Barclays Banking on AI: Driving Efficiency and Profitability

Barclays has reported a significant 12% increase in annual profit for the year 2025, with earnings before tax reaching £9.1 billion, up from £8.1 billion the previous year. The bank has also revised its performance targets for the period up to 2028, aiming for a return on tangible equity (RoTE) of over 14%, compared to the previous target of above 12% by 2026. This improvement is attributed to the growth of its US business and cost-cutting measures, with Barclays highlighting artificial intelligence (AI) as a key driver of these efficiency gains.

Unlike many other large companies that are still in the experimental phase with AI, Barclays has directly linked the technology to its cost structure and profit forecast. By positioning AI as a crucial factor in maintaining lower costs and enhancing returns, Barclays is demonstrating a strategic shift towards integrating AI into its core business operations, rather than keeping it confined to separate innovation initiatives.

The 12% rise in Barclays’ profit not only benefits its shareholders but also reflects a broader trend where traditional, heavily regulated firms are embracing AI as an essential component of their business operations, moving away from viewing it as a peripheral technology confined to innovation labs. This shift signifies a move towards practical, operational use of AI, focusing on tangible outcomes such as profitability and efficiency, rather than mere hype.

The Significance of AI for Cost Management

Barclays has emphasized the role of technologies like AI in its cost reduction and operational efficiency strategies. This includes streamlining legacy technology systems and reimagining work processes. The integration of AI tools complements the bank’s broader cost-saving objectives spanning multiple years.

For many large corporations, labor costs and outdated systems constitute a significant portion of their operating expenses. Leveraging AI to automate repetitive tasks and streamline data processing can alleviate this burden. In Barclays’ case, these efficiencies form a crucial part of the bank’s rationale for setting ambitious performance targets, notwithstanding the ongoing challenges in certain business segments.

It is essential to outline the practical implications of these efficiencies. AI technologies, such as risk analysis models, customer service workflows, and internal reporting tools, can reduce the time employees spend on manual tasks. While this may not necessarily lead to job cuts, it can contribute to lowering overall costs, particularly in routine or transactional functions.

Transitioning from Investment to Results

The outcomes of AI investments are not immediate. Barclays’ strategy involves combining these tools with structural cost-cutting programs to manage expenses effectively, especially when revenue growth alone is insufficient to achieve desired returns.

Barclays’ performance targets for 2028 reflect this dual focus. The bank aims to return over £15 billion to shareholders between 2026 and 2028, supported by enhanced efficiency and profitability.

While many companies discuss technology investments in vague terms, Barclays’ latest results establish a clear link between technology adoption and improved profitability: the 12% profit increase was directly associated with technology’s role in trimming costs. Although improved market conditions and US growth also contributed, technology integration is a key narrative that management is presenting to investors.

This emphasis on cost discipline and profitability sets Barclays apart from organizations that view AI as a distant investment or a futuristic endeavor. Here, AI is seamlessly integrated into ongoing cost management and financial planning, offering a viable pathway to stronger returns in the foreseeable future.

Implications for Established Companies

Barclays’ exploration of AI for cost savings and operational efficiency is not unique, with other banks also highlighting technology investments as part of broader restructuring initiatives. What distinguishes Barclays is the scale of its strategy and its alignment with measurable performance targets, rather than mere experimentation or small-scale pilots.

In traditional industries, particularly highly regulated sectors like banking, adopting AI poses unique challenges. Companies must navigate compliance, risk management, customer data protection, and the integration of AI with legacy systems not originally designed for automation. Barclays’ public statements indicate a level of confidence in these tools, anchoring a portion of its financial projections on AI technology. This reflects a mature approach to operationalizing AI within the institution.

Barclays is not merely developing isolated AI projects; its leadership is integrating technology into cost management, system modernization, and long-term strategic planning. This shift is significant as it demonstrates how legacy enterprises, even those with complex operations, can progress from experimental phases to widespread business applications that directly impact financial performance.

For companies considering AI investments, Barclays serves as a practical example: a large, regulated entity can leverage technology to achieve cost efficiency and profitability targets, moving beyond exploratory phases to realize tangible benefits.

(Photo by Jose Marroquin)

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