For the banking and financial services sector, artificial intelligence (AI) is no longer an interesting new trend to be experimented with. It is a strategic capability that has the power to reshape how they manage risks, explore new revenue streams, expand the scope of the business, and serve customers. Banks that treat AI as a tactical add-on risk falling behind. Instead, the institutions that will thrive are those that embed AI at the heart of their business model to transform data into actionable insight and insight into revenue.

The Shift from Productivity Gains to Full-Scale AI-Enabled Business Models

70 percent¹ of financial services executives believe that AI will directly tie to revenue growth in upcoming years but 44 percent are deploying solutions based on individual productivity, only 27 percent² are leveraging it to drive business-focused innovation, and only 29 percent are using it to reshape critical functions by improving process level productivity. Banks must move beyond pilot projects and approach AI as a core enabler of their business model. The real value for them lies in embedding AI across critical processes ranging from payments and customer engagement to onboarding and compliance.

A Structured Path to Scalable AI

Unlocking this potential hinges on three pillars: a robust data foundation, operational automation, and strategic monetization.

• Data Foundation: High quality and comprehensive data lie at the heart of AI implementations. Banks need to establish a clear data strategy that establishes how data will be sourced, stored, secured, analyzed, and governed. This must be followed by implementing roust machine learning models that can detect patterns and generate predictive or prescriptive insights for decision leaders to act on.

• Automation: Once the data and ML models are established, the next step is to use them to drive operational efficiency. AI can automate routine tasks like onboarding, document verification, or even basic customer service interactions, freeing up teams to focus on higher-value work. Compliance teams will benefit from automated KYC processes that ensure error-free documentation and continuous monitoring of the regulatory landscape and rapid adaptation to regulatory changes. Relationship managers can use AI-powered insights to craft better personalized offers, map offers against market trends and even forecast market trends for proactive strategies. AI-powered automation and real-time pattern detection capabilities can help enhance risk management, fraud detection, and anomaly detection efforts to improve organizational security posture.

• Revenue Leverage: The final stage is converting insight and operational lift into tangible business value. This means rolling out AI-powered personalized pricing strategies, contextual cross-selling or upselling, or even exploring new business models like embedded finance, or subscription-based services. Banks that adopt AI not just to cut costs but to drive revenue gain sustainable competitive advantage

The Emergence of Agentic AI-Powered Banking Models

The next phase of AI evolution is agentic AI or autonomous agents who don’t need human intervention to carry out tasks. Agentic AI can use advanced reasoning to understand nuanced goals and contexts of a situation or problem and then provide detailed problem-solving support. For banks, agentic AI holds the promise of revolutionizing key processes ranging from deal lifecycle management and pricing strategies to customer engagement and advisory services. But to properly leverage agentic AI, organizations need to establish a strong foundation with a strong data pipeline, robust implementation roadmap, and above all, a comprehensive strategy for integrating AI across processes and departments.

Partner, Modernize, and Build for AI Scalability

Legacy banking systems can hold banks back from fully leveraging the power of AI. And it may be a good idea to explore partnerships with fintechs and technology providers to help accelerate deployment, share risk, and handle regulatory or cyber complexities more efficiently. Alternatively, they may also choose to work with specialized partners to deploy robust, cloud-native revenue management systems that can sit over the legacy core and drive AI integration and innovation.

People, Skills, and Governance Will Make or Break AI Ambitions

But technology is only part of the story. Successful AI transformation relies on strong executive sponsorship, clear governance, and a culture of data-driven decision-making. At the same time, banks must be cognizant of the fact that there exists a significant AI skills gap at the moment. Over 90 percent³ of enterprises across the world are expected to face skills shortages by 2026. And sustained skill gaps can result in losses to the tune of $5.5 trillion in losses. 94 percent of CEOs and CHROs say that AI is their top in-demand skill for 2025, but only 35 percent of leaders are confident that they have prepared their teams for AI roles. In fact, only a third of employees report receiving any AI training in the past year.

Now is the time for banks to invest in building AI literacy across the workforce and encourage experimentation. Strong governance guardrails to prevent misuse and ensure regulatory compliance must also be a top priority at this juncture.

As AI becomes inseparable from modern banking strategy, the institutions that will succeed are those that treat it as a core enterprise capability rather than an experimental add-on. With agentic AI on the horizon, the opportunity to reinvent banking has never been greater. By combining robust strategy with workforce upskilling and responsible governance, financial institutions can turn AI into a sustained source of growth, resilience, and competitive advantage.