Shubhra Jain, an Enterprise Architecture leader at Barclays with 18 years of experience in technology, decided to enhance her knowledge by enrolling in the Certificate Program in Agentic AI offered by Johns Hopkins University. Her main goal was to gain a deeper understanding of how AI systems are developed, implemented, and managed in real-world scenarios. Her journey of learning went beyond just theoretical concepts of AI, focusing more on practical experimentation, exploring enterprise use cases, and understanding the governance of AI systems.
The field of artificial intelligence is rapidly evolving, with more organizations shifting towards the adoption of AI technologies. According to McKinsey’s 2026 State of AI survey, 44% of respondents reported that AI was being implemented on a broader scale within their organizations, showcasing a significant increase from the previous year. This transition presents a new challenge for technology leaders, as they now need to have a comprehensive understanding of how AI systems are constructed, where they can be applied, and what regulatory measures are necessary for their integration into business processes.
Shubhra Jain’s decision to delve into Agentic AI stemmed from her desire to comprehend the practical workings of these systems and how they can be effectively governed on an enterprise level. With her extensive background in software engineering, architecture, and technology strategy, she was already familiar with AI governance across various sectors such as Risk, Compliance, Legal, HR, and Sustainability. However, she felt the need to move beyond theoretical knowledge and vendor presentations to truly grasp the complexities involved in building and deploying AI systems.
The Agentic AI program at Johns Hopkins University offered Shubhra a hands-on learning experience, allowing her to work on projects involving autonomous financial analysis and multi-agent mortgage underwriting systems. These projects provided her with valuable insights into autonomy boundaries, decision traceability, auditability, and the role of human intervention within AI systems. By building these systems, she was able to understand how these concepts interact in real-world business scenarios.
Shubhra’s advice to other professionals is to not just learn about AI but to actively engage in building AI systems and apply them to real-world problems. By integrating existing domain expertise with new AI capabilities, technology leaders can bridge the gap between theoretical knowledge and practical application.
Overall, Shubhra’s journey in learning Agentic AI emphasizes the importance of hands-on experience, connecting technical learning to real-world business challenges, and understanding governance through practical application. As technology continues to evolve, continuous learning and adaptability are essential for technology leaders to navigate the ever-changing landscape of AI adoption in enterprises.
