Tom Padgett is President of the Smarsh Enterprise business, which serves large global banks and financial services organizations.
At some point, nearly every individual has likely interacted with a bank or financial institution—whether applying for a loan, opening an account or using a mobile app. All of these interactions generate valuable data touchpoints: recorded support calls, transaction histories, app usage and internal communications.
Because of financial institutions’ need to balance security, compliance and customer trust, I think this makes them a model case for other organizations on how to navigate responsible use of data in decision-making.
As generative AI continues to change how business is done, organizations must recognize that AI’s return on investment (ROI) is intrinsically linked to the quality of data it leverages to generate outputs. Therefore, I believe the best way to obtain quality data is for organizations to build trust through transparency, strong governance and a focus on strategically deployed AI.
Trust As The Foundation For ROI
Simply put, today’s consumers are more aware of how their data is used. Influenced by privacy regulations like the California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR), trust is no longer just an ethical imperative but a competitive advantage. I believe that when customers understand how their data is being used, they’re more likely to contribute high-quality data, which, in turn, enhances AI performance.
Data Governance: Preventing Costly AI Mistakes
Bad data equals bad outcomes. What’s worse, once AI has started training on bad data, it’s incredibly difficult and costly to fix. Effective data governance ensures AI systems operate on high-integrity information, preventing missteps that could erode trust and reduce ROI.
Strong governance goes beyond just anonymizing user data. It’s about working with the right partners, leveraging the right models and making sure that organizations have the expertise and tools to protect sensitive information.
Financial institutions have long upheld stringent data governance standards, ensuring that customer information remains protected while maximizing its value. I think other industries can learn from this approach, embedding governance frameworks that reinforce both security and customer confidence.
Scrutinizing AI Use Cases
Users and businesses are generating more data than ever before, and I know the temptation to implement AI in every facet of business is strong. However, not every use of AI provides true value. Organizations must scrutinize AI use cases to ensure they contribute to efficiency, accuracy, and revenue.
The stakes are extremely high. Trust and reputation take years to build but can be destroyed in a second by implementing even a single faulty, unproven solution. Recklessly implementing AI for short-term gains can lead to pricey missteps, from biased algorithms to ineffective automation that drains resources instead of driving efficiency.
History remembers companies that push the boundaries of what was thought possible, but sustained success stems from a combination of bold innovation and careful consideration. In the age of AI, the same principle applies.
AI’s Future: Trust, Strategy And Measurable Success
The AI revolution is in full swing, but the focus is shifting from theoretical potential to tangible impact. I am certain that the companies that thrive will be those that harness data responsibly, deploy AI strategically and ultimately prove its financial value.
It’s clear that shared data can unlock transformative insights. By prioritizing transparency, governance and ROI-driven AI use cases, organizations can innovate boldly while maintaining trust, turning AI from an experimental gamble into a lasting competitive advantage.
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