AI in Finance: Unlocking Potential and Overcoming Barriers

The finance function, like many others, is on the edge of a tech revolution, driven by AI and part of a wider wave of innovation. As AI and generative AI gain traction in the market—with its potential to improve forecasting, automate tasks, and enhance strategic decision-making—this technology is set to transform how CFOs manage operations, strategy, and competitiveness. While trustworthy AI use cases for the finance function are still in their nascent phase, it’s estimated that 80% of finance-related activities have AI applications within the next 5 years.¹

Despite this potential, many CFOs feel unprepared to engage with this technology. Russell Reynolds Associates’ H1 2024 Global Leadership Monitor found that, while 69% of CFOs believe understanding AI is crucial for future leaders, only 28% feel confident in their ability to implement it.

Even with the lack of confidence, over 60% of CFOs have taken developing action towards implementing GenAI, with only 1% having successfully implemented it (Figure 1) — behind both CEOs (71%) and Technology Officers (89%). This gap highlights a significant challenge: while CFOs recognize AI as crucial, many are taking steps towards implementing a technology they don’t fully understand. As such, further development and external advising will enable CFOs to take advantage of AI’s benefits.

To uncover the applications of AI in the finance function and empower finance leaders to develop and hire for the skills necessary to enable its power in the finance function, Russell Reynolds Associates aims to uncover:

• The transformative potential of AI in finance
• Barriers to AI implementation in the finance function
• Strategies for successful AI adoption

Figure 1: AI implementation progress among global CFOs

^{Source: Russell Reynolds Associates’ H1 2024 Global Leadership Monitor, n=80 CFOs}

The transformative potential of AI in finance

AI is increasingly being integrated into various aspects of the finance function, promising significant advancements in efficiency, accuracy, and strategic decision-making (Figure 2). Specifically, AI is driving change in:

1. Real-time, and predictive forecasting: AI is transforming financial forecasting and planning processes via predictive analytics. Finance teams can forecast accurate and timely future forecasts using historical company data, as well as broader industry data. Siemens, for example, is harnessing the power of AI to elevate their financial reporting and improve their financial forecasting accuracy.
   
   
2. Optimization: Effective cash flow management always ranks high on CFOs’ priority lists, and AI is proving to be a valuable tool in cash flow optimization, one example of optimization via AI in the finance function. Companies can now leverage AI agents, software entities designed to perform specific tasks, to compile data from all relevant sources to continuously predict cash flows.
   
   
3. Risk management: AI models identify and prevent transaction errors or violations of accounting principles in real time, helping to improve audit and accounting efficiencies. Mastercard is starting to leverage AI to enhance its financial analytics, especially around transaction monitoring and fraud detection. Similarly, PWC has built an AI bot called GL.ai to accelerate the audit process, while simultaneously generating insights to boost efficiency.

Figure 2: AI use cases for transforming the finance function

^{Source: Russell Reynolds Associates’ literature review, 2024}

Barriers to AI implementation in the finance function

The path to AI adoption is fraught with challenges, particularly in areas such as knowledge and expertise, data quality, and system integration (Figure 3).

Key barriers identified by the finance function

1. Knowledge and expertise: Many finance leaders cite a lack of knowledge, expertise, skills, and competencies as the primary barrier to AI implementation. This is especially prevalent in organizations that are in the early stages of exploring AI. Concerningly, only 15% of CFOs strongly agree/agree that their organization has employees with the right technical skills required to implement generative AI solutions.³
   
   
2. Data quality and governance: Poor data quality and challenges related to data governance, compliance, and security are significant concerns, especially as AI models rely heavily on accurate and comprehensive data.
   
   
3. Resistance to change: Both CFOs and next-generation finance talent are concerned about employees’ resistance to change slowing the adoption of AI. This resistance often stems from concerns about integration with existing systems, ethical implications, fear of AI taking away jobs and biases in AI outputs.
   
   
4. Resource constraints: The implementation of AI technologies requires significant time, resources, and investment, which can be limiting factors for finance teams already stretched thin.

Next-gen finance leaders are more attuned to AI implementation barriers

There is a distinct contrast between the concerns of CFOs and next-gen finance leaders regarding AI (Figure 3). Since next-gen finance leaders are more immersed in the organization’s daily operations, they are more aware of—and place greater emphasis on—the key challenges of AI implementation, such as data quality and governance, potential biases, and the broader issues of AI ethics and safety. Most significantly, next-gen finance leaders are almost 2x more likely to view bias in outputs as a barrier to implementation than CFOs.³

Next-gen leaders' concerns highlight the need for discussions on strengthening AI policies related to data quality, use cases, and governance. While next-gen leaders are more concerned about these issues at the functional level, only 13% of CFOs agree that their organization has the processes in place to protect itself against AI misuse and mishaps, and only 14% agree that they’re getting the right level of guidance to harness AI ethically and safely.³ This signals a broader issue with organization-level policies impacting AI and highlights the importance of consulting external AI and transformation experts when implementing this technology.

Figure 3: Top barriers for finance teams implementing generative AI

^{Source: Russell Reynolds Associates’ H2 2023 Global Leadership Monitor, n=139 CFOs and next generation finance leaders}

Strategies for finance’s successful AI adoption

A thoughtful and strategic approach to implementation will be critical in advancing finance’s AI capabilities, including:

Research and market analysis: Engage in comprehensive research to understand AI's potential within the finance function, ensuring readiness for market shifts. Check-in with your industry CFO network to learn who is ahead of the change curve and the best practices they’ve adopted.

• Who are the industry CFO leaders who are ahead of the change curve?
• What are the best practices they’ve adopted for the key use cases you see opportunity in?

Partnering with technology: Core to AI enablement is an “enterprise mindset,” across the C-suite, in which leaders focus on the success of the team and the company, rather than on their specific function’s prowess. Key to this mindset is collaborating with tech leadership, both to expedite AI implementation and mitigate risks. Ask the following to begin defining what this tech-finance partnership might look like:

• Who owns AI functionally and where do they sit in the organization?
• What role does AI play in the 5-year vision for your organization?
• How are you partnering with the owners of AI in the organization?

Developing use cases: Investigate where AI can be leveraged in your current finance processes.

• What are the low-effort, high-reward AI applications in the finance function?
• As proficiency grows with AI, ask what are more complex implementations of AI that are high-reward?

Assessing the finance team’s AI capabilities: First, assess your Finance talent for their readiness and potential to implement AI, by creating Leadership Portraits for your Finance executives. Then, reconcile where skills need to be developed, versus outsourced or recruited. Ask the following to begin understanding your team’s AI skills and aptitude:

• Do you have the right capabilities and people to address transformational changes in the finance function?
• How are you assessing your finance team to ensure they are innovative, versatile, and curious?
• How do you plan to continuously develop the skills needed on your team?
• Do you need to hire externally to fill any gaps on your team?

The GenAI opportunity is in the hands of the entire organization. The most powerful approach to AI transformation equips the entire organization—including finance—with the tools and leadership to drive change across the business at every level.

Authors

Fawad Bajwa leads Russell Reynolds Associates’ AI practice globally. He is based in New York and Toronto.
Linda Barham leads Russell Reynolds Associates’ Financial Officers practice in the Americas. She is based in Chicago.
Jenna Fisher co-leads Russell Reynolds Associates’ Financial Officers practice globally. She is based in Palo Alto.
George Head leads Russell Reynolds Associates’ Technology Knowledge team. He is based in London.
Mohammed Khan is a member Russell Reynolds Associates’ Financial Officers Knowledge team. He is based in London.
Catherine Schroeder leads Russell Reynolds Associates’ Financial Officers Knowledge team. She is based in Toronto.

Sources

_{¹ Generative AI in the Finance Function of the Future, BCG, 2023
² Russell Reynolds Associates’ literature review, 2024
³ Russell Reynolds Associates’ H1 2024 & H2 2023 Global Leadership Monitor
⁴ AI and ML Design Resources, Siemens
⁵ Mastercard leverages its AI capabilities to fight real-time payment scams, Mastercard, 2023
⁶ Harnessing the power of AI to transform the detection of fraud and error, PWC
⁷ Decoding the Future: The RRA Systems View on Leading Through AI Transformation | Russell Reynolds Associates}