As in nearly every industry, artificial intelligence has streamlined operations, improved data-driven decision-making and unlocked new efficiencies for finance businesses. However, its integration presents unique challenges that must be navigated carefully, from ethical concerns to the risk of biased algorithms.
To fully realize AI’s benefits while avoiding common risks and missteps that can derail progress, finance leaders must understand and proactively address the issues surrounding this emerging technology. Here, 20 Forbes Finance Council members offer insights on the biggest AI hurdles currently facing the industry, along with strategies to overcome them.
1. The Inevitability Of AI
The biggest challenge is that AI is coming, whether CFOs are ready or not. However, because not all AI is prime-time ready, CFOs need to identify the right use cases with enough upside potential in which to invest. Right now, those workstreams include automated close, cash flow forecasting, contract intelligence, invoice-to-cash automation and sell-side readiness. – Nick Leopard, Accordion
2. Alignment With Human Values
The biggest challenge that AI poses in the finance business is alignment with human values and ethical code. Leaders need to ensure it operates safely and transparently as AI becomes more powerful. AI is promising, but it requires leadership. Leaders who prioritize ethical innovation will shape AI as a benefit for all of humanity. – Asli Erem, Arsal Management Consultancy
3. Overreliance On AI
AI’s biggest challenge in finance is overreliance, risking amplified biases or volatility. Leaders can counter this with governance blending AI efficiency and human oversight—using transparent, auditable systems, stress-testing for flaws and upskilling teams to challenge outputs, ensuring a balanced approach. – Lawrence Pross, Nexi
4. Acceptance Without Critical Review
It is important for leaders to critically review the results that AI produces and not purely accept them at face value. AI is not a replacement for diligence, experience, intuition and hard work. View AI as a complement, not a crutch. One should embrace AI but realize it’s an emerging technology. AI assists with human decision-making, but as with any decision-making process, if the assumptions are wrong, so will be the outcome. – Brian Lindstrom, HealthX Ventures
5. Gaps Between Finance Experts And AI Specialists
One overlooked AI challenge in finance is bridging the gap between finance experts and AI specialists. Even sophisticated models flounder without aligned teams and modern data structures. Leaders must unify skill sets, upgrade data systems and foster collaboration, ensuring AI drives strategic value, not mere automation, for sustainable, long-term impact. – Mark Kane, Sunwise Capital
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6. Quick Adoption Without Safeguards
AI needs to be adopted quickly. But I would caveat that with “safely.” One of the biggest challenges I’ve seen with quick adoption is that many teams will just “turn it on” and not think of the security implications. Quickly but safely means knowing where the data is going—if it is being shared to train the models, understand if there is a risk of it getting released to competitors. – Jay Korpi, NextLink Labs
7. Lack Of Verification
AI can enhance fraud prevention, streamline coding and reduce attack surfaces, but it’s not a silver bullet. Leaders must adopt a “trust but verify” mindset—rigorously test and validate AI-driven decisions to ensure accuracy, security and compliance. If you choose not to take these necessary precautions, you do so at your own peril. The key is balancing innovation with oversight. – Dean Leavitt, Boost Payment Solutions, Inc.
8. Innovation Without Trust
One of the biggest challenges AI poses in finance is balancing innovation with trust. While AI boosts efficiency, customer experience and fraud detection, it risks bias, data privacy issues and regulatory uncertainty. Financial institutions must build transparent, ethical and well-governed AI systems to protect customer trust and industry integrity. – Monica Hovsepian, OpenText
9. Lack Of Imagination
The biggest challenge posed by AI is a lack of imagination and lulling the sector into a false sense of security. Leaders need to be skeptical that everything can be done by AI when there are so many elements that will still rely on the involvement of humans. While adopting AI is crucial to moving forward, there are still actions that will need to remain human, such as the ability to extend code. – Tomer Guriel, ezbob Ltd.
10. Long-Term Impact On Talent Development
One major challenge is the long-term impact on talent development. AI is becoming more sophisticated and efficient, performing a lot of the work typically done by entry-level analysts, which historically has been the way to train them. Without that technical development, the next generation of professionals may not have the experience or training necessary to do their job in the long run. – Alvina Lo, Wilmington Trust
11. Internal Resistance
One of the biggest challenges AI poses to leaders right now is not embracing it. Embracing AI and encouraging your teams to embrace and experiment, develop guardrails and policies and determine where and how to use it will enable you to keep up with your competition, customers and vendors. – Shannon Power, Scope AR
12. Investing Too Much, Too Soon
AI is evolving rapidly, leaving businesses unsure whether to be pioneers, early adopters or cautious followers. The benefits are immense, but investing too much too soon carries significant risks. The smartest approach? Move strategically—start small, gain hands-on experience and build a foundation before making major investments. Now is the time to explore, not go all in. – Jamie Ellis, Katz, Sapper & Miller
13. Bias
One major challenge we face is bias in AI decision-making, which results in unfair outcomes, especially in credit scoring and loan approvals. To combat this, leaders need to focus on using diverse training data, implementing explainable AI techniques and ensuring human oversight in the process. Doing so can enhance fairness, transparency and trust, ultimately reducing risks. – Tomas Milar, Eqvista Inc.
14. Lack Of Quality Data
One of the biggest challenges AI poses in finance and accounting today is effectively managing the quality of data and leveraging AI-driven automation to realize tangible business value. Leaders can address this by prioritizing robust data governance, aligning AI initiatives closely with strategic business goals and proactively training professionals to integrate and optimize these advanced tools. – Paul Peterson, Wiss & Company
15. Model Drift
One major challenge is model drift, or when customer behavior, market conditions or rules reduce model accuracy. Historical financial models may misjudge economic movements, resulting in bad predictions and actions. Voting on financial decisions, monitoring models and introducing new data into AI systems may help. Dynamic AI control keeps technology running during recessions. – Neil Anders, Trusted Rate, Inc.
16. Evolutionary And Preparatory Challenges
Key AI challenges in the financial business are broadly two-fold: evolutionary and preparatory. The evolutionary challenges relate to privacy, compliance and ethical concerns of using AI. The preparatory challenges refer to legacy infrastructure and a lack of appropriate skills to jumpstart the AI adoption. Organizations will need to make strategic and structural changes to address these concerns. – Sunayana Gutta, Reddit
17. Inability To Foster Personalized Connections
While AI can streamline processes and analyze data faster than ever, clients still need personalized guidance and trust in their advisors. AI can’t replace trust, empathy or personalized advice. Leaders must focus on maintaining strong client relationships and ensuring that technology enhances, not diminishes, the personal connection clients rely on. – Michael Foguth, Foguth Financial Group
18. Gap Between Hype And Tangible Gains
One of the biggest challenges AI poses in finance today is the gap between AI hype and tangible, scalable efficiency gains. While AI can streamline workflows, many companies struggle to see real, transformative benefits beyond incremental automation. Finance leaders must integrate AI into existing processes, ensuring solutions are purpose-built, deeply embedded and deliver measurable ROI. – Razzak Jallow, FloQast
19. Impersonation
AI impersonation is a big concern. It poses a threat to financial and personal security. Industry leaders and their businesses are more susceptible to highly engineered and sophisticated attacks by hackers. To protect themselves and their accounts, they should talk to a trusted advisor before responding, establish protocols to follow and implement more comprehensive security features. – Neil Kawashima, McDermott Will & Emery
20. Data Governance
Effective AI requires a lot of data, so data governance can be a sizable obstacle, particularly for fledgling fintech firms. Leaning on enterprise partners, especially those in highly regulated fields, can help finance businesses ensure the data underpinning AI is kept secure and private, as well as free from bias and unintended consequences. – Lindsey Downing, TransUnion
The information provided here is not investment, tax, or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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