Artificial Intelligence will be an accelerant: For productivity, collaboration and efficiency. But only, when applied with focus. For companies it is time to calibrate their business models to an AI future.
Mastering AI in the finance function
By Cyrus Asgarian and Andreas Poeschl
Where to use AI solutions, how to implement them and proven success factors
AI is giving companies a huge boost, and the potential for performance improvement in finance is particularly attractive. Yet adoption of AI solutions in the function is still low. Our new report aims to support companies to implement the technology, highlighting potential use cases and adoption strategies.
"The implementation of AI solutions in the finance function is not straightforward – CFOs must be fully involved."
Artificial intelligence (AI) solutions have taken the business world by storm in the past few years. Promising more efficient task completion, processes and decision making, they are saving time and money across business functions.
The finance function is a classic example. The repetitive nature of many finance tasks and processes lend themselves to AI, and many companies are already investing in the technology. As a result, AI is driving a transformation in finance, with a shift away from accounting and controlling to value creation. AI solutions are freeing up time for finance professions, facilitating improved data management to aid decision making across companies and enabling better strategies. Indeed, AI-driven automation, predictive analytics and data analysis tools have put finance at the center of corporate steering.
However, a lack of proven use cases is slowing AI’s implementation in the finance function. Our ‘AI in the finance function’ report seeks to address this situation. It uses the results of an exclusive survey of CFOs to gauge the potential benefits of AI in finance, as well as assess leading use cases and outline how best to successfully implement the technology.
AI’s potential in finance
Digitalization is a clear priority for finance leaders, with 70% of CFOs saying it is the most important business topic. However, only 50% of companies have so far adopted AI applications in the finance function. This is despite finance being a great match for AI. For example, finance is heavily reliant on data, AI’s essential ingredient. This means finance can act as a huge data resource to aid strategy development. In addition, one of finance’s main roles is in forecasting, the ideal task for AI-based predictive analytics tools.
Business leaders recognize this potential. Our survey found that 74% of CFOs plan to invest in AI accounting solutions by 2025, and 72% in controlling solutions. Accounts payable and management reporting were the most popular investment targets in a list of common finance processes.
Such investments are likely to pay off. Our research showed that existing AI tools can lead to significant performance improvements in the finance function. For example, companies that have already implemented solutions recorded a 33% reduction in the time taken to approve invoices, and a 25% reduction in the time required for monthly closes.
"Plan key steps in advance and focus on defining targets, involving the workforce and governance structures."
Where to use AI
Targeting the right use cases is crucial to this success. In our analysis, we determined use case suitability using two criteria – feasibility and impact. Feasibility For example, regarding the former, how complex is the process, and does it follow pre-defined rules? For the latter, how many employees are occupied with the task, and how much value does it create?
Using these criteria, we derived 12 promising use cases spanning all finance processes. An efficiency potential was calculated for each based on their potential for time and cost gains. Processes in accounting and controlling had the best efficiency potentials, with gains of between 35 and 40% in accounts payable, general ledger accounting, accounts receivable and planning and forecasting. Savings in tax, treasury & risk management, and investor relations were smaller (8-18%) but still significant. Overall, we found that AI solutions promise to make the finance function 15-20% more efficient.
Our analysis also found that existing AI solutions have the biggest impact in accounting support, financial analysis and anomaly detection. Automation, generative AI and natural language processing solutions have been implemented across all 12 use cases.
Managing AI transformation
So how should companies go about adopting AI solutions in finance? Roland Berger has developed a comprehensive framework for managing AI-driven transformations. It incorporates five core building blocks, from developing a digital strategy and objectives to ensuring close collaboration between business units and investing in talent. Each is supported by a set of enablers. Together, these lay the foundation for smooth transformation that ensures companies can unleash the full potential and value of AI.
When it comes to implementation, we outline a step-by-step approach focusing on defining targets, involving the workforce and developing governance structures. The report also offers several key success factors based on our experience and how industry leaders have adopted AI in the finance function. These include tips on what to invest in, first steps and preparing employees.
For more details, please download the full report or contact one of our experts. We look forward to hearing from you.
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