Managing the New AI Team

Ways of Working, Power Skills

Nicholas Beaudoin

Director of AI Programs

Caltech

About the Presentation

The emergence of generative AI has redefined how organizations build and deploy intelligent systems. Traditional machine learning teams, once focused on algorithm development, model training, and structured data workflows, are rapidly evolving into AI engineering teams centered around pre-trained foundation models and API integration. This talk explores the operational and organizational impacts of that shift. Project managers will learn how to realign team roles, timelines, and success metrics to support AI initiatives driven by tools like LangChain, vector databases, and prompt orchestration. As generative AI moves from proof-of-concept to production, project leaders must manage evolving skill sets, fragmented MLOps practices, and increasing pressure to deliver scalable, trustworthy AI solutions.

Learning Objectives:

·         Differentiate AI and ML Team Structures - Understand the key organizational and workflow differences between traditional ML teams and modern AI engineering teams leveraging generative AI.

·         Manage Evolving Technical Skill Sets - Identify the new skills required for AI projects—including API orchestration, prompt engineering, and vector search—and how to align hiring and team composition accordingly.

·         Navigate Shifting Development Lifecycles - Learn how generative AI alters the traditional project lifecycle, from proof-of-concept through deployment, and what it means for timelines, deliverables, and risk.

·         Ensure ROI Through Evaluation and Governance - Develop strategies to evaluate generative AI solutions for reliability and business value, including model testing, performance metrics, and governance practices.

About Nicholas Beaudoin

Nicholas Beaudoin is the Director of AI at Wavicle Data Solutions and the Director of AI Programs at Caltech’s Center for Technology and Management Education (CTME). He designs and leads executive AI training and strategy initiatives, focusing on real-world AI adoption. With a background in building and managing AI/ML teams, Nicholas has shifted from hands-on development to advising industry leaders on AI integration, governance, and education. He has authored white papers on AI team evolution and built enterprise AI programs across sectors like energy, finance, and manufacturing. His expertise spans generative AI, compliance, and large-scale AI system deployment.