The largest financial institution in the United States is undertaking an unprecedented initiative: ensuring that every one of its more than 300,000 employees becomes deeply proficient in leveraging artificial intelligence to enhance their work. With a technology budget exceeding $18 billion, JPMorgan Chase has already poured immense resources into AI research and development. Now, the organization is redirecting its attention toward equipping its entire global workforce with the ability to practically apply these tools. According to Derek Waldron, the bank’s Chief Analytics Officer, this mission reflects not a uniform or simplistic training plan, but a nuanced, targeted strategy aimed at empowering each employee to exploit AI in ways most relevant to their role and daily responsibilities. In a recent conversation with McKinsey & Company, Waldron emphasized that the diversity of applications necessitates equally diverse learning pathways.
Waldron explained that just as artificial intelligence encompasses a vast spectrum of functions—from data analysis and natural language processing to decision support and automation—employee training must be adapted to meet varied needs across departments. In his words, “Training needs are as diverse as AI applications themselves,” which means that the most effective strategy is a segmented approach, tailored to different groups within the company. Everyone, from junior analysts and operations staff to senior executives, will need to expand their technical and analytical skill sets in order to meaningfully engage with intelligent systems.
To build this foundation, JPMorgan has rolled out a dedicated internal learning program called “AI Made Easy,” specifically designed to introduce beginners to the fundamentals of artificial intelligence. Waldron noted that tens of thousands of employees have already completed the course, reflecting robust enthusiasm across divisions. The educational modules guide learners through practical exercises, such as conducting in-depth research using generative models, structuring queries for optimal performance, and interpreting outcomes derived from large and complex data sets. Rather than treating AI as a distant or abstract concept, the training focuses on embedding expertise directly into everyday workflows, helping employees discover how the technology can serve as an empowering complement to human judgment.
The transformation, however, is not confined to the broader employee population alone. Waldron observed that senior leaders and managers must also evolve their methods and mindsets. He anticipates that the next generation of CEOs and business heads will need to develop sophisticated strategies for guiding their organizations through this technological shift. In his view, genuine business value will not arise merely from handing employees advanced tools but from leaders orchestrating cross-functional collaboration and cultural change. He urged that executives must become champions of innovation, ensuring that AI integration translates into long-term strategic advantages rather than isolated technical experiments.
Supporting this cultural movement, JPMorgan has implemented a comprehensive and multi-layered communication plan. Outreach channels include company-wide town halls, manager-led discussions, and in-office marketing displays that continuously promote the initiative. These diverse formats are helping to demystify AI and normalize its presence within the corporate environment, promoting curiosity and confidence among employees who may initially be apprehensive about emerging technologies.
Waldron elaborated that their pedagogical structure unfolds in two fundamental phases. The first step focuses on developing an understanding of what generative and large language models are truly capable of—and, equally important, what their limitations are. Only after staff build a conceptual familiarity with AI’s potential and its constraints does the second stage begin: learning how to formulate precise and effective questions, commands, or prompts. Through practical exercises and guided examples, trainees explore frameworks for prompt construction, the logical variables involved, and the art of steering AI-generated outputs toward meaningful results. As proficiency develops, more complex topics are introduced, such as teaching models to adopt specific “personas” that shift between creator and reviewer roles, or orchestrating debates between multiple models to stimulate creativity and examine differing perspectives on the same problem.
This holistic reform is not being conducted through isolated top-down directives; rather, it thrives through collaborative learning. Waldron pointed out that employees are organically forming networks of knowledge sharing. Teams are establishing internal “prompt libraries,” weekly newsletters highlighting innovative examples, and social channels for exchanging advanced usage techniques. Such peer-to-peer communication ensures that insights spread rapidly across the organization. Waldron emphasized that when cutting-edge technology is placed directly into employees’ hands—accompanied by structured training and thoughtful change management—the workforce becomes a powerful engine for innovation and improvement.
He further noted that the evolution of roles within technical departments vividly illustrates this change. Software engineers, for instance, are being retrained to architect scalable systems that rely on AI agents and large language model components. These professionals must now learn to design and maintain infrastructures flexible enough to support continuous AI evolution. Similarly, other technologists within JPMorgan are becoming increasingly interested in constructing intricate applications using generative or agentic AI frameworks—a skill set that requires specialized instruction and disciplined practice to master.
Agentic AI, which enables semi-autonomous digital agents to perform complex, end-to-end tasks with minimal supervision, has recently become a hot topic both on Wall Street and in Silicon Valley. While this concept has sparked debate—some hailing it as revolutionary, others cautious about the implications—its possibilities are capturing the imagination of leaders across industries. At a recent conference, Teresa Heitsenrether, JPMorgan’s Chief Data and Analytics Officer and Waldron’s direct superior, remarked that overseeing digital agents could allow new employees to experience management-level decision-making earlier in their careers, cultivating leadership skills from the outset.
For data scientists, meanwhile, the AI revolution is fundamentally redefining the boundaries of their discipline. Waldron noted that many traditional tasks, such as building standard predictive models from scratch, are now handled by external providers supplying robust, well-tested frameworks. This shift enables in-house data specialists to redirect their expertise toward higher-order functions—designing, validating, and optimizing AI systems for specific institutional needs. Put simply, it brings them closer to the most intellectually rewarding part of their profession: shaping how machine intelligence interacts with human insight to generate value.
In essence, JPMorgan’s comprehensive AI education campaign represents far more than a corporate training exercise. It is an ambitious institutional transformation that seeks to blend human intelligence with machine capability, preparing an entire workforce to thrive in a rapidly evolving digital era. By cultivating curiosity, technical fluency, and creative confidence among its 300,000 employees, the bank is positioning itself at the forefront of a new model of enterprise innovation—one in which every individual, regardless of rank or role, contributes to shaping the intelligent systems that will define the future of banking and work itself.
Sourse: https://www.businessinsider.com/how-jpmorgan-is-training-employees-artificial-intelligence-2025-10
