SKN | Barclays Expands Claude AI Across Software Development and Banking Operations

Key Points

• Barclays expects Claude Code to reach approximately 50% of its developer population by the end of 2026, with adoption expanding to the majority of software engineers during 2027.
• The bank is already using Claude across employee support and Global Markets, including a system processing around 120,000 incoming emails each day.
• Barclays estimates AI could contribute approximately £2 billion in efficiency savings, but realizing that potential will depend on effective implementation, governance and human oversight.

Barclays Moves AI From Experimentation Into Core Workflows

Barclays is expanding its use of Anthropic’s Claude AI across software development, customer support and operational processes as the bank seeks to modernize legacy technology and improve efficiency.

The expansion is significant because it moves generative AI beyond isolated experimentation and into recurring workflows across a major financial institution. Barclays expects Claude Code to be used by approximately half of its developers by the end of 2026, with adoption expected to reach the majority of software engineers during 2027.

The broader technology program could become an important component of the bank’s efficiency agenda. Barclays has indicated that AI could contribute approximately £2 billion in efficiency savings, making successful deployment relevant to both its technology strategy and longer-term operating model.

Software Modernization Becomes a Major AI Opportunity

One of the most important applications is Claude Code, which Barclays is using to support software development and modernization.

Large financial institutions continue to operate substantial legacy technology environments, and updating those systems can require significant technical resources and time. Barclays is applying Claude to routine development tasks, software-quality improvements and work involving older systems.

The potential benefit is not simply faster coding. By reducing repetitive development and maintenance work, AI could allow technical teams to dedicate more capacity to complex modernization and transformation projects.

For investors, the distinction matters. The financial value of generative AI increasingly depends on whether it can alter the underlying cost structure of a business rather than simply improve isolated employee productivity.

AI Is Already Supporting Customer and Market Operations

Barclays’ use of Claude extends beyond software engineering. Its Colleague Knowledge Assistant, introduced in 2025, helps employees retrieve information when supporting customers.

More than 16,000 employees have adopted the tool, which has processed more than one million searches. This provides an example of AI being integrated into everyday employee workflows rather than remaining a specialist technology used by dedicated teams.

Within Global Markets, Barclays is also using Claude to classify, enrich and route incoming emails. The system handles approximately 120,000 messages per day, reducing manual processing and helping employees identify and respond to relevant client requests more efficiently.

For an institution serving corporate and institutional clients, these applications demonstrate where AI can potentially create operational leverage: high-volume information processing, employee assistance and workflow prioritization.

The Efficiency Opportunity Comes With Governance Requirements

Barclays’ AI expansion also highlights a central challenge for regulated financial institutions. Scaling AI across software development, customer servicing and market operations requires more than access to capable models.

The bank must integrate the technology into existing systems while maintaining appropriate governance, security and human oversight. This becomes particularly important when AI is applied to sensitive financial information or processes that can influence client interactions and operational decisions.

Anne Marie Darling, Barclays’ group co-chief operating officer, has emphasized that the objective is not simply adopting AI, but changing how work is performed across the organization.

That distinction is increasingly relevant across the banking sector. The institutions most likely to capture meaningful AI benefits will need to combine technological adoption with disciplined implementation and controls.

Closing Insights

Barclays’ expansion of Claude illustrates how generative AI is moving deeper into the operating infrastructure of major banks. Software development, employee support and Global Markets processing are becoming practical deployment areas rather than experimental use cases.

The potential £2 billion efficiency contribution gives the initiative strategic significance, but the ultimate investment impact will depend on how effectively Barclays converts AI adoption into measurable productivity and cost improvements.

For global wealth investors, the broader development highlights an important competitive shift across banking. AI is becoming part of the operating architecture through which large institutions manage technology, employees and client service. The opportunity is substantial, but so is the requirement for governance, resilience and disciplined execution.

For a confidential discussion regarding retail banking strategy, insurance distribution models, customer loyalty ecosystems, digital financial services, or cross-border financial innovation opportunities, contact our senior advisory team. For investors assessing European banks undergoing digital transformation, AI adoption should be evaluated alongside cost-income performance, technology investment, operational resilience, regulatory controls and the institution’s ability to translate productivity gains into sustainable financial outcomes.