Lilly Tech Systems
Learn AI Project Management
Master the unique challenges of managing AI and machine learning projects. From planning and scoping to execution, risk management, and delivery — learn the frameworks that separate successful AI initiatives from the 85% that fail.
Your Learning Path
Follow these lessons in order to master AI project management from start to finish.
1. Introduction
Why AI projects are different, common failure modes, and the mindset shift needed for AI project success.
2. Planning
Scoping AI projects, defining success metrics, building roadmaps, and assembling the right team.
3. Execution
Sprint planning for AI, managing experiments, tracking progress, and iterating on models and data.
4. Risk Management
Identifying and mitigating AI-specific risks: data quality, model drift, ethical concerns, and technical debt.
5. Delivery
Deploying AI to production, measuring business impact, stakeholder communication, and handoff processes.
6. Best Practices
Lessons from successful AI projects, organizational readiness, scaling AI initiatives, and continuous improvement.
What You'll Learn
By the end of this course, you'll be able to:
Plan AI Projects
Scope, budget, and roadmap AI initiatives with realistic timelines and success metrics.
Execute Effectively
Run AI development sprints, manage experiments, and keep projects on track.
Manage Risks
Identify and mitigate AI-specific risks before they derail your project.
Deliver Results
Deploy AI solutions that deliver measurable business value and stakeholder satisfaction.