how AI models work, can assess the validity of their outputs, and use this information to craft compelling business narratives - will be in high demand.

This shift in roles and responsibilities will require a change in recruitment strategies with a focus on attracting candidates who possess strong analytical finance skills and a solid understanding of data science, machine learning in finance, and AI. Although it is not clear yet whether all these requirements will apply to specific functions or to the team as a whole. Moreover, organizations will need to invest in the continuous upskilling of their current employees, equipping them with the necessary tools and knowledge to successfully leverage AI in their daily operations. As a result, the finance workforce of the future will not only require technical proficiency but also the ability to think critically and strategically about how AI impacts decision-making and business outcomes.

Steps to integrate AI into financial processes

Companies need to have a clear vision of what they hope to achieve with AI. Whether it’s improving decision-making, driving operational efficiency, or enhancing forecasting accuracy, defining a specific vision for AI integration is crucial.

“Without a clear understanding of the desired outcomes, it becomes difficult to measure success and align AI efforts with business objectives” says Olivier De Boeck Expert Practice Leader - CFO Services.

For businesses looking to leverage AI in their financial processes, it’s essential to take a strategic approach. Next to a structural mining track where companies scan all processes until activity level to identify potential cases, they’ll have to start with small pilot projects that focus on specific finance business processes where AI can deliver immediate value. This might include automating routine data entry tasks, implementing predictive analytics for cashflow forecasting, or improving financial reporting accuracy.

However, finance departments often deal with legacy systems, large sets of unstructured data, and unintegrated applications. While AI has the potential to help streamline these complexities, it can also become a hurdle if organizations rush to implement AI solutions without laying the proper groundwork. People often expect immediate, accurate insights from AI, but such outcomes require a preparatory phase to ensure that systems and data are properly aligned. Rushing into AI adoption without addressing these foundational issues can hinder progress rather than accelerate it.

Additionally, organizations should assess their data maturity. AI’s effectiveness is heavily dependent on the quality of the data it is trained on. Poor data quality can lead to inaccurate or unreliable AI outputs, so ensuring that data is clean, reconciled, and well-organized is crucial for successful AI implementation. This is the case for a lot of automation processes as well, where errors or incomplete information can snowball, particularly when AI and machine learning are involved. Since AI models take input as given and rely on data to generate outputs, if that input is flawed, the models can produce incorrect results, which are then used in other models. This creates a dangerous cascade effect, amplifying the impact of any initial errors.

Once a solid foundation has been established - and this could be realized with the help of a specific AI application - businesses can scale up their AI adoption across different financial processes. This might involve expanding AI applications to other areas, such as budgeting, reconciliation, and compliance, as well as integrating AI tools with existing financial systems.

Digital transformation in the finance function: realizing the potential of AI

Integrating AI into financial processes holds tremendous promise, but realizing its full potential requires more than just adopting new technology: it requires a cultural shift within the organization. Finance teams need to view AI as a valuable partner, not just a tool, to enhance decision-making and improve operational efficiency. This transition won't happen overnight, but with the right approach, businesses can gradually unlock AI's full impact in finance. However, this is often more challenging in finance departments compared to others. In finance, there’s a higher need for control and transparency: every figure needs to be traceable to its origin. Accuracy is paramount, and the financial impact of errors can be significant. In finance, even small discrepancies can lead to major issues, making the integration of AI more complex and requiring a higher level of scrutiny.

CFO transformation: As AI becomes more integrated into finance, the role of the CFO is likely to evolve as well. With AI's ability to provide faster, more accurate insights, CFOs will spend less time on historical analysis and more on strategic, data-driven decision-making. For organizations just beginning their AI journey, the key is to start small, focusing on building a solid foundation of data maturity, and gradually scale up as the value of AI becomes clearer. This approach allows businesses to realize the true potential of AI without overwhelming their teams.

Partnering with external experts for AI Integration

Working with external experts or consultants is key when integrating AI into finance processes. These professionals offer an objective, rational perspective, helping to pinpoint the best AI opportunities without the internal biases or emotional attachments that can sometimes influence in-house teams. Finance departments often get caught up in restrictions, exceptions, and potential risks, making it harder to see the bigger picture. External consultants can help challenge these views, guiding teams to adopt a more practical approach and focus on the high-impact areas that will drive the most value. Their expertise ensures that AI is integrated in a way that truly aligns with the organization’s goals and needs.