Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Financial services firms are increasingly viewing artificial intelligence (AI) as a transformation lever – to improve efficiency, explore new business opportunities, ensure regulatory compliance, and manage risks.
- Given the myriad complexities BFSI business leaders tackle on a daily basis, it would be unwise to treat AI adoption as a plug-and-play affair – it is a whole new ball game.
- We have outlined a five-point framework for BFSI CxOs to drive value-driven, risk-aware, AI adoption that is closely aligned with the long-term business strategy.
On this page
On this pageinpage
Overview
Strong foundations. Smarter futures.
Our global engagements with financial services leaders reveal a consistent pattern: as AI adoption accelerates across the financial services sector, the C-suite faces a set of fundamental and strategic questions.
- How should we evaluate the business value of AI across the enterprise and within individual business lines?
- What is the real impact of AI and its adaptation for organisational processes on the workforce? What skills must we focus on as we train and retrain?
- Should we build our own models? How about industry-specific small language models? How do we protect our investments in this rapidly evolving technology environment?
- How should AI be embedded into the organisation to generate sustained, long-term business value rather than isolated productivity gains?
- How do we plan our AI journey and prioritise implementation milestones?
Addressing these early enables organisations to embark on their AI journey with clarity, confidence, and measurable outcomes. Thoughtful consideration of these questions will provide CxOs a structured approach to adopting AI in a way that is value-driven, risk-aware, and closely aligned with the long-term business strategy. To that end, we have put together a five-point framework:
- Business value assessment
- Impact of AI on workforce
- AI models for industry
- Strategic infusion of AI
- AI roadmap and prioritisation
Business value assessment
An effective AI strategy is anchored in three core objectives that together define how AI delivers sustainable business value.
These objectives go beyond efficiency gains and focus on transforming capabilities, experiences, and outcomes (see Figure 1).
Figure 1: The three levers of AI-led business value
Differentiated capabilities, products, services: AI will enable financial services firms to offer new products and services to current client segments or create new customer segments – and this is of significant strategic value. An AI-first approach to business offers new capabilities such as organisational knowledge management, agentic automation, and natural language processing, which can be leveraged to strengthen the product and service portfolio.
AI augments human performance by allowing the same tasks to be completed faster, at lower cost, and with fewer resources. What we are suggesting is not replacement, but amplification—freeing skilled professionals to focus on higher value activities.
AI-infused products and services, intelligent credit and risk models, data monetisation, product bundling, and dynamic pricing are some of the key themes that are drawing attention of industry leaders.
Superior customer experience: Another marker of AI-led business value is the customer experience it will enable financial institutions to provide. Through hyper-personalisation, faster access to information, and enhanced quality of service, AI drives higher customer satisfaction and improves engagement, ultimately strengthening trust and long‑term relationships. The value these initiatives generate will be reflected in the customer satisfaction and net promoter scores that financial institutions track regularly.
Enhanced ways of working: This is unarguably the most important aspect of business value generation through AI. Where on one hand, financial institutions will be able to improve operational efficiency by streamlining workflows and reducing manual effort, on the other, the measurable improvements in productivity, cycle times, and operational resilience will deliver tangible value across the enterprise.
The extent of AI impact, however, depends on the nature of work. Repetitive tasks like data extraction, data capture, routine analysis, and content creation are prime candidates for AI-led automation, whereas customer interaction, strategy creation, and insights analysis are augmented by AI tools.
AI augments human performance by allowing the same tasks to be completed faster, at lower cost, and with fewer resources. What we are suggesting is not replacement, but amplification—freeing skilled professionals to focus on higher‑value activities.
Impact of AI on workforce
AI is reshaping how work is performed in financial institutions, not through a single pattern of automation, but across a spectrum of role transformation.
It is a common misconception that AI is going to take away jobs and reduce the need for workforce. However, we believe that the impact varies by the nature of work, the level of judgment required, and the degree of overlap with AI capabilities (see Figure 2).
Figure 2: The human+AI spectrum in the financial services industry
At one end of this human+AI spectrum are leadership and decision‑making roles, where AI primarily acts as an intelligence amplifier. These roles will continue to exist, but will immensely benefit from faster access to insights, improved synthesis of information, and stronger decision support. Accountability, ethics, and final decision ownership remain firmly in human hands.
A second category includes expertise-led and judgment‑intensive roles—such as risk officers, relationship managers, and product designers. These roles remain essential, but AI will significantly enhance human effectiveness and productivity. AI will sharpen expertise, improve analytical depth, and reduce the time spent on preparatory and routine tasks, enabling professionals to focus on higher‑value judgment and stakeholder engagement.
Financial institutions of the future will be:
- Leaner, with fewer resources spent on low value repetitive work
- Augmented, with human expertise amplified by AI
- Superior in decision making, driven by richer, faster, and more contextual insights
- More agile, able to respond quickly to market, client, and regulatory changes
A third category consists of execution‑oriented roles that partially overlap with AI strengths. These roles will exist, but with reduced headcount or redefined skill requirements. AI will automate most routine activities, while human intervention will be required for contextual understanding, oversight, and exception handling.
At the far end are roles dominated by structured, repeatable, and language‑heavy tasks with minimal tacit knowledge. These roles are the most susceptible to full automation by AI agents.
Financial institutions of the future will be:
- Leaner, with fewer resources spent on low‑value repetitive work
- Augmented, with human expertise amplified by AI
- Superior in decision‑making, driven by richer, faster, and more contextual insights
- More agile, able to respond quickly to market, client, and regulatory changes[ss1]
The cumulative impact will be visible across P&L performance, customer satisfaction, productivity, and revenue growth. While large language models excel at language, knowledge retrieval, and synthesis, agentic AI extends this capability into coordinated execution across workflows. However, humans remain indispensable where accountability, ethics, risk ownership, trust, and relationship management are critical.
AI models for the industry
Developing an AI model from the ground up is quite an involved activity and it is not the core competency of financial institutions.
Building a large language model (LLM) or a small language model (SLM) from scratch requires huge efforts to collect and curate data; selecting the right transformer architecture; and massive GPU capacity to train the model. As technology is rapidly evolving with risk-funded capital, it would be sub-optimal for financial institutions to focus their efforts in this area. We believe financial institutions should instead leverage an advanced LLM available in the market and introduce industry- and company-specific context using one of the following methods:
Retrieval augmented generation: This method is used to store organisational knowledge externally to the LLM and pass on the relevant information along with the prompt. This will help financial institutions in keeping their knowledge base revised and up to date, without involving huge costs in retraining the model.
Fine-tuning: Fine-tuning is about taking the original LLM as the starting point and further training it based on use case specific data. This approach should be used to train the model for use case specific characteristics (report generation, claim generation, digital assistant) and branding (tone, language, writing style). Fine-tuning should not be ideally considered for inducing organisational knowledge as it will not provide traceability and requires rework whenever knowledge base is updated. However, whenever the underlying LLM is upgraded, the fine-tuning process needs to be rerun on the latest model to take advantage of its features.
Small language models: Small language models typically replicate the performance of a large language model but with much less processing capacity and time for inferencing. Financial institutions can consider these at a stage when ‘AI for business’ use cases scale up significantly, and if there is a need to cut down on the processing capacity or response time. There are techniques to distil a small language model from a trained LLM, which may be used for this purpose.
Strategic infusion of AI
AI can’t be deployed as an overlay – it must be woven into the very fabric of an organisation.
For most businesses, the AI transformation journey typically begins with experimentation and pilots. Through a series of AI workshops between technology and business teams, high-value use cases are identified and the top few are then taken up to run pilots.
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.
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.
AI roadmap and prioritisation
AI adoption should be a business led exercise, where organisational priorities drive a clear AI business case and roadmap.
Prioritisation requires identifying a comprehensive set of AI use cases, mapping them to existing processes, and defining measurable benefits and success metrics upfront. Targeted proofs of concept (PoCs) are used as a low risk mechanism to validate value, technical feasibility, and organisational readiness before scaling. Insights from PoC outcomes are then fed back into strategy refinement, ensuring decisions are evidence based rather than assumption driven. This framework balances business impact, technical complexity, and time to value to prioritise initiatives effectively.
Overall, the approach enables disciplined experimentation, controlled scaling, and repeatable AI adoption across departments rather than isolated pilots. AI implementation programs should be included in the roadmap after carefully considering the following aspects.
Risks: AI implementations can open several risk vectors for financial services firms: data and privacy risks, reputational risks, accuracy risks, and cyber risks. A detailed evaluation of these risks is therefore a prerequisite and potential control measures should be built in before deciding on the implementation roadmap.
Regulations: AI regulations are still evolving across geographies. The EU AI Act and the US Treasury AI Risk Management Framework for Financial Services provide guidance on compliance requirements based on the maturity and level of AI adoption.
Roles: AI initiatives empower certain critical roles to do their jobs better, while they automate certain others – mostly, routine tasks that do not need much discretion. The impact of AI programs on workforce should be aligned with the overall objectives of the financial institution.
Conclusion
AI goes far beyond IT modernisation – so should your AI adoption strategy.
Financial institutions are beginning to view AI as not just a technology upgrade, but a structural shift in how risk is managed, capital is deployed, and customer trust is built. The standard approach of point experiments will be a good starting point but will not provide the landscape needed for real business transformation.
A holistic approach to AI adoption – decomposing key business lines, transforming them, and aggregating the pieces in a manner aligned with overall AI governance principles – will provide maximum benefit. This will not only provide the much-needed – and often, elusive – RoI, but also enable financial institutions to achieve the triple benefits of product differentiation, efficiency gains, and improved ways of working.
The decompose–transform–aggregate approach provides a structured yet flexible blueprint for strategic AI adoption. It enables targeted experimentation, controlled scaling, and enterprise level value realisation at financial institutions—avoiding both fragmented point solutions and risky big bang transformations.