As banks rely more on AI for daily decisions, those whose teams can confidently work with, challenge, and safely grow these systems will benefit most (not just those running pilot projects). This is when AI workforce readiness moves from being an HR task to becoming a core business strategy for AI in banking.
Now that AI is central to digital transformation in banking, true progress depends on closing the AI skills gap and developing digital banking talent alongside investing in technology.
That is exactly what this POV explores: how AI in banking, workforce readiness, and the AI skills gap come together to decide which BFSI leaders pull ahead.
How Fast AI Is Moving in Banking
Across industries, AI has gone from “interesting experiment” to “everyday tool” in just a few years. Reported use of AI increased in 2024, with 78% of organizations now saying they use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier.
For professionals building careers in BFSI, this also reshapes role expectations and skill paths, a shift you can read about in our blog: How to Build a Successful Career in Banking & Financial Services in 2025
AI adoption in banking sits at the centre of this curve, with AI supporting fraud monitoring, credit decisions, service interactions, and compliance tasks.
The money flowing into AI tells the same story.
Financial services firms are estimated to have spent about 35 billion dollars on AI in 2023 and could reach close to 97 billion dollars by 2027.
Grand View Research suggests the AI in banking market could grow to more than 140 billion dollars by 2030, implying growth of over 30% a year. In other words, AI in banking is steadily moving from “innovation initiative” to core infrastructure in BFSI and is now central to banking technology trends.
Why People Decide Whether AI Pays Off
AI adoption in banking only creates value when people across departments know how to use it. Teams need to identify the right use cases, understand data, question AI outputs, and run models safely within regulations. That’s why leading firms now treat AI workforce readiness as a strategic priority, putting people and processes ahead of technology alone.
Despite growing AI adoption in banking, many organizations still lack the skills to get the most from it. Only about a quarter feel ready, while most struggle to scale value (source: BCG). At the same time, AI skills demand is soaring, and banks face a serious tech talent shortage. They compete for scarce AI, data, and cloud skills, and also need to upskill their people. The banks that close the AI skills gap and build strong banking digital talent will keep up with where AI is heading.
What Should You Do as a Banking Leader?
Today, AI in banking is as much about people as it is about technology. The most successful banks focus on AI workforce readiness, making sure their teams not only use AI tools, but also question and improve them in a regulated environment. This approach turns workforce strategy into a core part of AI strategy.
Three practical moves stand out:
- Focus on skills, not just job titles. Build teams with up-to-date skills in AI, data, cloud, cybersecurity, and model risk, so they can keep up with changing use cases, rising AI skills demand, and new rules.
- Be flexible in how you find talent. Mix internal teams with outside AI experts who can help quickly with tasks like data engineering or AI governance. This helps manage the tech talent shortage.
- Make upskilling part of everyday work. Help teams in risk, operations, and business keep growing their AI skills, so they’re always ready for new challenges.
This is consistent with what AI leaders in other sectors are doing:
BCG notes that high performers put a significant share of their AI investment into people and processes, not just platforms.
Our blog on AI and the Evolving Workforce: Roles, Skills, and Opportunities in 2025 expands on this shift, with practical examples of new AI driven roles emerging across industries and what they mean for workforce strategy.
What Defines an AI-Ready BFSI Workforce
In future-ready banks, AI literacy becomes a basic skill everyone uses, not just something for a small group of specialists. This means relationship managers, underwriters, branch staff, and operations teams all know how to use AI-driven insights, spot when something isn’t right, and raise concerns when needed.
Alongside these teams, banks have access to the right mix of specialist banking digital talent. They can quickly bring in experts for things like model risk, regulatory reporting, or new fraud trends whenever needed. By blending permanent staff with global talent pools and flexible experts, banks can adjust their workforce as projects change, without being slowed down by lengthy hiring. Good governance is built in from the start, with risk and compliance teams working together as AI use grows and regulations evolve.
Conclusion
AI adoption in banking is now mainstream, with over 70% of organizations using it and investment set to nearly triple soon. Banks that lead the way combine technology spending with steady investment in skills, workforce models, and AI workforce readiness. The real difference comes from building teams who can make AI work, grow, and stand out from the rest.
If you’re looking to build an AI‑ready banking workforce and want the right mix of skills, talent models, and support, feel free to connect with us.
