AI in Project Management

This course introduces students to the principles and applications of AI-driven project management, emphasizing how artificial intelligence enhances traditional project management processes. Students will explore AI-powered tools and techniques for planning, scheduling, risk analysis, resource optimization, and performance monitoring. Through practical examples, case studies, and hands-on activities, the course prepares learners to manage complex projects more efficiently and make informed, data-driven decisions in today’s technology-driven organizations.

The objective of the AI-Driven Project Management course is to equip students with the knowledge and practical skills to integrate artificial intelligence into modern project management practices. The course focuses on using AI-powered tools and data-driven techniques to improve project planning, scheduling, risk management, resource allocation, and decision-making. By the end of the course, students will be able to apply AI concepts to real-world project scenarios, enhance project performance, and lead technology-enabled projects effectively in dynamic organizational environments.

By the end of this course, students will be able to:

• Embracing AI in Project Management and Basic Concepts
• The AI Project Life Cycle: Navigating from problem scoping to evaluation
• Optimizing Project Outcomes with AI: AI tools and techniques
• The business challenges of incorporating AI into the organization
• Real-world applications of AI in project management
• Harnessing the future: Action plan for AI-Driven project management

AI in project management refers to the application of artificial intelligence technologies to support project planning activities. AI tools automate repetitive tasks and analyze large volumes of project data to provide actionable insights. These tools allow project managers to streamline workflows, improving decision-making, reduce time-consuming manual tasks and drive project success.

AI technologies—such as machine learning, natural language processing (NLP), generative AI, predictive analytics and automation—are being integrated into intelligent systems that act as copilots for project management. These intelligent assistants help teams manage workflows, track milestones and allocate resources with better efficiency. Instead of traditional static processes, AI tools allow for dynamic, data-driven approaches that support productivity and collaboration between team members and stakeholders.

Project management involves preparing for and executing business initiatives to achieve specific goals within defined constraints such as time, budget and scope. Effective project management is relevant to every industry, from small startups launching niche products to global corporations handling multi-million dollar infrastructure projects. Poorly managed projects can lead to wasted resources, missed deadlines or financial losses.

AI is changing the future of project management by enabling smarter planning and faster execution. AI introduces data-driven approaches to project workflows so project leaders can make better-informed choices.

Gartner research has predicted that by 2030, 80% of routine project management tasks would be handled by AI. In a sign of the speed at which AI is being adopted in the field, a study by the Association for Project Management found that 70% of project professionals said their organization used AI, up from 36% two years earlier. As the technology continues to advance, so too will the ways in which it can be applied.

Some of the major benefits of applying AI tools to project management include:

• Greater efficiency: AI-powered systems automate manual tasks and track project progress so teams can focus on more strategic decision-making.
• Improved communication: AI chatbots and natural language processing tools like ChatGPT provide instant communication and smooth communication for stakeholders and team members.
• Informed decision-making: AI can process current project data in real time to detect trends, assess resource allocation and suggest improvements as the project evolves.
• Smarter predictions: By analyzing historical performance data, AI applies predictive analytics to forecast risks, so problems can be addressed earlier in the process.

Summer 2026: 

Dates: June 6 - August 1 | Saturdays | 10:00am - 12:00 pm 

Total Instructional Hours: 16; Total Sessions: 8; ONLINE

$400 + $25 registration fee

Total: $425*

*CCNY students, staff, and Alumni Association members receive a 10% discount.

Mehdi Hasan, PhD

Dr. Mehdi is an AI for development (AI4D) professional. His research interests include Product/project management, Human-AI interaction, eHealth and AI for Social Good. He is aspired to make technology as a tool to determine the deficiency in expected results and actual outputs to establish social equity and good governance in emerging economies. He is a founder and Executive Director of Infotix. He is also a reviewer of the International Journal of Advanced Computer Science and Applications, Saga University, Japan.