From AI deployment to business value: The role of technology partners in enabling trusted transformation in financial services

The financial services sector has long been at the forefront of AI adoption. From fraud detection and risk management to compliance monitoring and customer engagement, AI has been embedded in banking operations for years now, with the industry among the earliest to operationalise AI at scale. Yet the challenge today is no longer proving the value of individual AI use cases. Instead, institutions are focussed on moving from isolated deployments to enterprise-wide implementation, supported by the infrastructure, governance, and operational frameworks needed to deliver consistent business value at scale.

According to the Lenovo CIO Playbook 2026, 70% of banking and financial organisations are either piloting or systematically implementing AI across their operations. Confidence in the technology remains high, with 93% expecting positive returns and reporting an average return of $2.48 for every dollar invested. Nearly half of AI proof-of-concepts are already being integrated into operational workflows.

What is changing now is not where AI is being used, but how broadly it is being embedded, that spans operations, compliance, risk management, customer engagement and employee productivity. The conversation is shifting from AI innovation to AI operations.

From AI Use Cases to AI Operations

AI has already delivered measurable value across financial services. Institutions have successfully deployed AI across fraud detection, underwriting, risk management, compliance monitoring, and customer service functions.

The next challenge is scale.

Business leaders are asking a different set of questions. How can AI be deployed consistently across business functions? How can organisations govern hundreds of models and workflows simultaneously? How can AI become an enterprise capability rather than a collection of individual projects?

This shift is particularly visible in India. Nearly 60% of organisations have adopted AI systematically, while 99% plan to increase AI investments over the next year. Overall spending is expected to rise by 19%. Indian organisations project returns of $2.99 for every dollar invested in AI, among the highest globally.

As AI becomes more deeply integrated across the enterprise, institutions are also investing in workforce readiness, skills development and change management. Building AI capabilities is no longer solely about technology deployment. It is equally about enabling people and processes to work effectively alongside increasingly intelligent systems.

The Governance Imperative

Trust has always been a prerequisite for financial services. Customers trust institutions with their data and assets. Regulators demand accountability and transparency. These expectations become even more important as AI assumes a greater role in business operations and decision-making.

This is reflected in current investment priorities. AI security, trust and transparency tools have emerged as the leading area of AI investment over the next 12 months. Financial institutions are placing increasing emphasis on explainability, accountability and responsible AI practices.

At the same time, governance maturity continues to evolve with only 34% of banking and financial organizations have established comprehensive governance, risk and compliance frameworks for AI oversight. Another 40% are actively developing formal structures, while 25% continue to rely on ad hoc or early-stage approaches.

As AI becomes increasingly incorporated into lending decisions, fraud detection, compliance monitoring and customer engagement, governance cannot be treated as an afterthought. Institutions that are making meaningful progress with AI are often those that established clear operating principles, accountability structures and risk management frameworks early in their AI journey.

Governance is not a constraint on innovation. It is an enabler of scale. Strong governance frameworks provide the confidence needed to deploy AI more broadly while maintaining security, compliance and trust.

Infrastructure Becomes Strategic

Scaling AI requires more than algorithms and data. It requires infrastructure capable of supporting enterprise-wide AI operations.

Financial institutions face a unique challenge. AI workloads must coexist with highly regulated environments, mission-critical applications and stringent data residency requirements. Scaling AI therefore requires infrastructure that can support both innovation and control.

Organisations must balance growing demand for computing power with requirements around security, model governance, regulatory audits, and integration with existing core systems. Infrastructure decisions are increasingly becoming strategic business decisions that influence how effectively institutions can scale AI while maintaining resilience and compliance.

This is one reason hybrid environments continue to gain traction. As per latest data, 68% of banking organisations globally and 80% of organisations in India prefer a combination of cloud and on-premises infrastructure. Cloud platforms provide flexibility and scalability, while on-premises environments continue to play an important role where sensitive data, regulatory obligations and mission-critical workloads require greater control.

As organisations scale AI, many are beginning to view AI less as a collection of individual projects and more as an enterprise capability. Building an AI Factory approach, where infrastructure, data, governance and operations work together, can help institutions move from experimentation to repeatable and measurable business outcomes.

Infrastructure is therefore no longer simply a technology consideration. It is becoming a critical foundation for trusted AI operations.

Preparing for Agentic AI

While organisations continue to scale current AI deployments, attention is increasingly turning to the next phase of AI evolution.

Agentic AI is beginning to gain momentum across the banking sector. Already, 27% of banks report deploying agentic AI at meaningful scale. Adoption is expected to grow by 104% across the sector over the next year, while India is projected to see even stronger growth at 139%.

The technology has the potential to reshape how financial institutions manage operations. AI agents could help investigate suspicious transactions, compile supporting evidence, cross-reference regulatory requirements and escalate only high-risk cases for human review. Similar applications are emerging across loan processing, compliance monitoring, customer servicing, and operational workflows.

As these systems become more autonomous, institutions will require stronger monitoring, governance and risk management mechanisms. Maintaining visibility into how decisions are made and ensuring systems operate within defined business and regulatory boundaries will become increasingly important.

The conversation around agentic AI is therefore not only about capability. It is equally about accountability, control, and trust.

The Road Ahead

The next chapter of AI in BFSI will not be defined by how many models are deployed, but by how confidently they can be scaled. Trusted AI, built on secure infrastructure, strong governance, and responsible innovation, will be the foundation of sustainable growth. In a sector where trust is the ultimate currency, the institutions that build it into their AI strategy from day one will emerge as tomorrow’s leaders.

(The author is Managing Director, Infrastructure Solutions Group (ISG), Lenovo India. Views are personal.)