Most conversations about AI in project management focus on AI as an assistant that drafts status reports and summarizes meetings. This session addresses a different and faster-growing challenge: managing projects where the AI system itself is the product.

 

These initiatives behave unlike traditional software or infrastructure projects. Requirements are probabilistic rather than fixed, success criteria shift as models are trained and evaluated, data quality drives outcomes more than feature scope, and governance obligations from emerging frameworks like the NIST AI Risk Management Framework and the EU AI Act reshape planning and stakeholder expectations.

 

Drawing on both practitioner experience and current governance research, this session gives project managers a practical way to recognize when they are leading an AI project, understand where conventional methods break down, and adapt their planning, risk, and stakeholder approaches accordingly.

 

Attendees will leave able to distinguish PM with AI from AI PM, and to lead the latter with confidence.