New Delhi, July 21 -- The banking industry is entering a defining phase of transformation. Over the past decade, banks across the world have invested heavily to become digital first, modernising channels, improving customer engagement, and automating processes to keep pace with changing customer expectations. However, the progress has been uneven. While some banks are digitally mature, others are still midway through that journey. Even as digital transformation continues, a new shift is already underway.
Artificial intelligence is pushing banks beyond digitisation toward an AI first operating model. Unlike earlier technology waves, AI has a distinct ability to exert influence across important areas such as compliance, customer experience, and operational efficiency. This transition from digital first to AI first marks a fundamental change in how banks operate and compete, reshaping the expectations CIOs have of their technology partners.
The trust factor
In our conversations across the banking industry, the top priority confronting a CIO is how to trust AI, which is continually evolving. Apart from AI adoption, CIOs are also responsible for ensuring AI can be trusted by boards, customers, business and regulators alike. Unlike previous technologies, AI is not static. It evolves continuously as models learn from data and adapt to new conditions. In the past, banks could afford to wait for technologies to mature before adopting them. However, in an AI-first world, banks do not have the luxury of waiting for AI to stabilise.
CIOs are asked to adapt to AI as it evolves. From this angle, trust in AI adaptation is the single largest concern for technical and business leaders at the bank.
As CIOs work to move their banks toward an AI-first model, the challenge extends well beyond technology. It covers four critical areas: transforming the customer experience, modernising real-time systems, reducing operational costs, and strengthening regulatory compliance, privacy, and resilience. AI adoption spans all of these dimensions.
CIOs are wrestling with questions such as how to reduce onboarding times and make personalisation more relevant, how AI and automation can drive productivity and reduce costs in highly human-intensive operations, and how regulatory and largely manual audit processes can be made more efficient and safer. Each of these areas introduces its own complexity and risk, which is why CIOs are becoming far more deliberate in choosing the partners they trust to support their AI journey.
Ensuring safe AI before and after deployment
With trust, safety and governance come into sharp focus. CIOs want to understand how AI is made safe before deployment, how it is monitored after deployment, how it is maintained to avoid slowness, and how to ensure models do not introduce bias. As banks move AI from experimentation into core business processes, the need for safe AI becomes more pronounced. Trust in AI has transcended model accuracy to cover the entire lifecycle management of how AI is governed, audited, monitored, and corrected once it is live.
AI at the core, not as an add-on
In the recent past, banks have invested significantly in AI initiatives. In many cases, however, the outcomes have not kept pace with the investment. Many organisations applied AI for the sake of experimentation without defined outcomes. Success from the AI initiatives was measured in learning rather than business impact. There is a pressing need to demonstrate measurable outcomes from AI initiatives.
As a result, banks are looking for partners who can embed AI into the core of the operations rather than positioning it as a bells and whistles around a solution. Banks seek partners with deep domain expertise who can help them define business priorities and work backwards to achieve outcomes through people, processes, and technology. Implementing AI at the core of operations requires partners who bring architectural discipline, a pragmatic, outcome-driven approach, the ability to integrate AI across legacy and modern environments, and experience collaborating between business and technology.
New operating model
Another common pattern emerging in banks is the proliferation of AI initiatives across teams and business units. However, uncoordinated bespoke initiatives tend to rapidly increase risk. To address this and pragmatically scale AI in regulated industries, banks are moving towards a hub-and-spoke model. In this approach, the hub-an AI Centre of Excellence (COE) establishes enterprise-wide policies, approved technology platforms, model risk management processes, ethical guidelines, shared data infrastructure, and monitoring tools. It also provides centralised expertise for complex evaluations, compliance, and cross-functional learning.
Meanwhile, the spokes, or business units such as retail banking, wealth management, or operations, drive use case identification, prioritisation, adaptation, development, and deployment, and are accountable for business outcomes. This hybrid structure balances the needs of different business lines while anchoring quality and governance through a central unit.
Conclusion
In an AI-first banking world, technology partners are expected to engineer trust by guiding responsible AI adoption, embedding AI as a core operational capability, and delivering measurable business outcomes while maintaining safety and governance. Competitive advantage will go to partners who clearly demonstrate these attributes and fulfil CIO expectations.
Published by HT Digital Content Services with permission from TechCircle.