Over the past decade, banks worldwide invested heavily to become digital-first. The focus was on digitising customer journeys, modernising channels, improving operational efficiency, and enabling faster access to financial services. That shift fundamentally changed how banks interacted with customers. But the next transition is significantly more complex.
Banks are now moving from being digitally enabled to becoming AI-first. Unlike previous technology waves, artificial intelligence is not limited to improving a single process or channel. It can simultaneously influence customer experience, operations, compliance, software delivery and business decision-making. As a result, the role of the CIO is evolving—from managing technology transformation to driving enterprise-wide business outcomes powered by AI. This raises a critical question: what does it take for a CIO to transition a bank from digital-first to AI-first?
The challenge is that becoming AI-first is not simply about deploying models or launching isolated use cases. It requires banks to rethink infrastructure applications, operating models and governance frameworks. In this context, the CIO’s mandate is expanding significantly. To credibly position as AI-first, banks must demonstrate progress across four core priorities: customer engagement and growth, infrastructure and application modernisation, operations transformation, and regulatory compliance and resilience. These priorities are interconnected and require an integrated approach rather than isolated initiatives.
Senior Vice President and Chief Technology Officer
Maveric Systems
Regulatory compliance and audit
Banking is one of the most tightly regulated industries in the world. Despite advances in digital banking, many compliance and audit functions continue to depend heavily on manual processes, fragmented reviews and retrospective checks. As AI adoption grows, CIOs are under pressure to make these environments more intelligent, proactive and scalable.
AI can significantly reduce manual effort in areas such as transaction monitoring, audit preparation, regulatory reporting and exception handling. More importantly, it can help institutions move from reactive compliance towards continuous oversight. Instead of reviewing issues after they occur, banks can identify anomalies earlier, improve traceability and create more reliable audit trails.
This is becoming important as regulators themselves begin paying closer attention to AI governance. Deloitte’s 2026 Financial Services Regulatory Outlook notes that 57% of financial institutions now rank AI governance and operational resilience among their top regulatory priorities.
For CIOs, this means AI adoption cannot happen without strong governance, explainability and accountability frameworks. Technology partners are therefore expected to provide tools and help banks establish safe deployment practices, monitoring frameworks and long-term governance structures.
Operations transformation
Operations is one of the largest cost centres inside a bank, in many cases exceeding technology costs by 1.5 to 2 times. Yet compared to front-end digital transformation, operational transformation has historically received less attention.
Large banking operations depend heavily on human-intensive processes across onboarding, servicing, investigations, reconciliations and compliance reviews. These areas present a significant opportunity for AI, machine learning and automation to improve productivity while still preserving the importance of domain expertise. While the objective is not to remove human involvement, the real value comes from improving decision support, reducing repetitive effort and enabling teams to handle larger volumes with greater accuracy and consistency.
This is where CIOs increasingly expect technology partners to bring more than implementation capability. They want partners who understand operational workflows, can identify measurable productivity opportunities and can integrate AI into existing environments without disrupting business continuity.
Infrastructure and application modernisation
The transition to AI-first banking also depends heavily on whether existing systems are ready to support AI at scale. Many banking applications were designed for rule-based processing. They were built to execute predefined logic consistently. AI-first systems operate differently. They need applications that are context-aware, adaptive and capable of interpreting data dynamically rather than simply following static workflows. This requires significant infrastructure and application modernisation.
Banks are investing in real-time architectures, cloud-native environments, API-driven ecosystems and modern data platforms that support AI-led decision-making. Modernisation is about creating environments where AI can operate reliably across interconnected systems. As a result, expectations from technology partner’s change. CIOs look for partners who can combine engineering discipline with AI capability, while helping institutions modernise incrementally rather than through large-scale disruption.
The focus shifts towards measurable outcomes. Faster release cycles, reduced defect leakage, improved system reliability and stronger integration across environments are becoming important indicators of AI readiness.
Customer engagement and growth
Customer expectations are evolving rapidly in an AI-first environment. Banks are expected to deliver faster onboarding, more relevant recommendations, personalised engagement and quicker product innovation cycles. This requires a deeper understanding of customer behaviour and the ability to act on that intelligence in near real time.
AI is helping banks improve segmentation, strengthen cross-sell and upsell strategies, identify customer intent earlier and create more personalised engagement models. At the same time, banks are under pressure to reduce the cycle time involved in launching new products and services. This creates a direct dependency between customer experience and technology delivery. Faster innovation requires faster software development, testing and deployment cycles. AI’s impact is more pronounced across engineering and quality assurance functions, supporting those experiences.
The evolving role of technology partners
As banks move from experimentation to execution, the role of technology partners is also evolving. CIOs are looking for partners who can scale AI responsibly, bring deep domain understanding, and deliver accelerators and reusable platforms that shorten adoption cycles and create measurable impact. Partners who can help establish governance, define outcomes clearly, and integrate AI into existing business and technology environments safely will gain a competitive edge.
This becomes even more critical as AI models, platforms, and vendors continue to evolve rapidly, with tools potentially being replaced every few months. In such an environment, organisations need clarity on business outcomes, governance, and execution discipline. Without these foundations, AI initiatives risk remaining stuck in experimentation.
Against this backdrop, the transition from digital-first to AI-first banking is emerging as a broader operational and business transformation agenda. CIOs are expected to drive customer growth, operational efficiency, resilience, and innovation simultaneously. As a result, the role of technology partners is no longer limited to implementing systems; they are increasingly expected to help banks apply AI in ways that are measurable, reliable, and aligned with long-term business priorities.
–Authored by Kishan Sundar, Senior Vice President and Chief Technology Officer, Maveric Systems