The Data Centers Powering AI Boom In Financial Services
Financial institutions are adopting AI at an increasing rate, a recent study by Goldman Sachs forecasts global power demand from data centers will increase 50% by 2027 and by as much as 165% by the end of the decade. To explore this in-depth, I met with Bill Borden, Corporate Vice President Worldwide Financial Services at Microsoft; John Kain, Head of Financial Services Market Development at AWS; and Toby Brown, Head of Global Financial Services Solutions at Google Cloud, for a discussion about the road ahead for the finance industry.
Video: Bill Borden, Corporate Vice President, Worldwide Financial Services at Microsoft
Unlocking AI Through Data Management
"Financial services has always managed data robustly due to regulatory needs," Borden begins. "Clean, correct, and structured data is foundational for decision-making analytics and for integrating advanced AI tools like generative AI models." Microsoft's recent launch of Microsoft Fabric, a cloud-based data and analytics platform designed to consolidate and manage data across various environments, exemplifies this strategic emphasis.
Google Cloud's Toby Brown amplifies this sentiment, noting historically that financial institutions have been "data-rich but insight-poor." Brown delineates between data "for offense", used to drive business growth, and "for defense", supporting risk management and regulatory compliance. According to Brown, Google Cloud provides a single data system that allows financial institutions like Citi and PayPal to integrate data traditionally siloed in spreadsheets and legacy systems into unified cloud-based platforms with AI models and generative AI tooling built directly in line to accelerate insights and decision-making processes.
AWS's John Kain concurs, adding that "breaking down data silos within financial institutions is critical." He cites successful examples like BBVA, which has leveraged AWS to establish a comprehensive data-sharing framework. Similarly, Goldman Sachs and JPMorgan have commercialized their expertise in data aggregation and analysis, signaling a shift toward more collaborative and efficient data ecosystems.
Video: John Kain, Head of Financial Services Market Development at AWS
Shifts in AI Adoption
AI and machine learning are already deeply embedded in financial operations, though the acceleration of generative AI tools has significantly transformed implementation approaches. Kain highlights how generative AI enables rapid deployment of sophisticated applications without extensive model training previously required, "Customers can now automate and innovate more quickly, significantly enhancing operational efficiency".
Brown highlights real-world use cases, such as marketing personalization, and customer service improvements at Discover, where Google's Gemini assists over 10,000 agents by instantly accessing vast institutional knowledge, transforming service interactions into potential sales opportunities. Brown notes, "Banks finally have the ability to transform cost centers like contact centers into genuine revenue generators."
Borden emphasizes developer productivity, citing GitHub Copilot, which has already demonstrated dramatic productivity gains at Citi, allowing thousands of developers to code more efficiently and securely.
Video: Toby Brown, Head of Global Financial Services Solutions at Google Cloud
Balancing Innovation and Regulation
Despite these successes, AI deployment in financial services isn't without challenges. "The industry naturally prioritizes regulatory compliance," Kain observes. Regulatory frameworks for algorithmic transparency and risk management are already embedded, providing financial firms with a head start compared to other sectors.
Brown emphasizes that risk management remains a crucial focus, noting banks often start with low-risk applications, utilizing Google Cloud's built-in security and compliance tools to mitigate potential risks. Borden also underscores the importance of collaborative engagements between technology providers and regulators, stressing the necessity of responsible AI frameworks to ensure secure, compliant deployments.
Future Forward: AI's Potential
Looking ahead, the trio forecast transformative developments in the next few years. Microsoft's Work Trend Index 2025 report, shows a dramatic rise in "digital labor," with AI-powered agents seamlessly collaborating with human teams. "Insights will be instantly available," says Borden, reshaping workflows and business processes significantly.
Kain predicts a substantial evolution in "agentic generative AI," where sophisticated AI agents autonomously handle complex financial tasks, effectively replacing traditional API-driven systems. This will revolutionize how banks innovate, significantly accelerating application development and deployment.
Brown expresses excitement about multimodal and agentic generative AI capabilities, predicting a fundamental shift in how financial advice is delivered. Rather than static, flat reports and charts, personalized financial guidance could soon be delivered interactively through diverse media like video or podcasts, enhancing customer engagement and financial literacy.
Navigating Tomorrow's AI Landscape
We concluded with a consensus on AI's expansive potential to reshape the financial sector profoundly. Institutions that successfully integrate robust data strategies, effectively balance innovation with regulatory compliance, and harness cutting-edge generative AI tools will position themselves as future industry leaders.
In this evolving landscape, financial services companies must embrace change, leverage innovative AI tools responsibly, and prepare strategically for the transformative impacts ahead. As Brown aptly concludes, the future promises not only technological advancement but a meaningful shift toward enhanced customer experiences and deeper financial empowerment.
More like this on Forbes, 3 No-Code AI Tools Changing How Financial Institutions Innovate and How Financial Services Can Tackle AI-Powered Fraud.
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