CBQ embeds AI in operations

Commercial Bank of Qatar shows how AI is becoming part of banking infrastructure, with production use cases across service, operations, compliance and customer engagement

Artificial intelligence (AI) in banking is now being judged less by experimentation and more by execution and by the outcomes it delivers for customers, including faster service, more seamless journeys and more relevant interactions. Commercial Bank of Qatar (CBQ) has achieved this by moving AI beyond isolated pilots and into production use across customer service, document processing, translation, compliance and personalisation.

The bank’s AI transformation was anchored in its Data & AI Strategy launched in 2022, supported by a dedicated Data & AI Lab. This gave CBQ the ability to develop proprietary AI capabilities within its own secure environment and under controls designed for a regulated banking market.

The bank reported more than 20 AI and generative AI solutions in production. The reported results included 50-80% improvements in operational efficiency and more than 50,000 man-hours saved in 2024.

For employees, the impact is not simply a reduction in processing time. By automating high-volume, repetitive workflows, CBQ can redirect staff capacity towards complex cases, problem-solving and higher-value customer and business priorities, while enabling faster delivery.

AI in practice

The use cases show how the bank applied AI to specific operational pressures. Smart Email AI, a proprietary large language model (LLM) system using retrieval-augmented generation, classifies and drafts responses for more than half of customer emails, reducing response turnaround time by 50%. Its AI-enabled intelligent character recognition capability processes more than 140,000 documents a quarter, with AI-based decisions accounting for up to 90% of document-processing workflows.

The gains were also visible in internal knowledge and control functions. The bank reported that its AI translation engine reduced Arabic-to-English translation time from 36 hours to 15 minutes. Its LLM-powered compliance knowledge base enables natural-language navigation through more than 700 regulatory circulars, reducing compliance checks from 24 hours to about five minutes.

What made CBQ’s approach stand out was not only the number of tools deployed but also the operating model behind them. The bank linked AI deployment to a broader enterprise data platform, including a data lakehouse, real-time streaming capabilities and a data governance platform. This allowed AI to support both batch analytics and real-time decisioning, rather than sit as a separate technology layer.

Practical value

That distinction matters in this category. AI use in financial services must show practical value while meeting high expectations for security, resilience, data protection and oversight. CBQ’s model kept infrastructure and data within the bank’s own environment, while its governance framework was reported to align with Qatar Central Bank AI guidelines, the three lines of defence model and board-level oversight.

The bank is now extending the same AI base into customer engagement. Its machine-learning models support micro-segmentation, next-best-action recommendations and real-time event-based campaigns, with a target of increasing cross-sell volumes by 15%. The case is therefore not only about automation. It shows AI becoming part of the bank’s operating infrastructure.