Artificial intelligence has been a buzzy topic in finance for years, but many organizations are still struggling to extract real value from their AI initiatives. A recent panel of finance and AI experts convened by the Association for Financial Professionals (AFP) shed light on how companies can finally break through and obtain that elusive return on investment from AI.
Key Challenges Holding Back AI Adoption
According to the experts, the top barriers preventing wider AI adoption in finance include:
- Unclear ROI and difficulty quantifying benefits
- Lack of in-house AI/ML expertise
- Absence of a cohesive AI strategy aligned to business goals
- Data quality and integration issues
- Regulatory and compliance concerns
Strategies for Realizing AI’s Potential
To overcome these hurdles, the panelists recommended several approaches:
Start small and iterate: Begin with focused use cases that can demonstrate quick wins, then expand gradually.
Upskill finance teams: Invest in AI/ML training for finance staff to build internal capabilities.
Partner across functions: Collaborate closely with IT, data science and business units.
Prioritize data foundations: Clean, integrate and structure data to enable effective AI applications.
Define clear success metrics: Establish KPIs to measure AI’s impact on efficiency, accuracy and decision-making.
Promising AI Use Cases in Finance
The experts highlighted several areas where AI is already delivering tangible benefits:
Cash forecasting: ML models analyzing historical data and real-time signals are dramatically improving forecast accuracy.
Anomaly detection: AI-powered systems can flag unusual transactions and activities to combat fraud.
Automated reporting: Natural language generation is being used to produce automated financial narratives and insights.
Intelligent automation: AI is enhancing robotic process automation to handle more complex, judgment-based tasks.
Looking Ahead: The Future of AI in Finance
As AI capabilities continue advancing, the panelists predicted several emerging trends:
- Increased use of causal AI and digital twins for scenario planning
- AI-human collaboration models that augment rather than replace finance roles
- Ethical AI frameworks to ensure responsible development and deployment
- Industry-specific AI solutions tailored for different sectors’ unique needs
While challenges remain, the experts were optimistic about AI’s potential to transform finance functions. By taking a strategic, measured approach focused on tangible outcomes, organizations can move beyond the hype and unlock real value from artificial intelligence.
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