By Kishan Sundar, Senior Vice President, Chief Technology Officer, Maveric Systems
Artificial Intelligence in banking and financial services has matured beyond pilot experiments. Institutions are no longer asking if AI works, but how to scale it across the enterprise to unlock value in customer service, decision intelligence, and operational efficiency. However, the ability to scale AI across the enterprise remains limited. The problem is not with technology readiness. The gap lies in the lack of structured translation between business ambition and operational execution.
The Isolation trap: Why pilots don’t scale
Many AI implementations are reactive. A line of business may propose a pilot to demonstrate potential gains and secure buy-in. While such efforts may achieve early results, they often remain confined to specific use cases. The bigger challenge is that these projects are not grounded in a broader business performance framework. As a result, they do not translate into sustained outcomes or replicable models. If scale needs to be achieved, there should be a clear understanding of what the business wants to achieve, and the focus must shift from building models to transforming functions.
Defining the gap between current performance and the target state is essential. AI will remain experimental if there is no structured assessment of where the business stands and the outcomes it is targeting.
A structured framework for scaling AI
A successful AI-at-scale approach begins with business reenvisioning. It is a process that involves evaluating where the institution stands compared to industry benchmarks and defining specific goals for improvement. Once the target state is established, the focus shifts to realigning the roles, processes, technology architecture, and data models needed to attain the goal.
This stage is not about replacing individuals but redesigning roles to fit new workflows. Operations and technology must be reimagined to support the transformation. A lending function, for example, may need redesign in how creditworthiness is assessed, how documents are processed, or how customer engagement is handled.
The next stage is the elaboration and design phase, involving identifying data requirements, setting up AI governance norms, and designing the technical architecture. Explainability, accountability, and ethical use are the key drivers in this stage. Models are tested at the build and verify stage. Feedback loops are established to ensure output aligns with business objectives and compliance expectations. For models to run reliably in a production environment, efforts are undertaken for continuous performance tracking, bias detection, and controlled model enhancement. At this stage, AI becomes an operational component, not a standalone initiative.
Scaling requires platforms and data readiness
Scaling AI is not possible without an integrated platform strategy. Projects falter when execution, monitoring, and support are handled separately. A platform-based approach allows institutions to standardise how AI is initiated, deployed, and managed across different functions. This approach ensures consistency in governance and simplifies integration with existing systems.
Data readiness is essential for AI, as it cannot function on poorly classified data. Institutions must focus on provisioning curated datasets with the right permissions, masking sensitive elements where required. Organising data for AI consumption is a critical step, often overlooked, that determines whether a model can move from lab to field.
In parallel, the solution strategy must balance proprietary models with market-adapted ones. Not every use case requires a custom build. A curated marketplace of AI solutions, combined with targeted internal development, allows for both speed and contextual relevance.
Thinking in terms of portfolios, not pilots
Instead of selecting isolated use cases, institutions benefit more from reimagining entire business portfolios. This means asking how AI can reshape the end-to-end lending lifecycle, or how it can improve the full customer service function. This portfolio-led view drives top-down thinking and makes it easier to map returns against investment.
For example, a lending portfolio that underperforms industry benchmarks by a double-digit margin can benefit from a comprehensive redesign.
The advantage of this method is its flexibility. While some organisations may need end-to-end engagement, others may seek only data infrastructure or governance blueprints. The framework allows for customisation depending on the maturity and goals of each institution.
Closing the loop between ambition and execution
AI at scale requires more than models and dashboards. It requires a deliberate method that connects high-level business priorities with executable AI programs. This connection is often the missing piece. Technology is advancing quickly, but its real impact depends on how well institutions can translate objectives into structured transformation across roles, processes, platforms, and data.
The promise of AI in BFSI is real, but it won’t be fulfilled through isolated experiments or siloed pilots. When business ambition is systematically translated into executable AI programs spanning data, processes, roles, and platforms, organisations can realise their full value at scale.