Certified AI Product Manager (AIPM)

Certified AI Product Manager (AIPM)

You are looking at the first and only program in Spanish designed to teach you how to manage AI-based products from end to end. Unlike a tool-focused course, AIPM prepares you to make better product decisions when artificial intelligence forms a central part of the solution.

44 hours
En Español

Nov 3 - Dec 22

USD 1000USD 800
Early bird price until Oct 27

From USD 295 for Labs subscriptions

Enroll Now

You are looking at the first and only program in Spanish designed to teach you how to manage AI-based products from end to end.


You will learn to identify real opportunities, define hypotheses, prototype solutions, validate their viability, coordinate cross-functional teams, and operate AI-based products in production.


Unlike a tool-focused course, AIPM prepares you to make better product decisions when artificial intelligence forms a central part of the solution.

It’s a whole new game now

Artificial intelligence isn’t simply another technology to add to the roadmap. When AI becomes part of a product, the questions teams need to answer change: where it makes sense to use it, what experiences to design around its capabilities, how to validate a solution whose behavior isn’t entirely predictable, and how to determine whether it is truly creating value.

This program is designed for experienced product professionals who need to develop the judgment required to work with this new generation of products.

Throughout the program, you’ll learn how to identify real opportunities to apply AI, assess their feasibility, ideate experiences that leverage its capabilities, prototype solutions, and validate their behavior before investing in development. You’ll work through real product decisions, connecting user needs with technological possibilities.

You’ll also understand what makes AI-powered products different: their dependence on data and context, the probabilistic nature of their outputs, the need for iterative experimentation, and the importance of evaluating and monitoring their behavior beyond launch.

You’ll explore concepts and techniques not to turn you into a technical specialist, but to help you make better product decisions and collaborate more effectively with technology, data, and AI teams.

Throughout the program, you’ll put these concepts into practice through an applied personal project, giving you the opportunity to learn by doing, test what you’re learning, and work through questions as they arise. You’ll integrate artificial intelligence into your project using LLMs through Claude Console or the OpenAI Platform.

To complete the hands-on work, you’ll need approximately USD 20 in credits on Claude Console or the OpenAI Platform and an estimated 3–4 hours per week to work on your project.

This program requires prior Product Management knowledge and/or experience working in product. If you have questions about this requirement, please contact us.

Tools

We will use the following tools

Claude Console
OpenAI Platform

What will I achieve?

You’ll understand what changes when artificial intelligence becomes part of the product: You will understand the key differences between Machine Learning, Deep Learning, and Generative AI, and how to strategically use them to create innovative products.

You will identify real opportunities for AI-powered products: You will learn to detect problems where AI adds value, assess their feasibility, and design solutions aligned with business and user objectives.

You will understand how to select the best AI technology for your product: You will discover how to choose between pre-trained models, custom solutions, or accessible options, ensuring a balance between impact, cost, and technical feasibility.

You will learn to lead interdisciplinary teams in AI projects: You will develop the skills to collaborate with data scientists, engineers, and stakeholders, translating business strategy into effective technical decisions.

You will learn to prototype, test, and refine AI solutions: You will explore advanced techniques to quickly design and validate AI-powered products, optimizing their performance before production implementation.

You will understand the key aspects of managing AI products in production: You will learn to define technical and business metrics, monitor models in production, and mitigate issues like data drift to maximize your product's value.

You will know how to measure and optimize AI product performance: You will be prepared to identify and address ethical and legal challenges, ensuring your products meet necessary privacy and transparency standards.

You will learn to improve the accuracy and reliability of generative models with Retrieval-Augmented Generation (RAG): You will know how to optimize generative AI models using advanced RAG techniques to make them more efficient, precise, and aligned with user context.

Curriculum

1

Kick-Off

This first module is designed to welcome you, set clear expectations for the program, and explain the course logistics. As soon as your participation in the program is confirmed, you will have access to this material to familiarize yourself with the available resources. Additionally, you will get acquainted with the essential fundamentals of AI, including key concepts, types of AI, and use cases across various industries. You will explore the strategic role of the Product Manager in AI projects and how this knowledge transforms technology product management. Also, in this module, you will discover how AI products differ from traditional digital products. You will learn about their unique lifecycle, including problem definition, training, experimentation, iterative development, and data dependency. You will improve your communication skills in AI.
2

Strategic Direction of AI Products

You will learn to strategically identify and validate an AI feature aligned with business objectives. You will explore how to prioritize AI opportunities by connecting them with the product’s vision and mission, maximizing their market impact.
3

AI Product Discovery

You will discover how to strategically select and evaluate AI solutions. You will learn how to communicate an effective PRD for AI products, define initial experiments, and align technical efforts with business objectives to maximize results and minimize risks.
4

AI Product Decision and Prototyping

You will explore advanced iterative prototyping techniques with AI and how to leverage models like LLMs for rapid testing and validating innovative ideas. You will learn to transform hypotheses into concrete solutions through functional prototypes.

* We will use no-code tools, so you don't need to have programming knowledge.
5

AI Product Delivery (Part 1)

You will explore the origins and capabilities of base models—the output of a large language model’s initial training—and understand why they are prone to hallucinations. You will analyze in-context learning and learn how to transform a base model into a useful assistant through post-training strategies such as advanced prompting, hallucination mitigation, and targeted fine-tuning. By the end, you will be able to assess when each approach is appropriate to boost the accuracy, robustness, and relevance of your AI systems.

* We will use no-code tools, so you don't need to have programming knowledge.
6

AI Product Delivery (Part 2)

You will dive into building user-centric conversational chatbots, focusing on equipping them with memory to sustain coherent, personalized interactions. You will uncover Retrieval-Augmented Generation (RAG) workflows that blend knowledge-base queries with language generation, and learn to design architectures that integrate memory and RAG to deliver context-rich, precise, and reliable responses.

* We will use no-code tools, so you don't need to have programming knowledge.
7

AI Product Observability

You will discover how to define relevant metrics for AI products, manage continuous monitoring, and handle issues like data drift. You will learn how to ensure that products deliver sustained value over the long term in dynamic contexts.
8

Vibe Coding & UI Integration

You will learn how to integrate your AI solutions with user interfaces, bringing to life functional prototypes that you can showcase and iterate upon to foster better understanding with development teams.

* We will use no-code tools, so no programming or prior coding knowledge is required.
9

Introduction to AI Agents

You will learn the basic concepts of AI agents, including their architecture and practical use cases in real products.

Competencies

Throughout the program you will develop the following competencies

1. Fundamentals

  • 1.1.Distinguish the main types of AI (Machine Learning, Deep Learning, Generative AI, Autonomous Agents, among others) and their applications.
  • 1.2.Understanding the Strategic Role of the Product Manager in AI Initiatives
  • 1.3.Recognizing the Unique Lifecycle of AI Products (Training, Experimentation, Data Dependency)
  • 1.4.Adapting Product Management Processes to the Specific Characteristics of AI
  • 1.5.Effectively Communicating Technical Implications and Continuous Iteration to Stakeholders

2. Product Direction

  • 2.1.Identifying AI-Based Business Opportunities by Connecting Real Needs and Organizational Objectives
  • 2.2.Identifying AI-Based Business Opportunities by Connecting Real Needs and Organizational Objectives
  • 2.3.Balancing Business Value Capture with Solving Relevant User Problems
  • 2.4.Communicating and Aligning AI Product Strategy Across Teams and Stakeholders

3. Product Discovery

  • 3.1.Defining and Refining Business and User Hypotheses to Ensure Opportunity Alignment with Real Needs
  • 3.2.Designing and Executing Early Validation Experiments (Lightweight PoCs, Quick Tests) to Confirm AI Utility and Feasibility with Minimal Investment
  • 3.3.Evaluating Data Criteria and Initial Metrics to Determine Solution Relevance and Potential Impact
  • 3.4.Collaborating with Technical and Design Teams to Rapidly Explore Idea Feasibility and Iteratively Adjust Hypotheses
  • 3.5.Selecting and Prioritizing AI Solutions Based on Impact, Risks, and Cost-Benefit Analysis
  • 3.6.Developing Functional Prototypes to Demonstrate Technical Feasibility and Business Value
  • 3.7.Validate Assumptions and Gather Early Feedback from Users and Stakeholders to Refine the Proposal
  • 3.8.Iterate Quickly to Optimize Prototypes, Incorporating Continuous Learnings to Prepare the Product for Later Stages

4. Product Delivery

  • 4.1.Plan the Training and Evolution of Models, from Prototypes to Production Environments
  • 4.2.Identify Technical and Operational Requirements to Scale AI Solutions
  • 4.3.Align Backlog and Roadmap to Integrate AI Features into the Core Product
  • 4.4.Define Strategies to Mitigate Technical Risks (Performance, Costs, Availability)
  • 4.5.Implement Advanced Prompt Engineering and Testing Practices for Large Language Models (LLMs)
  • 4.6.Design and Manage the Data Pipeline for RAG (Retrieval-Augmented Generation), Ensuring Relevance, Efficiency, and Ethical/Legal Compliance
  • 4.7.Manage the Transition to Production and Coordinate Internal/External Adoption of New Capabilities
  • 4.8.Collaborate with Development, Data, and DevOps Teams to Streamline Workflow

5. Observability and Continuous Monitoring

  • 5.1.Define Key Metrics (User Behaviour, Business and Technical) to Monitor AI Performance
  • 5.2.Detect and Manage Issues Like Data Drift, Model Degradation, and Prediction Errors
  • 5.3.Ensure Sustained User Value Delivery Through Iterative Model Improvements (Re-Training, Prompt Refinement, Dataset Updates, etc.)
  • 5.4.Coordinate the Communication of AI Solutions’ Results and Impact to Stakeholders

Frequently Asked Questions

Everything you need to know about this course

What our graduates say

"It’s a great entry point into the AI world to understand how the role can evolve and where to add value."

Sofia Valperga

"It helps you understand the general concepts of AI from a product perspective in a very didactic way. Highly recommended—it frees your mind so you can dive deeper into learning."

RUBEN STANLEY MORAN MEJIA

RUBEN STANLEY MORAN MEJIA

"The course is very comprehensive and designed for people who need to incorporate AI skills or knowledge into their day-to-day work as Product Managers. "

Giada Gentili

"If you’re interested in applying artificial intelligence in a practical and responsible way, this course is ideal. It not only teaches you how to design solutions with LLMs, but also gives you concrete tools to evaluate, improve, and monitor models in production. It’s very hands-on, and everything you learn can be applied directly to real projects."

Juan Pablo Cordeiro

Juan Pablo Cordeiro

"It provides a solid foundation for the conversations you need to have when planning projects that will use AI. I also find the guidance on moving forward with rapid prototyping very useful. "

Juan Martin Torrecilla

"Excellent. I’ve been looking for AI training focused on product for a while—something that wasn’t purely theoretical—and I think this course, along with the ones that follow, is exactly what I needed."

Diana Saimovici

Your Instructor

Martin Alaimo

Martin Alaimo

Since 2009, he has worked with more than 200 organizations and supported over 8,000 professionals in their career development journeys.

His approach is situational and hands-on, delivering immersive learning through innovative experiences that enable practical, immediately applicable outcomes—especially in areas often overlooked by traditional academia.

He has spoken at more than 30 conferences across the United States and 14 countries in Latin America and Europe, and is the author of six books on product and digital innovation.

His most recent book, AI Strategy Workshop, provides tools to move beyond the “feature factory” mindset and integrate artificial intelligence with strategic intent and real business impact.

He is the founder of Verica, a platform for running evaluations (Evals) on LLM-based products, designed for organizations that need to measure the quality of the outputs generated by their AI products.

As part of his commitment to innovation, he is an organizing member of Product Tank, the world’s largest Product community.

He is one of the few experts to hold the highest-level certifications in Agile practices: Certified Scrum Trainer (CST), Certified Enterprise Coach (CEC), Certified Team Coach (CTC), Certified Agile Leadership Educator (CAL Educator), and Path to CSP Educator.

Visit his complete professional profile and thought leadership activities on LinkedIn.

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