Barclays recorded a 12% jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above 12 % by 2026. A growing US business and cost reductions underpinned this outcome, with Barclays citing AI as a key driver of those efficiency gains.

Why AI matters for cost discipline

Barclays has said that technology such as AI is part of its plan to cut costs and make its operations more efficient. That includes trimming parts of the legacy technology stack and rethinking where and how work happens. Investment in AI tools complements broader cost savings goals that stretch back multiple years.

For many large companies, labour and legacy systems still make up a large chunk of operating expenses. Using AI to automate repetitive tasks or streamline data processing can reduce that burden. In Barclays’ case, these efficiencies are part of the bank’s rationale for setting higher performance targets, even though margins remain under pressure in parts of its business.

From investment to impact

Investments in AI don’t translate to results overnight. Barclays’ approach combines these tools with structural cost reduction programs, helping the bank manage expenses at a time when revenue growth alone isn’t enough to lift returns to desired levels.

Barclays’ performance targets for 2028 reflect this dual focus. The bank’s leadership has said that its plans include returning more than £15 billion to shareholders between 2026 and 2028, supported by improved efficiency and profit strength.

What this means for legacy firms

Barclays is far from unique in exploring AI for cost savings and efficiency. Other banks have also flagged technology investments as part of broader restructuring efforts. But what makes Barclays’ case noteworthy is the scale of the strategy and the way it is tied to measured performance targets, not just experimentation or small-scale pilots.

In traditional industries, especially ones as regulated as banking, adopting AI is harder than in tech startups. Firms must navigate compliance, risk, customer privacy, and legacy systems that weren’t designed for automation. Yet Barclays’ public comments suggest that the bank is now comfortable enough with these tools to anchor part of its financial forecast on them. That signals a degree of maturity in how the institution operationalises AI.

Barclays isn’t simply building isolated AI projects; leadership is weaving technology into cost discipline, modernisation of systems, and long-term planning. That shift matters because it shows how legacy firms, even those with large, complex operations, can start to move beyond pilots and into business-wide use cases that affect the bottom line.