Why do most banks struggle to adopt and scale AI successfully?

Many banks struggle to scale AI because legacy systems block digital progress. These outdated systems can consume up to 80% of tech budgets, limiting access to the high-quality data AI needs. As a result, as many as three-quarters of GenAI pilots fail to move beyond proof-of-concept. Other major challenges, such as a shortage of AI-ready talent and the inability to demonstrate tangible ROI to secure continued investment, also slow adoption.

How can finance leaders achieve measurable ROI from AI in banking and corporate finance?

To achieve measurable ROI, leaders should align GenAI initiatives with clear strategic goals like cost reduction, revenue growth or risk management. The most effective approach is incremental modernization, which delivers value early and continuously to sustain funding. Measuring ROI requires tracking metrics such as revenue per customer, cost-to-serve, or modernization speed, turning AI from a pilot expense into a proven growth driver.

What AI governance framework helps banks balance innovation with compliance and risk management?

A strong AI governance framework in banking should focus on three key pillars: Data quality: Establish rigorous cleansing, normalization and lineage tracking to ensure data accuracy and reliability. Model development: Define use cases and train models on reliable data, ensuring the ability to detect and mitigate bias. Cybersecurity: Protect AI systems and customer data through encryption, access controls, and continuous threat detection. Together, these elements help banks innovate confidently while staying compliant and resilient.

How can explainable AI help banks and financial institutions ensure transparency, fairness and compliance?

Lack of transparency in AI models is one of the biggest barriers to accountability. Explainable AI helps banks trace decisions back to the data and logic that shaped them, making outcomes easier to understand and justify. This transparency enables early detection of bias, supports fairer decisions in areas like lending and credit scoring, and strengthens regulatory compliance and stakeholder trust.

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The GenAI guide to financial industry excellence

Learn how to scale AI in banking and gain the market edge

75% of GenAI pilots in banking never scale.

Most financial institutions are experimenting with AI, but few are seeing measurable impact.

The problem isn’t ambition. It’s architecture.

Legacy systems, siloed data and fragmented governance keep AI stuck at the pilot stage, while locking up nearly 80% of IT budgets just to maintain the past.

This guide shows how to change that.

Learn how leading banks are modernizing their platforms, unlocking data and building responsible governance frameworks to scale AI safely, strategically and at speed.

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The ROI of doing modernization differently

One global bank unlocked $800 million in new revenue in just two years by modernizing incrementally, achieving zero downtime and a 30% reduction in costs.

How GenAI helps demystify legacy

Using GenAI-driven analysis, one financial firm achieved 80% accuracy in understanding complex legacy code, modernizing 4.5 million lines in just 14 weeks.

Scaling GenAI without scaling costs

Cloud hyperscalers are removing the entry barriers to GenAI, giving banks on-demand computing power without the massive upfront investment.

GenAI’s ripple effect across the enterprise

From 30% faster delivery to greater digital resilience, banks using GenAI tools are gaining new speed, sharper insights and a stronger competitive edge.

Why this guide matters

AI has moved from exploration to expectation.

For financial leaders, the question is no longer if AI can drive value, but how fast it can scale safely across the enterprise.

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Meet the authors

Sannesh Prabhu

Global Head of Pre-Sales Engineering and Solutions at Thoughtworks

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Sandeep Reddy

Global Head of Financial Services Strategic Initiatives and Delivery at Thoughtworks

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Wayne Te Paa

Managing Director of Banking, Financial Services and Insurance Services (APAC) at Thoughtworks

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Omar Bashir

Technical Director of Banking and Financial Services (APAC) at Thoughtworks

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FAQ

Many banks struggle to scale AI because legacy systems block digital progress. These outdated systems can consume up to 80% of tech budgets, limiting access to the high-quality data AI needs. As a result, as many as three-quarters of GenAI pilots fail to move beyond proof-of-concept.

Other major challenges, such as a shortage of AI-ready talent and the inability to demonstrate tangible ROI to secure continued investment, also slow adoption.
To achieve measurable ROI, leaders should align GenAI initiatives with clear strategic goals like cost reduction, revenue growth or risk management. The most effective approach is incremental modernization, which delivers value early and continuously to sustain funding.

Measuring ROI requires tracking metrics such as revenue per customer, cost-to-serve, or modernization speed, turning AI from a pilot expense into a proven growth driver.
A strong AI governance framework in banking should focus on three key pillars:
- Data quality: Establish rigorous cleansing, normalization and lineage tracking to ensure data accuracy and reliability.
- Model development: Define use cases and train models on reliable data, ensuring the ability to detect and mitigate bias.
- Cybersecurity: Protect AI systems and customer data through encryption, access controls, and continuous threat detection.
Together, these elements help banks innovate confidently while staying compliant and resilient.
Lack of transparency in AI models is one of the biggest barriers to accountability. Explainable AI helps banks trace decisions back to the data and logic that shaped them, making outcomes easier to understand and justify.

This transparency enables early detection of bias, supports fairer decisions in areas like lending and credit scoring, and strengthens regulatory compliance and stakeholder trust.