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Colorful loom weaving image symbolizing ai and tech strategy for banks.
Author
Namit Agarwal
Senior VP, Customer Success
07 minutes read
11 Nov 2026
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Your AI Stack Is Growing. Is Your Strategy?

In brief

  • Adoption is not the differentiator; business value is, and many banks are still struggling to turn AI implementations into measurable returns.
  • The competitive gap is widening. Leaders are investing in enterprise-wide AI capabilities rather than isolated pilots.
  • Strategy must lead technology. Business priorities, not available tools, should determine AI investments.
  • Governance provides direction. Clear guardrails and accountability create the foundation for responsible AI adoption at scale.
  • Data, people and architecture determine success. These foundations distinguish scalable AI programmes from fragmented experimentation.
  • The destination is an AI-native bank. An institution where intelligence is embedded across decisions, operations and customer experiences.
The Question
What are banks really trying to achieve with AI? More importantly, how do they turn a growing collection of implementations into a coherent enterprise strategy?
The next phase of banking transformation will not be defined by the number of AI tools deployed. It will be defined by how effectively intelligence becomes part of the institution’s operating and business model.

The Master's Take

AI adoption needs a clear direction
Banks are exploring AI across customer acquisition, personalisation, credit assessment, fraud detection, operations, software engineering and cybersecurity.
The 2026 Evident AI Index, covering 50 major banks, highlights accelerating AI maturity. Yet, the challenge is no longer experimentation. It is translating adoption into measurable business outcomes.
The gap is visible across the industry:
  • Pilots struggle to scale. Riverbed's 2025 global survey found that 62% of financial services AI initiatives remained in pilot or development stages, while only 12% had achieved full enterprise-wide deployment.
  • Value remains uneven. BCG's 2025 AI research identified only 5% of companies as achieving substantial value from AI. Its 2026 Applied AI Index indicates that meaningful value creation is becoming more widespread.
India reflects a similar transition. Zeta’s 2026 CXO survey, covering 40 executives across 18 banks and NBFCs, found that 70% of responding chief data officers reported selective or scaled AI deployment, though data readiness and internal capabilities remain constraints.
Without a common direction, banks risk fragmented investments, overlapping platforms and disconnected pilots.
It’s more than just the technology
An AI strategy is not a shopping list of models, tools or agents. It is an enterprise transformation decision.
Consider lending. AI can accelerate document verification and credit assessment. But the larger opportunity emerges when intelligence is integrated across onboarding, underwriting, decision-making, disbursement and portfolio monitoring.
The focus must shift from automating individual tasks to redesigning entire workflows.
For bank leadership, this requires alignment across four dimensions:
  • Governance and trust: Establish accountability, risk management, security and regulatory controls.
  • Business transformation: Identify processes, products and customer journeys that can be reimagined.
  • Technology and architecture: Build reusable platforms, integration capabilities and model infrastructure.
  • People and operating model: Develop skills, ownership and organisational capacity.
Among the various regulatory and industry guidelines addressing responsible AI adoption, the FSB’s sound practices and the RBI’s FREE-AI framework offer useful reference points.
The FSB’s 12 sound practices
The Financial Stability Board’s October 2026 report on responsible AI adoption outlines 12 practices covering organisational governance, AI lifecycle management and cross-cutting risks
It emphasises board oversight, clear accountability, data governance, model monitoring, human supervision, cybersecurity and third-party dependencies.
The message for boards is clear: AI governance cannot remain confined to technology and compliance teams. It must be integrated into enterprise strategy and risk appetite.
The RBI’s FREE-AI framework
In India, the RBI’s FREE-AI Committee Report (August 2025) outlines seven principles for responsible AI adoption and 26 recommendations across six pillars: Infrastructure, Policy, Capacity, Governance, Protection and Assurance.
For banks, this reinforces the need for institutional AI policies, defined accountability, lifecycle controls, data protection and appropriate human oversight.
Both frameworks reinforce an important principle: responsible AI is not a constraint on innovation; it is a foundation for scaling it with confidence.
Start With the Business, Not the Technology
Governance defines the boundaries within which AI operates. Business strategy determines where it creates value.
Before evaluating platforms or models, banks must establish their three-to-five-year ambition. The starting point should be the business problem:
  • Where are operating costs disproportionately high?
  • Which customer journeys create friction?
  • Where do manual decisions constrain growth?
  • Which risks can be identified earlier?
Only then should banks determine whether AI, analytics or conventional automation is the appropriate solution.
The business case is both defensive and offensive
McKinsey’s Global Banking Annual Review 2025 estimates that AI could deliver net cost reductions of up to 20% across the banking industry.
However, the opportunity is not limited to efficiency. McKinsey also highlights how AIpowered customer agents could increase competition for deposits and financial products, potentially putting up to $170 billion of global bank profits at risk in a scenario where banks fail to adapt.
AI strategy must therefore address both sides:
  • Defend the business: Improve efficiency, strengthen risk management and reduce fraud exposure.
  • Create new opportunities: Enhance customer experiences, improve product relevance and explore new distribution models.
Measure outcomes, not implementations
Every priority AI initiative should have an accountable business owner, a measurable baseline, defined targets and clear criteria for scaling or discontinuation.
For example:
Business priority AI application Success measure
Customer experience Intelligent service Resolution time, retention
Lending Document intelligence Processing cost, decision time
Fraud Anomaly detection Fraud losses, false positives
Operations Workflow automation Productivity, turnaround time
Technology AI-assisted engineering Development cycle time, quality
The strategic objective is to move beyond isolated successes towards reusable enterprise capabilities.
The Data Foundation Cannot Be Overlooked
AI strategy is only as strong as the data supporting it. Fragmented systems, inconsistent definitions and poor data quality can limit AI’s effectiveness, particularly when AI agents interact with multiple enterprise applications or influence consequential decisions.
Gartner’s 2025 research on AI-ready data found that 63% of organisations either lacked or were unsure whether they had the right data management practices for AI. They also predicted that 60% of AI projects unsupported by AI-ready data would be abandoned through 2026.
A practical data agenda should address four priorities:
  • Integration: Enable secure access to consistent customer and transaction information across systems.
  • Quality and lineage: Establish data ownership, quality standards and traceability.
  • Privacy and protection: Align data usage with applicable privacy regulations, including India's Digital Personal Data Protection framework.
  • Reuse: Build governed data products and common capabilities that support multiple AI applications.
The objective is not necessarily one centralised data repository but trusted, accessible and appropriately governed information across the enterprise.
Data readiness is not merely an IT prerequisite. It is a strategic capability that determines how effectively AI can scale.
People, Operating Model and the Agentic Turn
Technology and data create potential. People and operating models convert that potential into results.
Capability: AI literacy must extend beyond specialist teams. Boards need to understand AI risks and opportunities, business leaders must identify areas for transformation, and employees need the skills to work effectively with AI-enabled systems.
Operating model: Banks should move from disconnected departmental projects towards enterprise coordination. A central AI office or Centre of Excellence can establish common standards and platforms, while business units retain ownership of use cases and outcomes.
Workforce: The focus should be on redesigning work, reskilling and redeploying capacity rather than viewing AI exclusively as a workforce-reduction exercise. Human judgement remains important in complex, sensitive and consequential decisions.
Agentic AI: Ambition with discipline
The next phase of AI is moving beyond systems that generate insights or recommendations towards agents that can plan, coordinate and execute tasks across enterprise applications.
In banking, this could include coordinating corporate service requests, supporting fraud investigations or managing multi-step operational workflows.
However, the ability to perform a task does not automatically justify granting an AI agent the authority to make decisions.
BCG’s 2026 Applied AI Index highlights the growing ambition around agentic AI, while Gartner’s 2025 forecast warns that more than 40% of agentic AI projects could be cancelled by the end of 2027 because of cost, unclear value or inadequate risk controls.
A sensible progression for banks is:
  • Assist: Retrieve information and support employee decisions.
  • Recommend: Analyse situations and propose actions for approval.
  • Orchestrate: Coordinate tasks across controlled workflows.
  • Execute: Permit bounded autonomous actions with monitoring, auditability and escalation.

The objective should not be maximum autonomy. It should be the right balance between
automation, human judgement and control.

Questions for the Board and the C-Suite
A useful test of any AI strategy is whether leadership can answer the following questions with confidence.
Dimension The question to ask
Vision What will AI change about how we compete over the next three to five years?
Value Which business outcomes will AI influence, and how will we measure them?
Portfolio Which initiatives should we scale, pause or discontinue?
Governance Do we have clear accountability, risk classification and lifecycle controls?
Data Can priority applications access trusted, governed data?
Architecture Are we building reusable capabilities or disconnected technology stacks?
People Are our leaders and employees equipped to work effectively with AI?
Agentic AI Have we defined autonomy, access and human oversight?
Third parties Do we understand our AI vendor dependencies and exit risks?
Customers Can customers access human support and seek redress when interacting with AI?
A bank that can answer these questions has a stronger foundation for enterprise AI. One that cannot may still be investing in AI, but risks doing so without a coherent institutional direction.
Final Take
The objective is not simply to make a bank AI-enabled, but to evolve it into an AI-native institution where intelligence shapes customer experiences, decisions, risk management and financial services.
This requires more than technology investments. Banks must align AI with business priorities, strengthen their data and governance foundations, and equip people to work effectively with intelligent systems.
The question for bank leadership is no longer How much AI have we adopted? but What measurable value is AI creating for our business and customers?
The future of banking will not be determined by AI alone, but by how intelligently banks combine technology, human judgement and institutional capabilities to create lasting value for customers, shareholders and the wider financial system.
Namit Agarwal
Senior VP, Customer Success
Namit Agarwal is a seasoned banking technology and digital transformation leader with over 28 years of experience spanning technology engineering, product strategy, consulting, and global delivery leadership. An alumnus of IIT (BHU), Varanasi, he began his career with Oracle Financial Services Software, where he held progressively senior roles and led a 150+ member organization delivering core banking transformation to over 45 banks across Western Europe.
Since 2012, he has been associated with i-exceed, driving strategic initiatives in digital banking, enterprise transformation, and technology innovation. His expertise spans digital banking platforms, large-scale technology implementations, platform modernization, and customer-centric transformation. He is particularly passionate about leveraging emerging technologies, including AI, to address real-world banking challenges and deliver measurable business outcomes.
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