AI in banking: Insights on turning AI into ROI

It’s important to note that AI in banking is no longer a future consideration. Rather, it’s fast becoming an operational imperative, powering automation, unlocking insights, and reshaping traditional workflows. 2025 CGI Voice of Our Clients research confirms this. Nearly 60% of the executives interviewed in the asset/wealth management and capital markets sectors have a holistic AI strategy, while nearly 40% of retail banking and corporate banking executives say the same. 

However, the path to value is not always linear. Organizations across banking, lending, and fintech are learning that true ROI comes from solving practical business problems, not just experimenting with the latest technology.

So, where are some of the wins of AI in banking happening? Further, how are forward-looking banks accelerating adoption while navigating complexity, risk, and legacy systems?

Use AI to address business-critical pain points

One thing is clear; the most successful AI use cases begin with real business challenges, not technology-first thinking. One mortgage lender, for example, is using AI to address a longstanding inefficiency—navigating more than 1,000 pages of underwriting guidelines. By reengineering that content into a custom Q&A knowledge base and integrating it with an AI assistant, the lender achieved the following outcomes:

• 98% user adoption
• 67% reduction in emails to underwriters
• Near 100% accuracy, with ongoing human validation and oversight

Embed AI into everyday workflows

Embedding AI into core workflows is no longer optional; it’s becoming expected. Professionals want tools that fit into the applications they already use, whether an ERP platform, a CRM system, or a spreadsheet tool.

When AI is embedded directly into the tools professionals use every day, such as spreadsheets or enterprise systems, adoption accelerates, especially when it saves time on frequent tasks. Training and behavior change are still required, but professionals across the organization are eager to explore how generative AI can help. The key is providing access, along with governance, and encouraging teams to share what works.

Implementing AI with security and governance in mind

As models become more powerful, banks must remain vigilant about privacy, accuracy, and ethical use. Responsible implementation of AI includes the following:

• Treating AI as a co-pilot to support, not replace, expert decision-making
• Setting up feedback loops (e.g., thumbs up/down features) to monitor quality
• Applying the same life cycle management and access controls used for any other business application

Especially in highly regulated sectors such as lending, the use of sensitive data, like social security numbers or income documents, requires rigorous safeguards. Leaders are right to move deliberately, focusing on business alignment and responsible innovation.

Best practices for implementing AI in banking

Banks leading the way in AI usage are following a clear set of best practices:

1. Prioritize ROI but make room for learning: Target quick wins with measurable outcomes . Then, reinvest learnings into longer-term initiatives with transformative potential.
2. Think across three levels: Consider best practices for organizational strategy, user experience, and technical development. Governance and monitoring must exist at all levels.
3. Strengthen your data foundations: Good data hygiene is critical. Whether using retrieval-augmented generation (RAG), embeddings, or agent-based models, access control, data quality, and versioning are essential.
4. Monitor and refine continuously: AI implementation in banking isn’t a one-time launch. Performance must be monitored over time, including tracking accuracy, latency, model drift, and user feedback.

AI doesn’t just improve workflows; it changes them

AI is prompting many banks to rethink their processes, product delivery, and even how their teams are organized. From underwriting to sales enablement to compliance, intelligent automation is improving outcomes across the value chain.

What sets successful adopters of AI in banking apart? A focus on real business value, an investment in governance and a willingness to empower teams to learn and experiment, responsibly.

Right now, I’m working on a new AI in banking blog series that will cover in depth specific AI use cases, including onboarding, personalization and resiliency. Stay tuned for the first blog in that series.

Also, feel free to reach out to me for further discussion. You also can learn more about our AI and banking work on cgi.com.