As businesses delve into the realm of generative AI tools, CFOs need to expand their from the intricacies of AI models to the foundation that drives them: data.
Dennis Gada, the global head of banking and financial services at Infosys, emphasizes the pivotal role of data in leveraging AI's power. "The most important challenge in AI lies in data," he explains. "Without streamlined, clean, and integrated data, the benefits of AI remain limited."
With nearly two decades of experience at Infosys, Gada is well-versed in the evolution of technology trends. While AI isn't entirely new, its recent advancements, such as GenAI, have accelerated its potential applications. However, Gada underscores that these applications are only as effective as the quality of the underlying data. As Infosys's chairman aptly puts it, "AI models may change, but data remains permanent."
For CFOs, laying the groundwork for robust data pipelines is imperative before integrating new GenAI tools. This ensures that data flows securely and compliantly throughout the organization. Despite the excitement surrounding AI's capabilities, implementing it in highly regulated industries like financial services poses challenges. Many firms struggle to extract data from legacy silos and adapt to outdated technology systems.
Nevertheless, finance leaders are enthusiastic about integrating GenAI to simplify day-to-day operations. Gada notes that CFOs often seek to apply AI across various functions, from accounting to financial reporting, to enhance efficiency and accuracy.
To unlock the full potential of GenAI, CFOs must not only optimize their data pipelines but also gain a transparent view of the information driving these pipelines. With emerging technologies evolving rapidly, maintaining clear visibility into the data fueling AI models is paramount. Gada emphasizes the importance of this visibility, noting that rapid decisions made by GenAI can pose compliance challenges if not monitored effectively. CFOs must strike a balance between advancing AI capabilities and managing associated risks. As data privacy, access, and ownership become focal points, regulators worldwide are scrutinizing GenAI's usage, highlighting the need for robust frameworks to navigate these complexities.
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