AI Essentials for Product Managers

 Description: This module provides a foundational understanding of Artificial Intelligence (AI) and its relevance to product management. Through a mix of theory and basic demonstrations, participants will learn about key AI concepts like natural language processing, behavioural analytics, and data integration. The module introduces the essential AI tools that will be covered throughout the course - ChatGPT, Amplitude, MonkeyLearn, and Airtable - with simple demonstrations of their basic capabilities. By the end of this module, participants will understand the potential of AI in product management and be prepared for the hands-on learning in subsequent modules .

Average Completion Time Estimate: 3 hours

Learning Objectives:

• Define AI and its core concepts in the context of product management.
• Identify key AI technologies and their potential applications in product development.
• Understand the basic capabilities of essential AI tools used in product management.
• Recognize how different AI tools can work together in the product management workflow.

Tool Featured:

• ChatGPT: Demonstration on using ChatGPT for content generation and competitor analysis.
• Amplitude: For user behavior analysis and data-driven insights.
• MonkeyLearn: For sentiment analysis and text processing.
• Airtable: For organizing and integrating data from various AI tools .

  Key Takeaways:

• Understanding of fundamental AI concepts in product management.
• Basic familiarity with key AI tools.
• Ability to identify which tools to use for different tasks.
• Foundation for more advanced tool usage in later modules.

 Description:  This module teaches product managers how to use AI tools to automate routine tasks and streamline their daily workflows. Through hands-on exercises with Amplitude and ClickUp, participants will learn how to automate user behavior tracking, implement predictive analytics, and manage project workflows efficiently. The module demonstrates how AI automation can free up valuable time for strategic work, with real examples of how companies have successfully reduced their manual workload through these tools.  

Average Completion Time Estimate: 2 hours and 30 minutes

Learning Objectives:

• Understand how AI can reduce manual tasks in product management.
• Implement AI-powered automation tools to enhance productivity.
• Utilize project management tools to track tasks and streamline workflows.

Tools Featured:

• Amplitude: Tracking user behavior and predictive analytics.
• ClickUp: Automating task assignments and tracking team workflows.

Key Takeaways:

• Steps to integrate Amplitude and ClickUp into daily workflows.
• How automation frees up time for strategic initiative.

Description:  This module focuses on leveraging AI tools to collect, analyze and visualize data for more informed decision-making. Through hands-on practice with Obviously AI, Heap, Google Analytics, and Tableau, product managers will learn to implement user tracking, create predictive models, and build data visualizations. The module emphasizes practical data integration and analysis techniques that enable product managers to extract actionable insights and make data-driven decisions .

Average Completion Time Estimate: 3 hours

Learning Objectives:

• Implement AI-powered data collection and integration strategies.
• Apply AI techniques for advanced data analysis and visualization.
• Implement user behavior tracking and data visualization techniques for enhanced decision-making. 

Tools Featured:

• Obviously AI: No-code predictive model creation.
• Heap: User behavior tracking and event tracking.
• Google Analytics: User tracking and data analytics.
• Tableau: Data visualization for reporting.

Key Takeaways:

• How to centralize and analyze data using AI tools.
• Making real-time, data-driven decisions to align products with user needs.

 Description:  This module teaches product managers how to create personalized user experiences using data-driven AI tools. Using Segment for user behavior-based segmentation, VWO and Optimizely for A/B testing, and Hotjar for visual behavior analysis, participants will learn how to identify user patterns and create targeted experiences. Through practical exercises in segmentation, A/B testing, and behavior analysis, product managers will learn to make informed decisions about personalization that drive user engagement and retention.

Average Completion Time Estimate: 3 hours and 15 minutes  

Learning Objectives:

• Implement AI-driven user segmentation strategies.
• Design personalized user journeys using AI insights from tools like Hotjar.
• Develop AI-powered recommendation systems.
• Analyze user behavior and refine user journeys based on real-time interactions. 

Tools Featured:

• Segment: Creating user segments based on behavior data.
• VWO & Optimizely: Running A/B tests for personalized user experiences.
• Hotjar: Analyzing heatmaps and user behavior flows.

 Key Takeaways:

• Steps to segment users and personalize their experiences using AI tools.
• How Hotjar can be used to visualize and analyze user behavior, driving engagement and retention.
• The role of A/B testing in refining personalized user journeys.

 Description:  This module focuses on using Amplitude's predictive analytics capabilities to forecast user behavior and product trends, enabling proactive decision-making. Product managers will learn how to leverage Amplitude's behavioral insights to predict user actions, identify trends, and implement data-driven strategies before problems arise. The module emphasizes practical applications of predictive analytics that can directly impact product success and user retention .

Average Completion Time Estimate: 2 hours and 45 minutes

Learning Objectives:

• Understand the role of predictive analytics in product management.
• Utilize AI tools for forecasting user behavior and product trends.
• Implement feedback loops for continuous product improvement.

Tools Featured:

• Amplitude: Predictive analytics for tracking user behavior.

Key Takeaways:

• How to use predictive analytics to inform product strategies.
• Creating feedback loops to continuously improve products based on user behavior.

Description: This module teaches product managers how to leverage AI tools to create a comprehensive customer feedback and support system. Using Lookback for in-depth user testing, Canny.io for feature request management, Intercom's Resolution Bot for automated support, and MonkeyLearn for sentiment analysis, participants will learn to collect, analyze, and act on customer insights systematically. Through practical exercises, product managers will set up automated support workflows while ensuring they capture and understand valuable customer feedback for product decisions .

Learning Objectives:

• Perform in-depth user testing and feedback analysis using AI-driven tools.
• Automate customer support processes with AI-powered solutions.
• Leverage AI tools to collect and analyze user feedback to refine product features.

Tools Featured:

• Lookback: User testing and feedback analysis.
• Canny.io: Collecting and prioritizing feature requests.
• Intercom’s Resolution Bot: Automating customer support responses.
• MonkeyLearn: Sentiment analysis for user feedback.

Key Takeaways:

• How to build an integrated customer feedback system.
• Strategies for balancing automated support with valuable customer insights.
• Techniques for turning customer feedback into actionable product decisions.