The financial services industry (FSI) is increasingly adopting artificial intelligence (AI) in recent years. The results of a recent survey by the Economist Intelligence Unit show that 85% of the respondents (banking IT leaders) have a “clear strategy” for using AI in product and service development. This is also evident in recent hiring trends in banks for AI-related jobs. It’s great to see AI adoption at this scale, but this also makes it crucial for FSI leaders to watch for and avoid some of the following leading pitfalls in AI initiatives.
1. Technology Rabbit Hole
It may be tempting to start an AI use case purely based on technology availability. And one can be easily convinced that a certain solution can solve all their natural language problems. AI teams often fall into this trap by hiring machine language (ML) experts who build a proof of concept without knowing what actual business problems the tech can solve.
Instead, AI teams should focus on top-down buy-in from executive sponsors in the business before researching Python packages. And they should understand the value of AI before choosing solutions.
2. Lack Of Domain Expertise
Technical roles such as data engineer, ML engineer and data scientists make up part of the core on-the-ground AI team. Once executive sponsorship is obtained, AI teams immediately dive into solution mode. But without having deep domain expertise in the business line, these teams often quickly turn to develop a solution that’s technically enticing but doesn’t solve the business problem.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/09/27/the-five-pitfalls-of-adopting-ai-in-financial-services-and-how-to-avoid-them/?sh=2d99bac126ad
1. Technology Rabbit Hole
It may be tempting to start an AI use case purely based on technology availability. And one can be easily convinced that a certain solution can solve all their natural language problems. AI teams often fall into this trap by hiring machine language (ML) experts who build a proof of concept without knowing what actual business problems the tech can solve.
Instead, AI teams should focus on top-down buy-in from executive sponsors in the business before researching Python packages. And they should understand the value of AI before choosing solutions.
2. Lack Of Domain Expertise
Technical roles such as data engineer, ML engineer and data scientists make up part of the core on-the-ground AI team. Once executive sponsorship is obtained, AI teams immediately dive into solution mode. But without having deep domain expertise in the business line, these teams often quickly turn to develop a solution that’s technically enticing but doesn’t solve the business problem.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/09/27/the-five-pitfalls-of-adopting-ai-in-financial-services-and-how-to-avoid-them/?sh=2d99bac126ad
