Videos
Making AI Real in Financial Services: Insights from Stanford with Infosys' Dennis Gada
Insights
- AI in financial services is moving beyond theoretical discussions. Industry leaders are now focused on actionable use cases, proven implementations, and replicable enterprise strategies that demystify AI and deliver tangible value.
- Enterprise adoption requires navigating more complexity—including data privacy, legacy systems, regulatory frameworks, and stakeholder diversity. Discussions at the Stanford event highlighted the need for tailored approaches and scalable frameworks.
- AI success is impossible without a robust data strategy. The sessions emphasized that data availability, quality, and integration are critical prerequisites—more important than the AI models themselves. Without it, AI remains just a surface-level solution.
How can financial institutions turn AI ambition into action?
Recorded at The Business and Economics of AI workshop co-hosted by Stanford University and Infosys on May 14, 2025, this short but insightful interview features Dennis Gada, Global Head of Financial Services at Infosys.
Dennis shares a practical perspective on enterprise AI adoption, emphasizing:
- The critical role of data readiness as the foundation for AI success
- Why collaboration between academia and industry is essential to drive innovation
- How Infosys Topaz is helping financial services firms accelerate AI-driven value
With real-world examples and candid reflections, Dennis reframes the AI conversation—from hype to impact, from cost savings to growth opportunities. A must-watch for leaders in banking, insurance, and financial services looking to scale responsible, high-impact AI.
Dennis Gada:
We are here on the Stanford campus, the Human AI Institute with clients and leaders for the financial services industry to really demystify the hype around AI. There's a lot of talk around what AI can do, what is the art of the possible, but I think what we’re trying to discuss here is: how do we make it real?
What are the use cases that apply in the industry? What are some of the learnings from AI programs and AI initiatives that have been done so far, which enterprises can adopt? And then, looking at the future, what more is possible?
I think AI is going to fundamentally change all industries, including financial services. But it's about how you prioritize? What are the pitfalls that you should take care of? And more importantly, how do you make things happen—so that it just not becomes a conversation, but something that can really benefit your business, whether it's your customers, your employees, or any other stakeholders.
The theme is to learn how to make AI real. And enterprise AI being different from consumer AI. So also the focus of what are the challenges that enterprises normally face and how some of those can be resolved. We have some great faculty members at Stanford here who are sharing those real experiences based on the research that they do as well.
AI is like the tip of the iceberg. Underneath that, the underlying systems and core data need to be ready and available to draw the power of AI. And I think some of that came out very strongly in the morning sessions of how critical and fundamental it is to get the data strategy right and build those models which will give the intelligence that AI has the power to do.
I think with AI, a lot of the discussion always becomes around productivity and efficiency. Will it save 20%, 30%, and so on? And I’m sure it will. What I'm more curious about is how will it help grow the business. How will it help in terms of entering new markets, new products, new services, doing much more for same or for less. And really shift the conversation towards growth and expansion of businesses rather than just around productivity.
I mean, there have been many technologies in the past that have driven a lot of efficiencies, but also it has resulted in fundamental shift in how businesses operate. And I think that’s the discussion that is starting to happen, and I’m more curious about that.
I think Infosys Topaz really brings together a great suite of AI offerings. The use cases that we’re implementing for clients, the tools, the models that we have. And it’s a huge accelerator with any new client conversation that we have on AI, having the Topaz framework enables us to really have a jumpstart in terms of how we can realize more value out of their AI strategy and AI programs. So it's been a great accelerator and something that our clients truly value.
