As banks move from experimenting with artificial intelligence to deploying it at scale, Dennis Gada, Executive Vice President and Global Head of Banking & Financial Services at Infosys, talks to Padmini Dhruvaraj of TNIE about shifting AI investments, business gains, enterprise-wide transformation, legacy modernisation, and the growing importance of AI security. Edited excerpts:
1. What are you seeing as the biggest gap between banks experimenting with AI and banks actually putting it into production? What are the main reasons pilots fail to progress?
The real divide across global banking is no longer experimentation versus production—it is between institutions that can make AI work inside a controlled sandbox and those equipped with the enterprise data architectures, governance frameworks, and operational discipline to run it dependably at scale.
Insights from our Infosys Bank Tech Index show that 59% of deployed AI initiatives are already delivering tangible business value. Concurrently, banks have introduced stringent ROI filters: pre-deployment project cancellations rose by 33% between late 2024 and late 2025.
When AI pilots stall or fail to reach production, the breakdown consistently traces back to three structural impediments: Absence of Measurable Unit Economics; Data Debt and Architectural Fragmentation; and the “IT Project” Trap vs. Business Model Redesign.
2. How is the nature of banks’ AI spending changing?
We are seeing a decisive capital rotation. Today, capital is flowing into the underlying plumbing required to make AI enterprise-grade: unified data platforms, hybrid cloud environments, zero-trust cybersecurity, and legacy core modernization.
Banks have recognized that frontier foundation models are rapidly commoditizing. The sustainable differentiator is not the base model; it is the bank’s proprietary data estate, the orchestration middleware, and the governance guardrails governing execution.
Rather than indiscriminate public cloud migration, institutions are building resilient hybrid and private cloud foundations, with private cloud allocation projected to grow to 33%.
3. What are the most significant business outcomes you have observed from banking AI deployments so far?
The most substantial, repeatable returns are emerging from human augmentation and high-volume process acceleration, rather than full end-to-end autonomous decision-making.
In our research, customer service represents the highest single value-creation engine in banking (22%), while software engineering and cybersecurity lead on operational cost takeout.
At Citizens Bank, the deployment of their AI-powered assistant, CiZi, enables retail customers to execute transactional queries, statements, and card operations instantly. Since its rollout, inbound call volume into contact centers originating from the mobile app has dropped by approximately 44%.
For a Tier-1 US financial institution, we implemented an AI agent copilot across mid-office service operations that eliminated over 5,800 hours of manual lookup and processing effort.