Bernardo Nunes is a data scientist specializing in AI transformation at Workera.
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AI is no longer just an experiment. According to McKinsey’s latest Global Survey on AI, 78% of organizations now use AI in at least one business function.
The banking industry is catching up fast. A recent EY-Parthenon survey found that 77% of banks have launched or soft-launched generative AI applications, up from about 61% in 2023. However, only 31% have progressed toward full implementation.
Meanwhile, while there is widespread AI investment in the banking industry, only a few have woven these capabilities into their strategic playbook. A BCG survey reported that just 25% of banks have done so — and the remaining 75% are stuck in siloed pilots and proofs of concept, risking irrelevance as digital-first competitors push ahead.
The banking industry is defined by strict regulations and deliberate strategies. That history has led to both risks and opportunities with AI. While other industries have raced ahead, banks that act now still have the chance to claim a first-mover advantage. Implementing AI successfully requires infrastructure, models, data pipelines, and compliance strategies. However, the most important aspect in turning AI’s promise into business value lies in human capital.
The financial institutions that win will be those that enable their employees to use AI tools not just ad hoc, but as part of their daily workflow. That means developing real, verified skills so that people can understand, leverage, and lead AI innovation.
Why employees drive AI innovation
AI has the potential to deliver incredible gains across productivity, customer experience, and risk management. But at its core, AI is simply a tool — one that requires human creativity and domain expertise to generate actual business value. Technology alone doesn’t drive innovation; people do. In banking, where trust, regulation, and judgment are central, this interplay between human and machine becomes even more important.
Every employee today must become an AI-enabled employee to varying degrees. Some will be deeply technical — data scientists, engineers, and model builders responsible for designing and maintaining the systems that underpin AI operationalization. Others, like tellers, underwriters, or customer service representatives, may never touch a line of code but can still use AI-powered tools to streamline workflows and make better decisions. Between these extremes lies “AI+X” employees. These are individuals who bring deep subject-matter expertise in areas like credit risk, compliance, or fraud detection and pair it with enough AI literacy to use the technology to augment that expertise.
AI+X employees will be those who drive true innovation. They can help bridge the gap between business needs and technical possibilities, translating complex banking challenges into opportunities for AI to deliver tangible results. For example, a compliance officer with AI fluency can partner with data teams to design fairer, more transparent models for KYC and AML processes. A product manager who prototypes using generative AI can reimagine customer interactions, creating personalized financial advice or improving onboarding journeys. In all these cases, AI amplifies human insight instead of replacing it.
In a sector as tightly regulated and risk averse as banking, this human layer is essential. The technology may identify anomalies or generate recommendations, but it will be humans who interpret, contextualize, and ensure decisions align with ethical, legal, and reputational standards. That’s why the banks that lead in AI adoption are those that invest not only in systems and models, but also in the skills and understanding of their workforce.
Driving development with verified skills
Building an AI-enabled workforce starts with understanding existing skills and gaps. To scale AI successfully, banks need more than enthusiasm and training budgets. They need a foundation of verified, measurable skills data. Without a clear view of employees’ capabilities, leaders can’t make informed decisions about how to develop their people or where to deploy AI most effectively.
Self-assessment alone isn’t reliable. Employees tend to either overestimate or underestimate their proficiency, leading to inefficiencies in training. Verified skills — measured through objective assessments — allow organizations to accurately map out current strengths and weaknesses. With this information, banks can design learning paths tailored to specific processes and goals, whether that means introductory AI literacy for front-line teams, deep technical knowledge for data professionals, or governance expertise for compliance officers.
Once employees know where they stand, they can pursue focused upskilling and verify skills in periodic cycles to measure progress and make accountable investments in people. This cycle of learning and validation creates a culture of continuous improvement, ensuring skills stay current as the field evolves. That’s particularly important in AI, where the half-life of a skill is shorter than ever. What’s considered cutting-edge today might be outdated within a year, making an employee’s capacity to learn quickly more valuable than any specific technical competency.
For banks, this translates into a need to prioritize skill growth velocity — the rate at which employees can acquire and apply new skills. Institutions that cultivate this adaptability will maintain a competitive edge, responding faster to new regulations, customer expectations, and technologies. Verified skills also strengthen governance, ensuring employees understand not just how to use AI, but how to use it responsibly, with attention to fairness, transparency, and risk.
The ultimate goal is alignment. When skills intelligence informs learning strategy — and learning strategy supports business priorities — banks can accelerate their AI transformation with confidence. Verified skills data allows leaders to see where to invest, how to mobilize talent, and when to scale innovation safely.
Building a workforce that wins
This is a pivotal moment for the banking industry. The institutions that establish a foundation for innovation will race ahead, while those that hesitate risk being left behind. The path forward is clear: banks that build broad-based AI capabilities among their employees — especially verified skills that blend technical and domain expertise — will be in the strongest position to thrive.
When every employee is empowered to use AI — whether as a creator, power user, or subject-matter expert — the bank as a whole gains agility, resilience, and the ability to drive strategic value rather than just incremental efficiency. Now is the time to move from experimentation to enablement. In AI, what separates leaders from laggards is not just the models you build or the R&D you fund, but the skills you cultivate.
