Unlike industries that prioritize rapid technological implementation, financial services must navigate complex challenges when adopting AI. Ensuring AI systems perform reliably is not solely a technological hurdle—it requires addressing foundational issues in data management and implementing oversight to mitigate risks. These elements are critical to maintaining trust and alignment with business objectives in an increasingly data-driven environment.
In a recent episode of Emerj’s AI in Business podcast, Corporate Functions Business Data Leader at Wells Fargo, Nate Bell shared valuable insights into these challenges. Nate emphasized that the success of AI systems depends on two often-overlooked factors: viewing data management as a strategic investment and establishing governance structures to maintain accountability and mitigate risks.
The discussion also touched on the growing need for automation tools to manage vast amounts of data efficiently and the risks of overconfidence in AI outputs. Together, these factors present significant opportunities for financial services leaders to optimize their AI strategies.
In the following analysis of this conversation, we examine two key insights for business and technology leaders:
- Viewing data management as an AI investment: Treating data management as an investment enhances AI accuracy and reduces inefficiencies by automating data processes with tools like APIs and REST interfaces.
- Managing AI risk through oversight boards: Implementing governance boards and structured user education mitigates overconfidence in AI and ensures alignment with organizational goals.
Listen to the full episode below:
Guest: Nate Bell, Corporate Functions Business Data Leader at Wells Fargo
Brief Recognition: Nate Bell is the Corporate Functions Business Data Leader at Wells Fargo, where he specializes in optimizing data strategies to support AI initiatives and address organizational challenges. With a focus on bridging infrastructure investments and innovative AI use cases, Nate provides valuable insights into managing risks and aligning AI technologies with business objectives.
Expertise: Data management, AI risk mitigation, infrastructure optimization, and governance strategies
Viewing Data Management as an AI Investment – Treating data as an investment enhances AI accuracy and reduces inefficiencies.
Data management is often viewed as a “cost center”—a necessary expense to keep operations running but not a direct contributor to business outcomes. Nate Bell challenges this view, describing data management as a “reconciliation factor” that underpins the success of AI systems.
According to Nate, companies that need to pay more attention to the importance of robust data management risk undermining their AI initiatives and exposing themselves to inefficiencies.
One of the primary challenges he highlights is the sheer volume of data enterprises must handle. He explains: “If we assume you have like one petabyte of data, that’s 575 million transfers. Even if you scale that down, that’s still a lot to maintain.”
These transfers often involve multiple systems, creating countless opportunities for data mismatches, errors, and inefficiencies. Without accurate data, AI systems are likely to produce unreliable outputs, which can harm decision-making processes.
To address this challenge, Nate emphasizes the importance of automation tools such as APIs and REST interfaces. He states: “We’re trying to create even more codified and automated ways to do this via, you know, application or APIs, via, you know, REST interfaces, things of that nature. But to maintain it, that’s still a lot.” Automation reduces the manual workload required to validate data transfers, ensuring consistency and reliability across systems.
Automation also plays a critical role in scalability. As organizations generate more data, manual processes become increasingly impractical. By investing in automation technologies, companies can handle larger data sets efficiently while minimizing errors.
Nate suggests that financial services organizations, in particular, must prioritize automation to meet the demands of advanced AI applications. His approach not only improves the accuracy of AI models but also reduces downstream inefficiencies that can drain resources.
Treating data management as an investment rather than an expense requires a shift in mindset. Companies must recognize that the quality of their data directly impacts the effectiveness of their AI systems. While the upfront costs of implementing robust data management systems may seem high, the long-term benefits—improved accuracy, reduced inefficiencies, and greater scalability—far outweigh the initial investment.
Oversight Boards to Manage AI Risks
As AI technologies become more advanced, they also introduce new risks, particularly when users misinterpret or misuse their capabilities. Nate Bell emphasizes the importance of governance boards in providing oversight and accountability for AI systems. He states: “I think keeping the human in the loop is the baseline, it is definitely from a governance and oversight role.”
One of the most significant risks Nate identifies is overconfidence in AI outputs. He describes AI as a “probability calculator” that users often perceive as more human-like than it actually is. This misperception can lead to poor decision-making, as users may place too much trust in AI-generated results without critically evaluating their accuracy. Nate warns: “They’re going to trust the answer a lot more than they should.”
Structured user education is essential to addressing this issue. Nate highlights the need for organizations to educate both internal teams and external customers about AI’s limitations. He explains: “Our customers are largely not going to be that [AI experts] and making sure that they understand what they are actually using.” By helping users understand that AI systems are tools rather than infallible decision-makers, organizations can manage expectations and reduce the likelihood of overconfidence.
Governance boards complement these educational efforts by ensuring accountability at every stage of AI implementation. Nate advocates for oversight structures that review AI decisions and assess their alignment with organizational goals. He asks:
“What governance structures are put in place? What these boards review is whether we should use or move forward on development and who has the authority to make that decision. Those are the types of aspects that we would have to think through and make sure that, as firms do this, that infrastructure is there. So, it’s definitely from a governance and oversight role all the way down to that feedback loop where humans are reviewing the data.”
– Nate Bell, Corporate Functions Business Data Leader at Wells Fargo
These boards play a crucial role in identifying potential risks, such as biases in AI models or cybersecurity vulnerabilities, and ensuring that AI systems are used responsibly.
In addition to mitigating risks, governance boards can help organizations align their AI initiatives with broader business objectives. By setting clear guidelines for AI development and implementation, these boards ensure that AI systems contribute to the organization’s strategic goals.
Aligning these systems is particularly important in financial services, where the stakes are high, and the consequences of poor decision-making can be severe.
Nate closes by emphasizing that combining robust oversight with structured user education creates a comprehensive approach to managing AI risks. Organizations that adopt these strategies can navigate the complexities of AI implementation while ensuring that their systems remain reliable, accountable, and aligned with their business goals.
