IBM’s first-quarter earnings underscore the growing demand from financial institutions for AI-driven infrastructure and consulting, as banks look to embed intelligence directly into core systems. 

Banks including RBC and NatWest are deploying IBM’s AI platform watsonx and automation tools to modernize mainframe environments, improve resilience and boost developer productivity, Chief Executive Arvind Krishna said during the company’s first-quarter earnings call on April 22. 

Those moves highlight a shift among banks to integrate AI into mission-critical transaction systems rather than layering it on top, he said.  

IBM reported its Z mainframe platform or its modern core banking platform continues to serve as a backbone for financial services, processing billions of transactions with near perfect uptime while increasingly supporting AI inferencing in real time.  

“Clients rely on our Z platform to process billions of transactions reliably,” Krishna said, noting that AI can run “directly in line with those transactions.”  

FIs are using the Z platform for real-time fraud detection, in some cases saving “tens of millions of dollars,” he said.  

IBM’s infrastructure revenue grew by $3.3 billion, up 12% year over year, while its software revenue grew to $7.1 billion, up 8% YoY, reflecting rising demand for AI-ready systems, Krishna said.  

AI consulting 

AI consulting remains a growth driver, particularly in financial services, which has identified IBM as one of its largest customer segments.  

The company reported $5.3 billion in consulting revenue, up 1% YoY in Q1, with gen AI consulting representing nearly 30% of total consulting revenue, according to the company’s earnings report. 

Krishna emphasized that enterprises are moving beyond AI experimentation.  

“They are scaling AI and … making deliberate choices about where workloads should run,” he said.  

IBM’s consulting strategy focuses on embedding AI into workflows across operations, helping banks transition from pilot projects to production environments, Krishna said. 

Internal application  

The tech giant is also using AI internally to drive efficiency.  

Its AI-powered development system, IBM Project Bob, delivers productivity gains of 45% across its engineering workforce, he said.  

IBM’s Project Bob, launched this year, is a multimodel AI platform used as coding tool, Shanker Ramamurthy, global managing partner for banking and financial markets at IBM Consulting, previously told FinAi News. 

“IBM Bob was built to give enterprises flexibility and choice, rather than locking them into a single model,” Ramamurthy said. “Different LLMs have different strengths, and no single model is optimal for every task across the software development lifecycle.” 

The platform lets enterprise and Z users choose the best model for each workflow, balancing performance, cost and governance, rather than being limited to one approach, he said. “Today it supports multiple models, including IBM Granite, Anthropic’s Claude, Mistral AI and Meta’s Llama, all within an enterprise-grade framework.” 

IBM sees financial institutions continuing to invest in hybrid cloud and AI platforms that allow them to control data, orchestrate multiple models and maintain governance at scale — priorities that are increasingly shaping technology decisions across the sector, Krishna said. 

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