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.
At a time when many large companies are still experimenting with AI pilots, Barclays is tying the technology directly to its cost structure and profit outlook. In public statements and investor filings, leadership positions AI as one of the levers that can help the bank sustain lower costs and improved returns, especially as macroeconomic conditions shift.
Barclays’ 12 % profit rise this week matters, not just for its shareholders, but because it reflects a trend that traditional, highly regulated firms are now positioning AI as a core part of running the business, not something kept in separate innovation labs. For companies outside tech, linking AI to measurable results such as profit and efficiency marks a shift toward operational use over hype.
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.
It’s important to be specific about what these efficiencies mean in practice. AI technologies, for example, models that assist with risk analysis, customer service workflows, and internal reporting, can reduce the hours staff spend on manual work. That doesn’t always mean cutting jobs outright, but it can lower the overall cost base, especially in functions that are routine or transaction-driven.
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.
Often, companies talk about technology investment in vague terms. Barclays’ latest figures make the link between tech and profit more concrete: the 12 % profit rise was reported in the same breath as the role of technology in trimming costs. It’s not the only factor; improved market conditions and growth in the US also helped, but it’s clearly part of the narrative that management is presenting to investors.
This emphasis on cost discipline and profit impact sets Barclays apart from firms that treat AI as a long-term bet or a future project. Here, AI is integrated into ongoing cost management and financial planning, giving the bank a plausible pathway to stronger returns in the years ahead.
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.
For other end-user companies evaluating AI investments, Barclays offers a working example: a large, regulated company can use technology to help hit cost and profitability targets, not just to explore new capabilities.
(Photo by Jose Marroquin)
See also: Goldman Sachs tests autonomous AI agents for process-heavy work
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