As banks move beyond isolated use cases toward scaling generative AI, they’d be wise to remember that AI is as much a people story as it is a technology shift. Most banks can’t hire their way to AI salvation: The traditional approach of targeting highly specialized talent with advanced degrees is impractical, costly, and often unnecessary.
The key for banks to become AI-ready lies in democratizing the technology, making it accessible to as many employees as possible and encouraging experimentation. This combination not only accelerates adoption but can uncover unexpected use cases.
The top banks are already doing this, such as Bank of America and JPMorgan Chase. Their approach is not about expecting everyone to become an AI expert overnight. Instead, it’s about fostering a culture where curiosity and innovation are rewarded and co-learning — the continuous collaboration between people and AI, with feedback, iterative learning and adaption happening in real time – is embraced.
There are parallels here to the 1970s, when a shortage of computer science majors existed to fill programming roles. Then, as in now, organizations didn’t have the needed expertise they had to build it from within.
So IBM developed the Information Processing Aptitude Test to help identify which of its existing employees would have the aptitude to be a successful programmer. The test proved remarkably accurate and, interestingly, one of the top backgrounds with the aptitude to learn programming was musicians (perhaps a philosophy or English degree won’t be so ‘worthless’ in the age of AI). This illustrates that the talent best suited to leverage GenAI may be outside the traditional technology backgrounds.
How To Find High-Agency Employees
Outside of banking, AI is already unleashing a wave of creativity, including through vibe coding, which is leading to the creation of compelling new mobile apps. You don’t need to be a programmer anymore to do this: High-agency individuals, characterized by their curiosity and willingness to challenge the status quo, can turn their visions into reality.
It’s time for banks to develop their own version of the IPAT for AI to identify the high-agency employees who are best suited to adopt and leverage the technology.
The test could assess logical thinking, problem-solving abilities, and a willingness to experiment. It would identify employees who have the potential to become GenAI champions and ambassadors, along with those who could serve as ‘humans in the loop’ for agentic AI, monitoring and supervising agents to ensure they are operating as intended.
Banks can’t afford to have employees “asleep at the wheel” here. The reality is that if a bank has 10,000 employees today, it must be thinking about how to manage 50,000 in the future – with 40,000 of those being agents. Banks shouldn’t think of AI as taking away, but as being additive and changing the way work gets done and monitored. If one employee can effectively manage up to 10 different agents, that is high agency and can elevate productivity.
Finding and training these employees can be challenging. Accenture (my employer) recently published Pulse of Change research, which surveyed bank executives in June, and 72% of respondents admitted that the pace of change in the AI environment is moving faster than their organization’s ability to train and prepare their workforce.
Imagine if banks could build a questionnaire that measures these aptitudes and run it across their teams. They could then use the insights to identify and develop the right talent that can thrive alongside AI.
This is not just a theoretical exercise; it’s a practical solution that can help banks stay ahead of the curve as they launch more gen AI tools and seek to train employees. A Massachusetts Institute of Technology report published last November titled “Artificial Intelligence, Scientific Discovery and Product Innovation” showed that productivity gains from GenAI are uneven among workers in the same profession. While AI can help the most productive and experienced workers to be even more efficient, the least experienced and less productive employees still spend their time ineffectively.
Meanwhile, a Stanford paper published in June titled “Future of Work with AI agents” outlined a framework to estimate workers’ readiness and willingness to work with AI agents. It found that there are “green light” tasks that have both a high automation likelihood and a high employee desire to automate — making them prime targets for AI deployment — as well as “red light” tasks that have a high automation likelihood but low employee desirability. The green light zone is what JPMorganChase refers to as the “no-joy work” — work that employees hate doing that can be automated. That will be the easy part. The hard part is tackling the work that should be automated that people don’t want to give up so easily.
In fact, a large proportion of employees remain hesitant to use AI, with more than three in 10 (31%) admitting that they’re actively working against their company’s AI initiatives (a figure that rises to 41% for Gen Z workers), according to a study from the AI platform Writer in March. This could stem from worker anxiety about job replacement — Citi estimates that 54% of bank jobs are considered to have a high potential for automation by AI.
But much like the launch of Excel in the early 1980s — when fears of mass job losses in banking ran rampant – I’m bullish that generative AI will lead to more jobs, not less, but different. AI can take waste out and put value in by allowing employees to focus on what matters, just like Excel has.
The best bank employees will grasp that the future of banking is a mix of human + machine and work side-by-side with digital workers and eventually agentic AI to reimagine key banking functions, including know-your-customer processes, fraud prevention, customer support, software development and more.
Banks’ leadership can help. Visionary leaders who listen and collaborate with employees are more likely to foster a culture of innovation and curiosity. They can create an environment where employees feel supported and are encouraged to take risks without fear of failure or job displacement.
Beyond finding the right people to harness the technology, culture may be the ultimate differentiator. The most successful banks will be those who embrace a culture of curiosity tempered with strong execution. This culture — of continuous learning, with a mindset where employees are looking for ways to improve, innovate and execute responsibly from a compliance and risk perspective — will be as important as the technology itself.
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