How do you predict GenAI adoption will evolve in 2025? Any new use cases you’d like to see that haven’t been rolled out yet?

As a data engineer, my focus is on creating end-to-end lineage for transactions within financial companies. I want to explore how GenAI can map transaction flows, identify latencies or failures, and enhance data governance and compliance detection. These are the areas I plan to roll out by 2025, alongside initiatives already approved by leadership. My goal is to bring these capabilities to my team and improve our data management processes.

From a business perspective, particularly in enterprise digital experiences, we're looking to expand GenAI's role in generating meeting summaries and automating action items. For example, during advisor-client interactions, we aim to use tools like Copilot to integrate with platforms like Teams, automatically generating summaries, PowerPoints, or PDFs. This would streamline the process, capturing discussions and action items efficiently. Compliance remains a challenge, but we're exploring how GenAI can assist in this area, similar to how healthcare has matured with note-taking technologies. In software development, we're expanding GenAI's use beyond developers to include product owners, aiding in writing epics or user stories more efficiently, thus improving backlog velocity.

I anticipate that GenAI will integrate more deeply into our DevOps pipelines, enabling end-to-end automation. I also foresee advancements in ethical frameworks for compliance and bias detection. New use cases I envision include automated software architecture design for scalable models and real-time code optimization for performance enhancements.