TL;DR

Barclays reported a 12% jump in annual pre-tax profit to £9.1 billion for 2025 and raised its 2028 return on tangible equity target above 14%. The bank has positioned AI as a core driver of cost savings and efficiency gains, tying the technology directly to its financial outlook.

AI as operational infrastructure

Unlike companies still running AI pilots in separate innovation labs, Barclays is weaving AI into its core cost structure. The bank is using AI tools for risk analysis, customer service workflows, and internal reporting to reduce the hours staff spend on manual work.

This doesn’t necessarily mean cutting jobs outright, but it lowers the overall cost base — particularly in routine, transaction-driven functions. The bank’s leadership presented the 12% profit rise alongside the role of technology in trimming costs, making the connection between AI investment and financial performance more concrete than most firms have managed.

Barclays plans to return more than £15 billion to shareholders between 2026 and 2028, supported by improved efficiency and profit strength.

A maturing approach

What makes Barclays’ case notable is the scale. The bank is not running small-scale experiments — it is anchoring part of its financial forecast on AI-driven efficiency. In a sector as heavily regulated as banking, where compliance, data privacy, and legacy systems create significant barriers to automation, this represents a degree of operational maturity.

The bank is also trimming parts of its legacy technology stack and rethinking where work happens, with AI investment complementing broader cost savings goals that stretch back multiple years.

Looking forward

For other large organisations evaluating AI investments, Barclays offers a working example: a regulated company using technology to hit specific cost and profitability targets rather than exploring capabilities in isolation. Whether the promised efficiencies deliver consistent results over the next three years will be closely watched by the sector.