Some of the successful pilots are rolled out to limited production; mostly, these are internal or inward-facing initiatives. It is unfortunate, yet not uncommon, that most financial services firms hit a pause here. The journey ahead remains unclear after these initial pilots as benefit realisation is limited, with no significant RoI to make the case for a grand rollout.
To maximise the RoI, financial institutions must take a holistic approach to strategically infusing AI into their business. We recommend a decompose → transform → aggregate approach for long-term business value instead of isolated use case-based approach.
The decompose → transform → aggregate approach is designed to systematically apply AI across complex business architecture by breaking down scale and complexity into manageable, value‑oriented components and then reassembling them into enterprise‑level outcomes.
Decompose: Financial services businesses operate through deeply interconnected value chains spanning markets, business processes, risk, data, and enterprise functions. The first step is to decompose the business into discrete domains, processes, and decision points. This allows organisations to identify specific, high‑impact workflows where AI can meaningfully augment human effort instead of embarking on monolithic transformation programs. For example, investment management organisations can be decomposed into several functions like front office, research, customer management, investment advisory, and risk management. Each of these functions can individually be taken up for AI treatment. By decomposing a function into smaller parts, business leaders are better equipped to make decisions, as there emerges deeper clarity on how effort is consumed and where latency, risk, or cost is introduced.
Transform: Once decomposed, each process or decision point is reimagined using AI capabilities, ranging from descriptive intelligence to agentic execution. Transformation does not imply uniform automation. Instead, AI is selectively applied based on context—augmenting human judgment where accountability and expertise matter, and automating routine or repeatable tasks where speed, consistency, and scale are critical. This stage focuses on embedding AI in a strategic manner based on business value considerations, ensuring controls, approvals, and risk considerations are built in right from the beginning, rather than layered on top.
Transformation can be analysed from a persona-based view or from a business process view. Persona-based transformation looks at some of the key persona such as relationship manager, portfolio manager, risk manager, and looks at all AI interventions to automate some tasks and augment the role. Process-based transformation looks at business processes like investment research or client onboarding and identifies all AI interventions to make the process efficient.
Aggregate: The final step is to join the transformed processes back into an integrated enterprise view. Individually optimised workflows, when connected, unlock disproportionate value—cross‑functional insights, faster cycles, improved risk visibility, and better strategic decision‑making. Aggregation ensures AI initiatives do not remain isolated pilots but instead contribute to cohesive business outcomes, such as improved client experience, stronger market integrity, operational resilience, and scalable growth. It is pertinent to note that for this aggregation to be seamless, financial services firms will need to use a horizontal AI technology platform, while ensuring policies around responsible and explainable AI, data management, and regulatory compliance are followed across business functions.
