Profitability & Cost Management Shared Interest Group

10 Reasons Why Finance Professionals May Overhype AI

By Pedro San Martin posted 02-14-2025 12:31 AM

  

The financial sector is lively with discussions about artificial intelligence (AI) and its potential to transform corporate finance, accounting, and strategic decision-making. AI-powered forecasting models, risk assessments, and automation solutions are being presented as game-changers for CFOs. However, a more critical analysis indicates that AI may not be the ultimate solution that some claim it to be. While AI has potential, finance professionals must recognize its practical limitations.

Here are ten reasons why AI may be overhyped for the CFO's office and finance professionals:

1. AI Lacks True Financial Intelligence
At first glance, AI-powered financial tools seem revolutionary, processing vast amounts of data in seconds. However, they lack real financial judgment. While AI models excel at spotting patterns, they struggle to understand macroeconomic shifts, geopolitical risks, or regulatory changes—factors significantly affecting corporate financial and investment decisions.

2. Hallucinations and Errors in Financial Analysis
AI-generated financial insights can sometimes be inaccurate or produce "hallucinations," where models generate misleading conclusions. In finance, even small errors can lead to significant losses. AI's occasional inability to differentiate between high- and low-quality data sources can result in erroneous risk assessments or incorrect financial forecasts.

3. AI Struggles with Complex Regulatory Compliance
Financial regulations are constantly evolving, with various jurisdictions imposing nuanced compliance requirements. While AI models can assist with regulatory reporting, they cannot fully grasp the complexities of frameworks like IFRS, GAAP, or evolving ESG disclosure mandates. A CFO who relies solely on AI for compliance may expose themselves to legal risks and reputational damage.

4. Over-reliance on AI Can Undermine Strategic Decision-Making  
Financial leaders make critical decisions that require intuition, industry expertise, and strategic vision—qualities where AI falls short. Although AI can analyze historical data, it cannot predict unexpected or disruptive events, such as supply chain interruptions or financial crises. Over-reliance on AI could lead to poorly conceived investment strategies or inefficient resource allocation.

5. AI Cannot Fully Automate Risk Management
Risk management requires a combination of quantitative analysis and qualitative judgment. While AI can detect anomalies in financial transactions and identify risk patterns, it lacks the human intuition needed to assess market sentiment or regulatory intent. CFOs must balance AI-driven risk models with human oversight to avoid false positives or costly errors.

6. Limited AI Adoption in Finance Functions
Despite the hype surrounding artificial intelligence (AI), its adoption among finance teams remains relatively low. A recent study found that only 5% of companies actively utilize AI for financial decision-making. Many Chief Financial Officers (CFOs) are skeptical about the cost-benefit ratio of AI, particularly given the significant expenditures required for infrastructure, talent acquisition, and ongoing model maintenance.

7. Overstated Productivity Gains 
While AI has the potential to enhance financial forecasting and automate repetitive tasks, its overall impact on productivity remains debatable. For example, AI-generated financial reports still require human review to ensure accuracy and compliance. Furthermore, AI tools can lead to inefficiencies if finance professionals spend more time correcting model results than engaging in strategic analysis.

8. Opaque and Biased AI-Powered Financial Models
Many AI-powered financial models function as “black boxes,” meaning their decision-making processes are not easily interpretable. This lack of transparency can pose a significant liability for CFOs, who must justify financial decisions to boards, investors, and regulators. Additionally, AI models trained on biased data sets can inadvertently reinforce systemic biases in lending, risk assessments, and investment decisions.

9. Increasing Data Security and Privacy Risks
Financial data is among the most sensitive corporate assets, and using AI systems heightens cybersecurity risks. AI-powered financial applications often require extensive data integration, exposing organizations to potential data breaches, fraud, and regulatory scrutiny. Without robust cybersecurity measures in place, AI adoption can create more risks than benefits for CFOs.

10. The Irreplaceability of Human Expertise in Finance
Ultimately, the most significant limitation of AI in finance is its inability to replace human expertise. Financial strategy, mergers and acquisitions (M&A) decisions, and capital allocation require judgment, negotiation skills, and ethical considerations that AI cannot replicate. AI should be viewed as a complementary tool, not a stand-alone decision-maker.

Conclusion: A Balanced Approach to AI in Finance
AI has enormous potential to improve financial decision-making but cannot replace human expertise. CFOs and finance professionals should adopt a realistic perspective on AI integration, leveraging its strengths while recognizing its limitations. Instead of getting swept up in the hype surrounding AI, finance leaders should focus on incorporating it in ways that offer tangible value without sacrificing strategic oversight, regulatory compliance, or ethical standards. 

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Author: 
Pedro San Martín is a Principal at Asher / PwC Interamericas. He can be contacted at psanmartin@asheranalytics.com

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