1. Financial Institutions Face a Growing AI Talent Shortage
One of the greatest challenges to achieving AI readiness among financial institutions is finding proficient AI talent. In a survey of CFOs conducted earlier this year, outsourcing company Personiv found that 87% of the executives acknowledged a talent shortage, which limits their institutions’ ability to design, implement and manage AI initiatives effectively. Addressing this workforce gap requires investments in training existing employees and incentives to attract the top AI talent.
2. Bad Data Inputs Lead to Poor AI Outcomes
Successful implementation of AI depends on high-quality, well-governed data. Unfortunately, many financial institutions have challenges with data quality and security. When AI projects are undertaken without sufficient preparation, poor data governance can lead to inaccurate results. Because banks deal with time-sensitive operations, such inaccuracies will reduce trust in AI-driven decision-making.
Guaranteeing data readiness requires institutions to establish robust data governance frameworks, improve interoperability across systems and implement best practices for data management.
3. An AI Regulatory Whirlwind Creates Uncertainty
The creation and evolution of AI policy and regulation presents a critical challenge to the adoption of AI in finance. Shifts in regulatory frameworks could lead to a delay or change of course on AI initiatives. Navigating these uncertainties requires banks and financial institutions to remain agile and adaptable as they formulate their AI strategies, ensuring compliance with emerging regulations.
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