Aman Goyal is an Agentic AI Product Manager working at the intersection of AI systems, content, and enterprise-scale user experience. He currently builds AI-driven decision and automation platforms at T-Mobile, where his work spans agentic systems, knowledge orchestration, and applied AI for high-impact business workflows. Previously, Aman worked at The Trade Desk on trust, policy, and compliance platforms, where he helped design systems that translate complex regulatory and policy requirements into scalable, user-friendly product experiences. His background also includes AI research at Intel, CMU, and IIIT-Hyderabad, where he contributed to published research and holds a U.S. patent. Aman regularly writes and speaks about AI, documentation strategy, and product clarity—especially how teams can design content and knowledge systems that remain usable as AI becomes more autonomous.

Show Me the Agents!” – Decision Framework for Content Operations AI

Content leaders are drowning in agentic AI pitches promising to “revolutionize” operations—but which promises are real and which are expensive vaporware? This session explores practical approaches for evaluating when autonomous AI systems make sense for content operations versus when they’re overkill. Drawing from enterprise implementations, we’ll examine the tension between AI hype and operational reality, discussing cost considerations, organizational impact, and strategic decision-making. Attendees will gain perspective on navigating vendor claims, identifying appropriate use cases, and making technology investments that strengthen rather than disrupt effective content teams..

In this session attendees will learn:

• Approaches for evaluating agentic AI technologies in content operations contexts
• Perspectives on distinguishing genuine transformation opportunities from expensive experiments
• Considerations for navigating vendor claims and assessing emerging AI capabilities
• Insights from enterprise implementations across regulated and complex content environments
• Strategies for framing AI initiatives as team augmentation rather than replacement
• Frameworks for making informed technology decisions amid hype and executive pressure