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Cracking the Code: AI and Data Transformation in Banking

How To Get the Most Value out of AI and Data in Banks

Artificial intelligence (“AI”) and data are reshaping the financial industry, yet many banks struggle to turn their potential into game-changing impact. The gap between innovation and execution is massive, held back by data hurdles, tech immaturity, talent shortages and siloed operations that stifle efficiency. But for those who crack the code, the rewards are transformative.

By embedding AI into critical banking functions like fraud detection and loan management, institutions can supercharge efficiency, hyper-personalize customer experiences and unlock new revenue streams. But true success isn’t just about adopting cutting-edge tech; it requires strong data governance, strategic execution and an AI-driven culture. A structured, value-first approach ensures AI investments align with business goals, regulatory requirements and market shifts, while avoiding costly tech overspending.

Banks that master AI won’t just keep up, they’ll lead, gaining a sharp competitive edge in an increasingly digital and regulated world. The time to act is now.

Figure 1: Illustrative Value at Stake for Successful AI and Data Programs

A Common Situation

Globally, financial institutions are facing unprecedented challenges, including increasing fraud and default rates, shrinking profit margins and tightening regulatory pressures. Rapid digitalization has also heightened customer expectations, pushing banks to offer seamless, personalized experiences while maintaining compliance with evolving regulations. At the same time, the competitive landscape is being reshaped by agile fintech startups leveraging cutting-edge technology to capture market share. In response, banks are turning to AI and data-driven solutions to enhance risk management, streamline operations and unlock new revenue streams. These organizations face several common challenges that hinder progress:

Limited and Slow Value Extraction: Many banks struggle to generate meaningful impact from their AI initiatives in a timely manner, leaving significant potential untapped.
Blurred Accountability: AI spans the entire banking value chain, often leading to unclear ownership in shared services for example, where overlapping front- and back-office functions can result in inefficiencies and duplication.
Data Quality Challenges: Fragmented, outdated or inconsistent data weakens AI-driven insights and hinders effective implementation.
Legacy Systems and Technical Debt: Outdated infrastructure and entrenched legacy technology slow down AI adoption and digital transformation.
Talent Gaps: A shortage of AI and data specialists creates execution roadblocks, limiting banks’ ability to scale AI-driven innovation.

Unlocking AI’s full potential in banking requires a structured, scalable approach that aligns executive vision with operational execution. A successful strategy combines a top-down perspective, ensuring leadership aspirations and long-term market ambitions are clearly defined, with a bottom-up approach that addresses functional and operational realities. By embedding AI-driven insights and strong data governance into core processes, banks can navigate challenges like fragmented data, legacy systems, and talent gaps. However, success hinges on a proven business case and tangible action plan that reflects the organization’s readiness — ensuring compliance, enhancing efficiency, and driving sustainable growth in an increasingly digital landscape.

Unlocking AI’s Full Potential

To future-proof banking capabilities, you need a bold yet pragmatic vision. AI success starts at the top and you need to define a strong, business-aligned vision that strikes the right balance between innovation and tangible impact. With AI-specific strategic frameworks designed based on marketing insight, this ensures that this vision is both ambitious and executable — setting the foundation for long-term success. But how do you do that in practice?

Pinpoint the Highest-Value Opportunities: Not all AI use cases are created equal. Instead focus on identifying the most impactful and feasible AI opportunities — whether in automated cross-selling, fraud detection, risk management or hyper-personalized customer experiences — to ensure maximum business value.
Build a Business Case That Delivers: AI must deliver measurable impact. Prioritize and execute AI initiatives that strike the right balance between quick wins and long-term transformation, ensuring every investment is directly tied to business value.
Create Scalable AI Foundations: Strong governance, modern infrastructure and skilled talent are non-negotiable for AI success. Building scalable frameworks and selecting the right technologies and infrastructure that enable sustainable growth in the long run while keeping costs optimized is vital. Equally important is the operating model — a critical foundation for seamless AI integration and execution.
Turn Strategy into Action: A clear roadmap transforms AI ambition into tangible results with, actionable implementation plans that are fully aligned with business objectives and supported by comprehensive change management strategies that drive data literacy, cultural adoption, and sustainable, long-term success. This structured approach ensures that strategy is not just a vision, but a reality — driving focused execution and delivering impactful results at every step.

Key Differentiators

To future-proof banking capabilities, you need a bold yet pragmatic vision. What these successful visions have in common are multiple key principles that drive AI and data transformation success, including:

Executive Sponsorship and Governance: A strong business sponsor, supported by a cross-functional steering committee, is essential for swift decision-making and sustained momentum.
Strategic Leverage of Existing Assets: Building on prior efforts enhances efficiency, accelerates delivery and fosters stronger stakeholder engagement.
Prioritizing Foundational Capabilities: A robust, centralized data infrastructure is non-negotiable—organizations must resist the temptation to prioritize high-visibility AI use cases at the expense of core enablers.
Proactive Alignment with Technology Leadership: Clear agreements on trade-offs and dependencies in the Tech and Data roadmap are critical to ensuring seamless execution.
ROI-driven Investment Narrative: A compelling, well-defined value case is crucial to securing the necessary funding and executive buy-in.
Dedicated Transformation and Change Leadership: A well-resourced change management function is a key enabler for successful implementation, ensuring adoption and long-term impact.

By integrating these learnings into their transformation journeys, financial institutions can overcome barriers, unlock the potential of AI and data, and position themselves for sustained growth and innovation.