Enterprise AI is past the hype phase and into the hard part: scaling what works without breaking security, blowing out costs, or shipping chaos into production. In this episode, Georgie chats with AWS technologist Rada Stanic about using AI as a “study buddy” to renew technical certifications faster, and why tools like AWS QuickSight can generate strong first drafts of strategy docs when you provide the right templates and context.

They go deep on AIOps: the operational discipline enterprises need to deploy agents and GenAI reliably at scale. Rada breaks AIOps into five practical pillars: defining agent intent, identity and security boundaries, policy and governance, observability and evaluation, and managing the rapid model lifecycle as new LLMs drop constantly. The conversation also covers why security questions dominate every enterprise AI project, why data quality still makes or breaks outcomes, and why “RAG” is fading as a buzzword even though retrieval is still foundational.

Finally, Rada shares a sharp concern for the next generation: what happens to junior roles when AI fills the entry level work, and why the pace of change itself may become the next generation’s greatest advantage.