Artificial intelligence may be changing how software is built at banks, but at JPMorgan Chase the message from technology leadership is clear: engineering discipline, automated testing and digital resilience are becoming more important, not less.
According to Chase CIO Gill Haus, generative AI and coding assistants are fundamentally altering software testing and development by automating much of the coding process, allowing engineers to focus more on solving business problems and less on writing code itself.
“We don’t really hire engineers to write code, we hire them to know what code to write,” Haus said. “They must understand the problem and use technology to solve that problem.”
The comments provide a glimpse into how one of the world’s largest financial institutions is approaching AI-enabled software engineering and what that means for testing and quality assurance teams inside heavily regulated organisations.
For banks, where resilience failures and software defects can quickly become customer-impacting incidents, Haus’s emphasis on engineering fundamentals is particularly noteworthy.
Faster code generation may accelerate delivery, but it also increases the need for robust testing, governance and architectural discipline.
“Code has transformed. Code is becoming English,” Haus said, arguing that AI tools will enable organisations to deliver more products and features in less time.
Haus repeatedly stressed that AI is not diminishing the importance of software testing and software delivery practices.
Instead, the rise of machine-generated code is making them even more critical, he said in an interview with the website TechTarget.
Automated testing now a strategic requirement
Perhaps the strongest message for quality engineering teams came in Haus’s comments on software testing and the software delivery lifecycle.
“The foundations of good software development and the software delivery lifecycle don’t change. They’ve always been important, and they’re much more important now,” Haus stressed.
For financial institutions increasingly experimenting with coding assistants and AI agents, the challenge is not simply generating code faster but validating it at scale.
“When you’re a human running code, you want to make sure it isn’t broken. If you have a computer now writing code for you, there’s a ton of testing that needs to be done,” he continued.
“We can’t keep up with that unless we automate it, so the good practices we teach in computer science for building software become even more important when you’re engaging with AI,” Haus added.
The comments underline a growing industry reality: AI-driven software development may ultimately increase demand for automated testing capabilities, continuous validation and sophisticated quality engineering practices.
Haus also highlighted the importance of maintaining strong engineering controls as AI systems become more capable. “Security is paramount, and so is privacy,” he stated.
In customer-facing applications, JPMorgan Chase continues to keep humans firmly involved in decision-making processes.
“The guardrails are improving, and over time, we will move toward more agentic experiences. But today, human oversight remains essential so we can intervene if something is off.”
“With controls in place, if an AI agent is writing code, we can detect issues before they reach production and respond quickly.”
– Gill Haus
For software delivery and operational resilience teams, perhaps the most significant observation came when Haus described how confidence in production environments is achieved.
“Confidence in production comes from strong engineering practices, automated testing, automated deployment and automated rollback,” Haus explained.
He added: “With those controls in place, if an AI agent is writing code, we can detect issues before they reach production and respond quickly.”
The emphasis on automated testing and deployment controls aligns closely with the increasing regulatory focus on operational resilience and software assurance across the financial services sector.
As banks introduce AI into their engineering organisations, the ability to test, validate and safely deploy machine-generated code may become one of the defining challenges of the next phase of digital transformation.
For JPMorgan Chase, however, the message appears straightforward: AI may change how software is written, but resilient engineering, testing discipline and strong governance remain the foundations on which safe and reliable banking technology is built.
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