The adoption of artificial intelligence (AI) in financial services is transforming the industry at an unprecedented pace. However, not all financial institutions are taking the same approach when it comes to integrating AI into their operations. In a recent discussion, Mike Sha, SigFig’s CEO and Craig Iskowitz, CEO of Ezra Group, shed light on the contrasting strategies of early adopters and pragmatists within the industry.
Early Adopters vs. Pragmatists:
In the realm of AI adoption, a distinct divide emerges between early adopters and pragmatists. Early adopters are organizations that eagerly embrace cutting-edge technologies, often serving as the first testers of new products and solutions. These firms are typically driven by innovation and the desire to gain a competitive edge by implementing the latest AI advancements.
Conversely, pragmatists in the financial services industry approach technology adoption with caution. They prioritize reliability, security, stability, and compliance. Pragmatists are reluctant to jump into new technologies without assurance that they are fully developed, secure, and compliant with regulatory requirements. These firms want to avoid any potential pitfalls associated with unproven technology.
Balancing Innovation with Pragmatism:
Mike and Craig agree that while some early adopters enthusiastically embrace AI innovations, many financial institutions prefer a more measured approach. Pragmatic firms often seek “low-hanging fruit” opportunities—areas where AI can bring immediate benefits without triggering regulatory or compliance concerns. The conversation highlights two primary approaches to AI adoption:
- Augmentation of Human Capabilities: This approach focuses on using AI to augment and enhance the abilities of human employees. It involves technologies that work alongside humans, offering suggestions and support without replacing them. Augmentation is seen as a low-risk strategy because it keeps human oversight in place, reducing the potential for errors and unexpected outcomes.
- Automation of Manual Processes: Another approach is to use AI to automate manual processes and tasks, reducing the need for human intervention. This approach can lead to significant efficiency gains by eliminating time-consuming manual tasks. However, it may carry higher regulatory and compliance risks, as automation can result in the potential for errors.
Statistical Models vs. Computational Models:
Another key consideration is the type of AI model used. AI models can be broadly categorized into two types: statistical models and computational models.
- Statistical Models: These models analyze large datasets to predict likely outcomes based on historical patterns. They are well-suited for applications where there is no right or wrong answer but rather more or less effective strategies. Examples include marketing optimization and fraud analysis.
- Computational Models: Computational models aim to provide definitive answers and are considered more risk-prone. They are based on the assumption that there is a right and wrong answer. Implementing computational models for tasks like providing financial advice carries higher regulatory risks.
The dynamics between early adopters and pragmatists in the financial services industry play a crucial role in shaping the AI landscape. While early adopters drive innovation and explore new horizons, pragmatists prioritize stability, security, and regulatory compliance. Striking the right balance between these two approaches is key to achieving AI success in the wealth management arena. By identifying low-risk opportunities for AI adoption and carefully considering the type of AI models used, financial institutions can navigate the evolving AI landscape while mitigating potential risks and ensuring a thoughtful future for the industry.
This blog post is the second installment of our 3-part series summarizing our WealthTech Today feature. You can find the first blog, The Expanding Horizons of AI in Wealth Management: Insights from SigFig, here.
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