A Curriculum for AI Product Management
For Those Ready to Learn and Grow on Their Own
Listen to this AI-generated podcast discussing the curriculum. Maybe you get even more inspired and intrigued to go through the entire article!
#beyondAI
What should I start learning if I want to become an AI Product Manager?
It’s a common question, but I’ve always hesitated to give a quick answer. Now that I’ve taken the time to outline it, I understand why: the answer depends on the type of AI product you’re aiming to build and the knowledge and experience you already have.
Are you managing an AI product that customers interact with, like ChatGPT or other external-facing solutions? Or maybe you're focused on an AI product meant for business use, designed to streamline operations or drive revenue? Perhaps it’s an in-house product, built specifically for employees or to optimize processes within the company. How long have you worked in a larger company, and in which field? What kind of work have you done there?
Each of these paths requires a slightly different learning journey. Today, I’m focusing specifically on those who want to become an Internal AI Product Manager—building AI solutions within a company to optimize operations. I’m assuming you’re starting from scratch, coming straight out of university with at least a bachelor's degree, in whatever field.
But before we dive in, it’s important to understand why I see differences between the various product types.
You can approach AI product management from several angles, but one of the clearest distinctions is based on the type of customer you serve. Having worked across B2C, B2B, and internal AI product management, I’ve seen firsthand how different each path really is. The target user changes everything—your priorities, your challenges, and the way success is measured.
Let’s start with B2C AI Product Management. In this space, your focus is always on the individual consumer. You need to understand their behaviors, what motivates them, and what keeps them engaged. The stakes are high because the competition is just a click away. Speed is essential—you’re iterating quickly, constantly optimizing to capture attention and drive engagement. Launching a product in this space is exhilarating because feedback is immediate; you’ll know within hours whether you’ve hit the mark or need to pivot. Scaling is always on your mind—how to reach more users, keep them coming back, and ensure retention. It’s a fast-moving environment, and you have to be ready to adapt instantly to changes in consumer preferences, or risk losing them.
Now, let’s talk about B2B AI Product Management. Here, you’re dealing with companies, not individual consumers, and the focus shifts significantly. It’s not about flashy features; it’s about solving critical business problems. Your product needs to seamlessly fit into a company’s operations, providing measurable value, whether that’s reducing costs, improving efficiency, or optimizing processes. The sales cycles are longer, and the feedback loops aren’t as immediate as in B2C. But once you secure a customer, you’re in for the long run—building strong, deep relationships and tailoring your AI solutions to their evolving needs. You’re not aiming for viral growth, but long-term partnerships. The challenge here is delivering real impact and demonstrating clear ROI over time.
Then, there’s Internal AI Product Management, where my experience is deepest. This is a different challenge, and it's more similar to B2B than B2C. Here, the “customer” is your organization—your colleagues, teams, and internal stakeholders. The goal is to improve processes, streamline operations, and create AI-driven solutions that are used internally. The main challenge is adoption—just because you’ve built something doesn’t mean people will use it. Getting buy-in means working across departments and managing stakeholders effectively. The stakes are high, as you’re not just delivering a product; you’re transforming how the organization functions. Feedback loops are slower, but they’re more personal—you get to see the impact of your work every day.
Having worked across all three types, I’ve learned that each path comes with its own rewards and challenges. Each experience has shaped my approach to AI product management, and today, I’m diving deeper into the specific curriculum for Internal AI Product Managers.
This is my first attempt at putting together a curriculum, and it's taken a lot of time and thought. I know it’s not perfect—it's a starting point that will evolve and improve as more people contribute to it. I’m including links to various online courses, both free and paid, to help guide your learning journey and make it as accessible as possible. This effort is part of my ongoing personal challenge to build a Curated Learning Repository for AI Product Managers, with the ultimate goal of creating the most expertly curated and comprehensive resource available. I'm building this in public, hoping it will become a go-to learning hub for aspiring and current AI Product Managers.
I'd love to collaborate with other experts to make this even better for internal AI Product Management. Your unique insights and experiences could help take this to the next level, and together, we can create something truly impactful. If you're interested in joining forces, please don't hesitate to reach out! I hope this curriculum will be a valuable resource for anyone passionate about making an impact through internal AI initiatives.
Now, without further ado, here it is—the very first (as far as I know) advanced program that offers a comprehensive study path for Internal AI Product Management.
By the way, if you're a university feeling inspired to offer a program like this, I'd love it if you could give a little shout-out to who sparked the idea. 😊 Happy learning!
JBK 🕊️
P.S. Just to clarify, this program isn't actually available—it's intended to inspire you to build your own knowledge, guided by a realistic AI Product Management study plan.
If you’ve found my posts valuable, consider supporting my work. While I’m not accepting payments right now, you can help by sharing, liking, and commenting here or on my LinkedIn posts. This helps me reach more people on this journey, and your feedback is invaluable for improving the content. Thank you for being part of this community ❤️.
Master of Science in Internal AI Product Management
This program was brought to you by the University of BeyondAI 😉
Program Overview
This Master's program is designed for individuals seeking an accelerated and comprehensive education in Internal AI Product Management. Over the course of 6 semesters (3 years), the program covers foundational and advanced concepts, providing an in-depth curriculum that spans technical, managerial, and strategic aspects of AI product management within organizations. Admission to this program requires at least a bachelor's degree in any subject, ensuring that all participants have the necessary academic foundation to engage effectively with the rigorous curriculum.
Why This Program?
Integrated Curriculum: Combines foundational and advanced coursework to ensure a thorough understanding of all necessary topics in Internal AI Product Management.
Emphasis on Organizational Understanding and Generative AI: Incorporates essential courses on company structures, operations, and the latest advancements in Generative AI, recognizing their critical role in modern AI solutions.
Comprehensive Skill Set: Prepares graduates for leadership roles by covering technical, managerial, strategic, and ethical aspects of AI product management.
Industry Alignment: Includes practical experiences like internships and capstone projects to ensure readiness for real-world challenges.
Flexible Learning: Offers elective courses allowing students to tailor their education to specific interests and emerging trends.
Program Structure
Total Credits Required: 180 ECTS (European Credit Transfer and Accumulation System)
Duration: 3 years, divided into 6 semesters
Capstone Project and Internship: Mandatory for graduation
Admission Requirement: At least a bachelor's degree in any subject
Year 1: Foundations in AI, Organizational Understanding, Product Management, and Business
Semester 1 (30 ECTS)
Introduction to Artificial Intelligence and Machine Learning (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: This course provides a foundational understanding of AI and machine learning concepts essential for evaluating AI projects and communicating with technical teams. Internal AI Product Managers (AIPMs) need to grasp the capabilities, limitations, and potential applications of AI technologies to make informed decisions. Understanding core concepts such as supervised and unsupervised learning, neural networks, and model evaluation enables AIPMs to bridge the gap between business objectives and technical possibilities, ensuring that AI initiatives are feasible and aligned with organizational goals.
Fundamentals of Product Management (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: This course establishes core competencies in product lifecycles, user-centered design, market analysis, and balancing stakeholder needs with technical possibilities. AIPMs must guide AI products from conception to delivery, ensuring they meet organizational objectives and deliver value. Understanding product management principles enables AIPMs to prioritize features, manage resources effectively, and align products with user needs and market trends.
Organizational Structures and Operations (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: Provides insights into how companies are structured and operate, including hierarchies, departmental functions, and cross-functional collaboration. AIPMs need to navigate internal dynamics, understand decision-making processes, and work effectively within various organizational models. This knowledge is crucial for aligning AI initiatives with business strategies and gaining support from different departments.
Data Management and Databases (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: Introduces the fundamentals of data storage, retrieval, and management, including relational databases, data warehousing, and data governance. AIPMs must ensure that data infrastructures support AI initiatives and maintain data integrity throughout the product lifecycle. Understanding data structures and database technologies enables AIPMs to collaborate with data engineers and ensure that AI models have access to high-quality data.
Programming Basics for AI (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: Provides basic programming skills, typically in languages like Python, needed to understand AI implementations. AIPMs with programming knowledge can communicate effectively with developers, comprehend technical challenges, and participate in technical discussions. This understanding enhances their ability to make informed decisions about technology stacks and development timelines.
Total Credits: 30 ECTS
Semester 2 (30 ECTS)
Data Analytics and Visualization (Core)
Credits: 6 ECTS
Prerequisites: Data Management and Databases
Why it matters: Focuses on interpreting, analyzing, and visualizing data using tools like Tableau or Power BI. AIPMs use data analytics to make informed product decisions, track AI solution performance, and communicate insights effectively to stakeholders. Proficiency in data analytics enables AIPMs to identify trends, measure outcomes, and adjust strategies based on empirical evidence.
Ethics and Governance in AI (Core)
Credits: 6 ECTS
Prerequisites: Introduction to Artificial Intelligence and Machine Learning
Why it matters: Teaches integration of responsible AI practices, focusing on ethical considerations, data privacy, bias mitigation, and compliance with regulations like GDPR. AIPMs must ensure that AI solutions are ethically sound and legally compliant to protect the organization from reputational and legal risks. Understanding governance frameworks helps in establishing trust with users and stakeholders.
Agile Methodologies for Product Development (Core)
Credits: 6 ECTS
Prerequisites: Fundamentals of Product Management
Why it matters: Equips students with skills to lead teams through iterative development cycles using Agile frameworks like Scrum or Kanban. In the fast-evolving field of AI, Agile methodologies allow AIPMs to adapt to changes quickly, prioritize tasks effectively, and deliver incremental value. Mastery of Agile practices enhances team collaboration and project efficiency.
Business Fundamentals for AI Product Managers (Core)
Credits: 6 ECTS
Prerequisites: None
Why it matters: Provides an understanding of financial metrics, business strategy, market analysis, and organizational goals. AIPMs must align AI solutions with company objectives, justify investments, and demonstrate return on investment (ROI). Knowledge of business fundamentals enables AIPMs to make strategic decisions that contribute to the organization's bottom line.
Data Privacy and Security (Core)
Credits: 6 ECTS
Prerequisites: Data Management and Databases
Why it matters: Focuses on protecting data within AI systems, understanding legal requirements, and implementing security measures like encryption and access controls. AIPMs are responsible for ensuring that sensitive data is handled appropriately, preventing data breaches, and maintaining regulatory compliance. This course emphasizes the importance of building trust through robust data security practices.
Total Credits: 30 ECTS
Year 2: Intermediate Knowledge and Application
Semester 3 (30 ECTS)
Machine Learning Applications for Product Managers (Core)
Credits: 6 ECTS
Prerequisites: Introduction to AI and Machine Learning; Programming Basics for AI
Why it matters: Offers a working knowledge of machine learning algorithms, model development, and deployment processes. AIPMs learn to manage technical teams effectively, evaluate AI approaches for solving business problems, and understand considerations like overfitting, model interpretability, and performance metrics. This knowledge is essential for guiding AI projects to success and making informed decisions about technology adoption.
Data Engineering Fundamentals (Core)
Credits: 6 ECTS
Prerequisites: Data Management and Databases; Programming Basics for AI
Why it matters: Teaches the processes of designing, building, and maintaining data pipelines, including ETL (Extract, Transform, Load) processes and real-time data streaming. AIPMs must ensure that AI models receive high-quality, timely data, which is critical for accurate predictions and overall AI solution performance. Understanding data engineering enables AIPMs to collaborate effectively with data teams and address data-related challenges.
Strategic Problem Identification for AI Solutions (Core)
Credits: 6 ECTS
Prerequisites: Data Analytics and Visualization; Business Fundamentals for AI Product Managers
Why it matters: Equips AIPMs with methodologies to identify high-impact problems suitable for AI interventions. By analyzing business processes, customer feedback, and operational data, AIPMs can pinpoint areas where AI can add significant value. This course emphasizes strategic thinking, ensuring that AI initiatives align with organizational goals, address critical needs, and justify resource allocation.
Process Optimization and Workflow Design for AI Implementation (Core)
Credits: 6 ECTS
Prerequisites: Strategic Problem Identification for AI Solutions
Why it matters: Focuses on analyzing and redesigning business processes to integrate AI solutions effectively. AIPMs learn how to map existing workflows, identify inefficiencies, and design optimized processes that leverage AI capabilities. Mastery of process optimization ensures that AI implementations enhance operational efficiency, reduce costs, and facilitate user adoption by seamlessly fitting into existing workflows.
Organizational Influence and Stakeholder Engagement (Core)
Credits: 4 ECTS
Prerequisites: Organizational Structures and Operations; Agile Methodologies for Product Development
Why it matters: Provides strategies for managing complex internal relationships, gaining stakeholder buy-in, and navigating organizational politics. AIPMs must communicate effectively with executives, managers, and team members to drive AI initiatives forward. This course teaches negotiation skills, conflict resolution, and influence techniques essential for successful project implementation.
Elective Course
Credits: 2 ECTS
Prerequisites: Varies by course
Why it matters: Allows students to specialize further in areas of interest, applying their knowledge to specific industries or advanced topics. Electives enhance expertise relevant to career goals and keep students abreast of emerging trends.
Total Credits: 30 ECTS
Semester 4 (30 ECTS)
Engaging Internal Stakeholders and Domain Experts in AI Initiatives (Core)
Credits: 6 ECTS
Prerequisites: Organizational Structures and Operations; Process Optimization and Workflow Design for AI Implementation
Why it matters: Equips AIPMs with skills to effectively engage with internal stakeholders and domain experts who are the end-users or beneficiaries of AI solutions. Understanding their needs, challenges, and workflows is crucial for developing AI products that solve real business problems. This course emphasizes communication techniques, requirement gathering, and collaboration strategies to ensure AI solutions are user-centric and have high adoption rates.
Generative AI Models and Applications (Core)
Credits: 6 ECTS
Prerequisites: Machine Learning Applications for Product Managers
Why it matters: Introduces students to Generative AI models like GPT-4 and DALL·E, exploring their capabilities in content creation, language translation, and image generation. Understanding these models enables AIPMs to innovate and implement cutting-edge AI solutions that can automate tasks, enhance creativity, and provide personalized user experiences. The course also addresses ethical considerations specific to Generative AI.
AI Product Design and Prototyping (Core)
Credits: 6 ECTS
Prerequisites: Process Optimization and Workflow Design for AI Implementation; Data Engineering Fundamentals
Why it matters: Focuses on designing and prototyping AI solutions, incorporating user feedback and data considerations. AIPMs learn rapid prototyping techniques, user interface design principles, and how to validate product concepts. This course ensures that AIPMs can bring AI products from concept to a viable prototype, facilitating stakeholder buy-in and early testing.
Enterprise AI Integration and Legacy Systems (Core)
Credits: 6 ECTS
Prerequisites: AI Product Design and Prototyping; Organizational Structures and Operations
Why it matters: Addresses the challenges of integrating AI solutions with existing enterprise systems and legacy infrastructure. AIPMs must ensure seamless functionality across platforms, manage compatibility issues, and plan for system upgrades. Understanding integration strategies minimizes disruption, reduces implementation time, and ensures that AI solutions enhance rather than hinder existing operations.
Internal Politics and Change Management (Core)
Credits: 4 ECTS
Prerequisites: Organizational Influence and Stakeholder Engagement
Why it matters: Provides strategies to navigate internal politics, manage resistance, and lead organizational change effectively. Implementing AI solutions often requires shifts in processes and culture. This course equips AIPMs with skills to manage the human side of technological change, including communication planning, training programs, and fostering a culture of innovation.
Elective Course
Credits: 2 ECTS
Prerequisites: Varies by course
Why it matters: Allows students to specialize further in areas of interest, applying their knowledge to specific industries or advanced topics. Electives enhance expertise relevant to career goals and keep students abreast of emerging trends.
Total Credits: 30 ECTS
Year 3: Specialization and Industry Application
Semester 5 (30 ECTS)
Strategies for Adapting Generative AI Models and LLMs (Core)
Credits: 6 ECTS
Prerequisites: Generative AI Models and Applications
Why it matters: Focuses on customizing and deploying Generative AI models, particularly Large Language Models (LLMs), to meet specific organizational needs. AIPMs learn about fine-tuning models, handling domain-specific data, and ensuring scalability. Mastery of these strategies enables AIPMs to implement GenAI solutions effectively, driving innovation and maintaining a competitive edge.
AI in Cloud Computing and Big Data Technologies (Core)
Credits: 6 ECTS
Prerequisites: Data Engineering Fundamentals; Enterprise AI Integration and Legacy Systems
Why it matters: Provides an understanding of deploying AI solutions in cloud environments using platforms like AWS, Azure, or Google Cloud. AIPMs learn about big data technologies for handling large-scale data processing. This knowledge is crucial for leveraging cloud infrastructure to enhance scalability, performance, and cost-efficiency of AI solutions.
Data Governance and Regulatory Compliance (Core)
Credits: 6 ECTS
Prerequisites: Data Privacy and Security; Ethics and Governance in AI
Why it matters: Teaches how to establish data governance frameworks that ensure AI solutions comply with internal policies and external regulations. AIPMs learn about data lineage, metadata management, and compliance standards like GDPR and HIPAA. Effective governance protects the organization from legal risks, enhances data quality, and builds stakeholder trust.
Product Roadmap Development and Strategy (Core)
Credits: 6 ECTS
Prerequisites: AI Product Design and Prototyping; Organizational Influence and Stakeholder Engagement
Why it matters: Guides students in creating strategic product roadmaps that consider market trends, technological advancements, and organizational objectives. AIPMs learn to plan feature releases, prioritize development efforts, and align the product vision with business goals. This course ensures that AI products remain relevant, deliver sustained value, and adapt to changing market conditions.
User Adoption and Change Management in AI Implementation (Core)
Credits: 6 ECTS
Prerequisites: Internal Politics and Change Management
Why it matters: Provides techniques for encouraging employee acceptance and usage of AI solutions. AIPMs learn about training programs, user support, feedback mechanisms, and strategies to overcome resistance. Managing the human aspects of technological change is crucial for maximizing the impact of AI initiatives and ensuring long-term success.
Total Credits: 30 ECTS
Semester 6 (30 ECTS)
Capstone Project: AI Product Proposal (Core)
Credits: 12 ECTS
Prerequisites: Completion of all core courses
Why it matters: Provides hands-on experience by developing a comprehensive AI product proposal. Students integrate data strategies, Generative AI solutions, technical implementations, and business considerations into a cohesive plan. This project demonstrates readiness for real-world challenges and the ability to deliver end-to-end AI solutions, showcasing the student's skills to potential employers.
Internship or Industry Placement (Core)
Credits: 12 ECTS
Prerequisites: Completion of Year 2
Why it matters: Offers practical experience in a real-world AI product team, applying learned skills in a professional environment. The internship bridges academic learning with industry practice, enhancing employability, professional networks, and understanding of organizational dynamics.
Elective Course
Credits: 2 ECTS
Prerequisites: Varies by course
Why it matters: Allows students to specialize further in areas of interest, applying their knowledge to specific industries or advanced topics. Electives enhance expertise relevant to career goals and keep students abreast of emerging trends.
Elective Course
Credits: 2 ECTS
Prerequisites: Varies by course
Why it matters: Allows students to specialize further in areas of interest, applying their knowledge to specific industries or advanced topics. Electives enhance expertise relevant to career goals and keep students abreast of emerging trends.
Elective Course
Credits: 2 ECTS
Prerequisites: Varies by course
Why it matters: Allows students to specialize further in areas of interest, applying their knowledge to specific industries or advanced topics. Electives enhance expertise relevant to career goals and keep students abreast of emerging trends.
Total Credits: 30 ECTS
Elective Courses
Team Dynamics and Roles in AI Product Development
Prerequisites: Organizational Influence and Stakeholder Engagement
Why it matters: Explores the composition of effective AI product teams, including roles like data scientists, engineers, and UX designers. AIPMs learn how to manage team dynamics, foster collaboration, and resolve conflicts. Understanding team structure is crucial for building and leading teams that can deliver successful AI products.
Organizational Structures for AI Teams
Prerequisites: Organizational Structures and Operations
Why it matters: Describes various ways AI teams can be structured within companies, such as centralized, decentralized, or hybrid models. AIPMs learn to influence organizational design to optimize team effectiveness, resource allocation, and alignment with strategic objectives.
AI in Telecommunications
Prerequisites: Machine Learning Applications for Product Managers
Why it matters: Focuses on applying AI solutions within the telecommunications industry, addressing challenges like network optimization, customer service enhancement, and predictive maintenance. Understanding industry-specific use cases enables AIPMs to develop tailored AI products.
AI in Healthcare
Prerequisites: Machine Learning Applications for Product Managers; Data Privacy and Security
Why it matters: Addresses data considerations specific to healthcare, including patient data privacy, regulatory compliance (e.g., HIPAA), and ethical concerns. AIPMs learn to develop AI solutions that improve patient outcomes while adhering to strict standards.
AI in Manufacturing
Prerequisites: Machine Learning Applications for Product Managers
Why it matters: Explores AI solutions for manufacturing, such as predictive maintenance, quality control, and supply chain optimization. AIPMs understand how AI can enhance operational efficiency and reduce costs in a manufacturing context.
Natural Language Processing for Product Managers
Prerequisites: Machine Learning Applications for Product Managers
Why it matters: Equips students to manage NLP projects, understanding data types, preprocessing, and applications like chatbots, sentiment analysis, and document summarization. AIPMs can leverage NLP to improve internal workflows and customer interactions.
Computer Vision for Product Managers
Prerequisites: Machine Learning Applications for Product Managers
Why it matters: Introduces computer vision technologies, focusing on image and video data processing, object detection, and pattern recognition. AIPMs learn to apply computer vision in areas like quality control, surveillance, and automation.
User Experience Design for AI Products
Prerequisites: AI Product Design and Prototyping
Why it matters: Teaches how to design intuitive interfaces and user experiences for AI products, emphasizing usability, accessibility, and user satisfaction. Good UX design ensures that AI products are user-friendly and meet user needs effectively.
Ethics and Bias in Generative AI
Prerequisites: Generative AI Models and Applications
Why it matters: Addresses ethical challenges posed by Generative AI, including potential biases, deepfakes, and misinformation. AIPMs learn to implement safeguards, ensure responsible use, and maintain compliance with emerging regulations.
Applied Generative AI Projects
Prerequisites: Strategies for Adapting Generative AI Models and LLMs
Why it matters: Provides hands-on experience in developing and managing projects involving Generative AI. AIPMs apply theoretical knowledge to practical scenarios, fostering innovation and creating portfolio-worthy projects.
Program Completion Requirements
Total Credits Required: 180 ECTS
Capstone Project and Internship: Mandatory for graduation
Admission Requirement: At least a bachelor's degree in any subject
Conclusion
This Master of Science in Internal AI Product Management program offers a rigorous and comprehensive curriculum tailored to the unique needs of Internal AI Product Managers. By integrating foundational knowledge with advanced concepts—including essential data courses, organizational understanding, and the latest developments in Generative AI—the program ensures that graduates are fully prepared to lead AI-driven transformations within organizations.
The inclusion of courses like "Process Optimization and Workflow Design for AI Implementation" and "Engaging Internal Stakeholders and Domain Experts in AI Initiatives" addresses critical competencies. These courses enable students to design effective processes and collaborate with internal customers to integrate AI solutions seamlessly, ensuring that AI initiatives are both technically sound and aligned with business needs.
Elective courses allow students to tailor their education to specific interests and career goals, enhancing their expertise in areas such as team dynamics, organizational structures, and industry-specific applications. The curriculum is carefully structured to maintain a total of 30 ECTS per semester, ensuring a balanced workload and compliance with European higher education standards.
Prospective students interested in leading AI initiatives within organizations are encouraged to apply to this program, which is designed to equip them with the skills and knowledge necessary to excel in the dynamic field of Internal AI Product Management—and perhaps finally convince AI to laugh at our jokes!
Absolutely good thought because I am trying to convince my college to do elective to start with initially on Product mindset
Jaser, I love the idea of having a go-to repository of AI learnings for PMs. We are going through a complete transformation in our careers - from what we build to how we build. Just sent you a private message on Notes. If you are up for it, lets collaborate.