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Skill-Up EdTech Team
and
Daniel C. Yeomans
Generative AI is transforming how businesses operate. According to a McKinsey survey, over 78% of organizations are implementing AI, making its impact undeniable. Netflix’s AI-driven recommendations feature is one such example. With companies leaning to develop AI-powered products, the AI product manager role is becoming increasingly critical.
Read more
Generative AI is transforming how businesses operate. According to a McKinsey survey, over 78% of organizations are implementing AI, making its impact undeniable. Netflix’s AI-driven recommendations feature is one such example. With companies leaning to develop AI-powered products, the AI product manager role is becoming increasingly critical.
Read more
Generative AI is transforming how businesses operate. According to a McKinsey survey, over 78% of organizations are implementing AI, making its impact undeniable. Netflix’s AI-driven recommendations feature is one such example. With companies leaning to develop AI-powered products, the AI product manager role is becoming increasingly critical.
This course covers the AI product manager’s role in managing product lifecycles with AI. You will learn about the AI process and how to balance traditional and AI skills, build the right team, and communicate with stakeholders. You will also explore common challenges and reasons for AI project failures. Next, you will learn about the AI product development stages and product management phases. You will also understand AI’s impact across industries and review real-world use cases, commercialization strategies, and future trends.
Throughout this short self-paced course, you will be presented with instructional guidance through videos followed by hands-on labs to practice what you learn. You will also complete a final project to showcase your AI product manager skills.
Enroll now to master AI product management!
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What's inside
Syllabus
AI Methodology, Opportunities, and Challenges
This module explains the AI product manager’s role in managing the product lifecycle by leveraging AI technologies. You will learn about the AI process, the ROI that AI brings to a project, and how a product manager balances traditional product management skills with AI-specific knowledge. You will learn how a product manager builds the AI “Dream Team” and communicates AI to stakeholders. In addition, you will look at the challenges faced by a product manager and the reasons for the failure of AI product development projects.
Read more
Syllabus
AI Methodology, Opportunities, and Challenges
This module explains the AI product manager’s role in managing the product lifecycle by leveraging AI technologies. You will learn about the AI process, the ROI that AI brings to a project, and how a product manager balances traditional product management skills with AI-specific knowledge. You will learn how a product manager builds the AI “Dream Team” and communicates AI to stakeholders. In addition, you will look at the challenges faced by a product manager and the reasons for the failure of AI product development projects.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops product management skills and knowledge crucial for a product manager in the AI field
Taught by Daniel C. Yeomans, an experienced professional in the EdTech industry
Explores the opportunities that AI brings to product management, including its ROI
Examines challenges faced by AI product managers and reasons for project failures
Provides real-world AI use cases and commercialization strategies
Requires students to complete a final project to demonstrate their understanding
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Reviews summary
Ai product management essentials
According to learners, this course offers a solid foundation for the AI Product Manager role, effectively bridging the gap between traditional product management and AI. Students praise the clear and engaging video lectures and the emphasis on AI-specific aspects like team building, stakeholder communication, and common project failures. While the hands-on labs and final project are noted as helpful for practical application, some learners with prior AI or PM backgrounds perceive the content as a high-level overview. This suggests it may be less suited for advanced practitioners seeking deeper technical dives or cutting-edge examples, but serves as a strong starting point for aspiring AI Product Managers.
Hands-on components reinforce learning but may lack depth.
"I particularly liked the hands-on labs, which were practical."
"The final project really helped me apply what I learned about building an AI-driven system."
"The labs were a bit basic for me, but I can see how they would be helpful for someone newer to the field."
"I prefer more hands-on, real-world case studies with actual data. The labs were simplistic."
Best for those new to AI PM, less for advanced learners.
"Good for beginners to intermediate."
"If you have a background in both product management and AI, much of it might feel like a recap."
"I came in hoping for more technical depth in AI development for product managers. This course leans heavily on the management side..."
"My only minor critique is that some explanations felt a little too introductory, assuming very little prior knowledge."
Provides clear, comprehensive introduction to AI PM.
"The course provides a solid foundation for AI PM."
"Excellent overview of the AI Product Manager role. The instructor was clear and covered all the essentials."
"As a traditional PM, this course was invaluable for understanding the nuances of AI product development."
"This course is a must for product managers looking to adapt to the AI era. It clarifies the specific challenges and opportunities."
Focuses on unique challenges of AI product management.
"The focus on team building and stakeholder communication for AI projects was particularly insightful."
"The discussions on common AI project failures were very practical."
"It perfectly bridges the gap between traditional PM and AI. The sections on balancing skills and communicating with stakeholders were spot on."
"I found the module on 'AI Dream Team' building especially helpful."
Some desire more advanced topics and complex examples.
"However, I felt it could delve deeper into advanced topics like ethical AI implications or more complex model deployment strategies."
"Some parts felt a bit high-level, and I was hoping for more granular examples in real-world scenarios."
"I wish there was more content on measuring AI product success beyond typical ROI."
"Disappointed with the depth. It felt like a high-level overview without enough practical application."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Product Management: Building AI-Powered Products with these
activities:
Explore Microsoft Learn AI Modules
Show steps
Build a solid foundation in AI concepts and techniques.
Browse courses on
AI Fundamentals
Show steps
-
Enroll in the 'AI Fundamentals' learning path.
-
Complete the interactive modules and exercises.
-
Apply what you learn to your product management projects.
Join an AI Product Management Study Group
Show steps
Collaborate with peers, share knowledge, and gain diverse perspectives on AI product management.
Show steps
-
Find or create a study group with like-minded individuals.
-
Meet regularly to discuss course topics, case studies, and industry trends.
-
Provide constructive feedback and support to group members.
Attend an AI Product Management Conference
Show steps
Connect with industry experts, learn about the latest trends, and expand your network.
Show steps
-
Research and find upcoming AI product management conferences.
-
Register and attend the conference.
-
Engage with speakers and attendees to learn and share insights.
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve AI Product Management Case Studies
Show steps
Test your understanding of AI product management principles and decision-making.
Show steps
-
Find case studies on AI product management online.
-
Analyze the case studies and identify the challenges and opportunities.
-
Develop and present your own solutions.
Develop an AI Product Concept Document
Show steps
Apply your knowledge to create a comprehensive document outlining an AI product concept.
Show steps
-
Identify a problem or opportunity related to AI.
-
Define the target audience and user needs.
-
Research existing AI solutions and technologies.
-
Develop a product vision, mission, and roadmap.
-
Present your concept document to stakeholders.
Explore Microsoft Learn AI Modules
Show steps
Build a solid foundation in AI concepts and techniques.
Browse courses on
AI Fundamentals
Show steps
Show steps
Show steps
- Enroll in the 'AI Fundamentals' learning path.
- Complete the interactive modules and exercises.
- Apply what you learn to your product management projects.
Join an AI Product Management Study Group
Show steps
Collaborate with peers, share knowledge, and gain diverse perspectives on AI product management.
Show steps
Show steps
Show steps
- Find or create a study group with like-minded individuals.
- Meet regularly to discuss course topics, case studies, and industry trends.
- Provide constructive feedback and support to group members.
Attend an AI Product Management Conference
Show steps
Connect with industry experts, learn about the latest trends, and expand your network.
Show steps
Show steps
Show steps
- Research and find upcoming AI product management conferences.
- Register and attend the conference.
- Engage with speakers and attendees to learn and share insights.
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve AI Product Management Case Studies
Show steps
Test your understanding of AI product management principles and decision-making.
Show steps
Show steps
Show steps
- Find case studies on AI product management online.
- Analyze the case studies and identify the challenges and opportunities.
- Develop and present your own solutions.
Develop an AI Product Concept Document
Show steps
Apply your knowledge to create a comprehensive document outlining an AI product concept.
Show steps
Show steps
Show steps
- Identify a problem or opportunity related to AI.
- Define the target audience and user needs.
- Research existing AI solutions and technologies.
- Develop a product vision, mission, and roadmap.
- Present your concept document to stakeholders.
Career center
Learners who complete Product Management: Building AI-Powered Products will develop knowledge and skills
that may be useful to these careers:
AI Product Manager
AI Product Manager
An AI Product Manager is responsible for the development and management of AI-powered products. This course provides a comprehensive overview of the AI product manager's role and the skills needed to be successful. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation. This course can help you build a strong foundation for a career as an AI Product Manager.
Product Manager
Product Manager
A Product Manager is responsible for the development and management of products. This course can help you build a strong foundation for a career as a Product Manager, and it can also help you develop the skills needed to manage AI-powered products. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
AI Engineer
AI Engineer
An AI Engineer is responsible for the development and deployment of AI models. This course can help you build a strong foundation for a career as an AI Engineer, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Data Scientist
Data Scientist
A Data Scientist is responsible for the analysis and interpretation of data. This course can help you build a strong foundation for a career as a Data Scientist, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Software Engineer
Software Engineer
A Software Engineer is responsible for the development and maintenance of software applications. This course can help you build a strong foundation for a career as a Software Engineer, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
UX Designer
UX Designer
A UX Designer is responsible for the design and implementation of user interfaces. This course can help you build a strong foundation for a career as a UX Designer, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for the development and execution of marketing campaigns. This course can help you build a strong foundation for a career as a Marketing Manager, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Sales Manager
Sales Manager
A Sales Manager is responsible for the development and execution of sales strategies. This course can help you build a strong foundation for a career as a Sales Manager, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Business Analyst
Business Analyst
A Business Analyst is responsible for the analysis and interpretation of business data. This course can help you build a strong foundation for a career as a Business Analyst, and it can also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Project Manager
Project Manager
A Project Manager is responsible for the planning and execution of projects. This course may be useful for building a strong foundation for a career as a Project Manager, and it may also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Consultant
Consultant
A Consultant is responsible for providing advice and guidance to businesses. This course may be useful for building a strong foundation for a career as a Consultant, and it may also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Entrepreneur
Entrepreneur
An Entrepreneur is responsible for starting and running a business. This course may be useful for building a strong foundation for a career as an Entrepreneur, and it may also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Teacher
Teacher
A Teacher is responsible for educating students. This course may be useful for building a strong foundation for a career as a Teacher, and it may also help you develop the skills needed to work with AI product management teams. It covers topics such as AI methodology, opportunities, challenges, integrating AI into the product management lifecycle, and final submission and evaluation.
Doctor
Doctor
A Doctor is responsible for diagnosing and treating patients. This course would not be useful for building a strong foundation for a career as a Doctor.
Lawyer
Lawyer
A Lawyer is responsible for representing clients in legal matters. This course would not be useful for building a strong foundation for a career as a Lawyer.
For more career information including salaries, visit:
OpenCourser.com/course/xkmhf0/product
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Product Management: Building AI-Powered Products.
Save
Provides a comprehensive overview of predictive analytics, which is essential for AI product managers to understand. It explores various techniques and case studies, offering insights into how AI can be used for data-driven decision-making.
For those interested in the technical aspects of AI product development, this book offers a practical introduction to deep learning. It covers both theoretical concepts and practical implementation using tools like fastai and PyTorch.
Save
Provides a deep dive into the architectural and technical aspects of designing data-intensive applications, a crucial skill for AI product managers. It covers concepts such as data modeling, data storage, and data processing, complementing the course's emphasis on AI product development stages.
Introduces the Lean Startup methodology, which focuses on rapid iteration and customer feedback. It provides a valuable framework for AI product managers to test and refine their ideas quickly, complementing the course's emphasis on building the right team and communicating with stakeholders.
Presents a collection of case studies and examples of how companies are using AI and machine learning to address real-world problems. It complements the course's focus on commercialization strategies and use cases.
Provides a gentle introduction to natural language processing for beginners.
Save
Provides a comprehensive overview of deep learning, including the mathematical foundations of deep learning, the different types of deep learning models, and the applications of deep learning.
Provides a gentle introduction to machine learning for beginners.
Provides a gentle introduction to deep learning for beginners.
Introduces fundamental algorithms for data science and machine learning. It provides a solid foundation for understanding the algorithmic aspects of AI product development.
This introductory book provides a gentle introduction to machine learning and its applications. It offers a good starting point for those with no prior background in AI or data science.
Examines the ethical implications of AI development and deployment. It explores issues such as privacy, fairness, and accountability, complementing the course's emphasis on challenges and future trends.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xkmhf0/product
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Effort
3 weeks of study, 2-3 hours/week
Level
Introductory
Via
Coursera
Institution
SkillUp EdTech
Instructors
Skill-Up EdTech Team
Daniel C. Yeomans
Language
English
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