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AI in Banking: From Hype to Realization – Practical Steps for CIOs and CDOs

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Banking Transformation in the Digital Era Enters a New Chapter: The Era of Artificial Intelligence (AI)

After two decades of focusing on channel digitalization and process efficiency, banks around the world are now realizing that AI, especially Generative AI, is no longer just an experimental tool, but a true growth engine with significant ROI.

However, in the context of Indonesia, adopting AI in banking requires more than just purchasing a model or setting up an AI Lab. Technology and data readiness are the absolute foundations for AI to truly deliver business value.

 

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Why AI Now?

A recent McKinsey study highlights the growing pressures faced by global banks: declining interest margins, rising operational costs, and a sharp increase in fraud cases. Amid these challenges, the banks that manage to sustain profitability are those that leverage AI for:

  • Optimizing deposit and loan pricing through predictive customer behavior models

  • Real-time fraud prevention using anomaly-based AI

  • Personalized services and product offerings powered by customer data

  • Operational efficiency in back-office functions through Generative AI automation

In Indonesia, the potential is even greater. The combination of high mobile banking adoption and low penetration of complex financial products creates significant room for AI-driven innovation to enhance cross-selling and customer loyalty.

 

The Challenges Ahead: Technology and Data Readiness

Based on KED Consulting’s experience supporting digital transformation, two main bottlenecks consistently hinder impactful AI adoption:

  1. Data Architecture and Engineering Pipeline Readiness
    Many banks already have data lakes, yet lack a data architecture that enables real-time integration and cross-system accessibility. Without a clean, standardized, and secure data pipeline, AI models quickly lose relevance and accuracy.

  2. Technology Infrastructure and AI Governance Readiness
    To effectively deploy Generative AI in areas such as customer service, risk, and compliance, banks need a strong governance framework, covering model management, data privacy, and AI hallucination mitigation, all established from the outset.

 

Three Practical Steps for CIOs and CDOs in the First 90 Days of AI Transformation

We recommend three pragmatic actions for CIOs and CDOs to kickstart their AI journey with a strong foundation and measurable ROI.

1. Audit Data and Infrastructure Readiness

  • Map key data sources (core banking, CRM, digital channels, fraud systems).

  • Identify gaps in data quality, latency, and integration.

  • Define the target architecture, whether a data mesh or modern data platform, capable of supporting real-time AI use cases.

  • Develop an AI data blueprint specifying metadata standards, lineage, and access controls.

Goal: Ensure data is AI-ready, not just available, but structured and trustworthy.

2. Prioritize 2–3 High-ROI Use Cases

Avoid launching large-scale AI programs prematurely. Focus on a few quick wins with direct bottom-line impact:

  • Fraud Detection AI to reduce transaction losses and speed up investigations.

  • Dynamic Deposit & Loan Pricing using predictive customer behavior models.

  • Customer Service Copilot (GenAI) to improve interaction efficiency and enhance CX.

We believe practical, high-impact initiatives like these can generate significant ROI in a relatively short period, provided they are executed with strong data discipline and governance.

3. Establish a Cross-Functional AI Taskforce

AI is not just a technology project. CIOs and CDOs must form an AI taskforce that includes:

  • Business Units (Retail, Lending, Risk, Fraud) – to define needs and success indicators.

  • Data & IT Teams – to prepare platforms and pipelines.

  • Legal & Compliance – to ensure proper data governance and model risk management.

  • Change & Talent Teams – to train employees in using new AI tools.

Within the first three months, this taskforce should produce a clear AI roadmap with timelines, ownership, and realistic financial targets.

 

Bridging the Gap Between Strategy and Execution

Most AI strategies fail not because of a lack of vision, but due to execution unpreparedness. This is where the roles of CIOs and CDOs become critical. They are not just technology enablers, but architects of data transformation and guardians of responsible AI governance.

AI is not merely an innovation tool, it is a lever to restore profitability and efficiency amid margin pressures. For banks, it’s time to move from AI experimentation to AI execution with impact.