AI Driven Product Innovation #254013

Upon completion of this course, participants will be able to:

• Master AI Product Thinking: Define the unique principles of AI product management and identify opportunities where AI can create transformative value, not just incremental improvement.

• Ideate and Conceptualize: Apply structured brainstorming techniques to generate viable AI-powered product ideas and concepts that solve real market needs.

• Navigate the AI Tech Landscape: Understand the capabilities and limitations of key AI technologies (e.g., LLMs, computer vision, predictive analytics) to make informed architectural and feasibility decisions.

• Validate and Prototype Rapidly: Leverage low-code tools and prompt engineering to create functional prototypes and MVPs to test assumptions and gather user feedback quickly.

• Design Human-AI Interactions: Design intuitive and trustworthy user experiences that effectively manage AI uncertainties like confidence thresholds and model errors.

• Build a Business Case: Develop a compelling business model, define key metrics for success, and articulate the ROI of an AI product initiative to stakeholders.

This course is designed for a cross-functional audience involved in creating and launching new products and services:

• Product Managers & Product Owners: Professionals responsible for product strategy, roadmaps, and feature definition.

• Product Designers & UX Researchers: Designers and researchers focused on user experience, interaction design, and usability testing for AI features.

• Innovation Managers & Strategists: Corporate strategists, R&D leads, and heads of innovation driving new ventures.

• Tech Entrepreneurs & Founders: Startup founders and leaders looking to build an AI-first company or integrate AI into their existing product.

• Software Engineers & Developers: Developers who want to better understand the product and business context for the AI systems they build.

• Digital Marketing & Growth Specialists: Professionals who will be responsible for launching and scaling AI-powered products.

• Business Leaders & Executives: CEOs, CTOs, and VPs who need to understand how AI can impact their product portfolio and competitive advantage.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI Product Thinking

• AM: The New Paradigm of Innovation

  • Beyond Hype: How AI fundamentally changes product possibilities.

  • AI Product Thinking vs. Traditional Product Management.

  • Case Studies: Deconstructing successful AI-powered products (e.g., ChatGPT, Midjourney, Grammarly, Netflix).

• PM: The AI Product Landscape

  • Key AI Capabilities for Product People: LLMs/GenAI, Computer Vision, Predictive Analytics, Recommendation Systems, NLP.

  • Understanding the Tech Stack: APIs vs. custom models, data requirements, and computational costs.

  • Workshop: “AI Capability Brainstorming” – Matching AI technologies to user problems.

Day 2: Ideation and Problem Definition

• AM: Finding the Right Problem

  • Techniques for identifying high-impact AI opportunities.

  • Jobs-To-Be-Done (JTBD) and pain point analysis in an AI context.

  • Assessing Feasibility, Desirability, and Viability of AI ideas.

• PM: Rapid Prototyping and Validation

  • The Role of Prompt Engineering as a Prototyping Tool.

  • Using no-code/low-code platforms (e.g., Bubble, Voiceflow) to mock AI interactions.

  • Workshop: Build a functional conversational prototype for a product idea in 60 minutes.

Day 3: Designing the Human-AI Experience

• AM: Principles of Human-AI Interaction

  • Designing for Trust, Transparency, and Control.

  • Managing Expectations: Handling model confidence, errors, and hallucinations gracefully.

  • The concept of “Progressive Disclosure” in AI features.

• PM: UX for AI Products

  • Creating effective user prompts and interfaces for generative AI.

  • Designing feedback loops for continuous learning (from users and from data).

  • Case Study: Critiquing the UX of popular AI applications.

Day 4: Building the AI Product

• AM: The AI Product Development Lifecycle

  • Agile for AI: Adapting sprints for model training and data collection.

  • Defining AI-Specific Requirements: Accuracy, latency, data privacy, ethical constraints.

  • Key Roles on an AI Product Team (Data Scientist, ML Engineer, AI PM).

• PM: Metrics and Measurement

  • Beyond Traditional Metrics: Defining success for AI features (e.g., engagement depth, automation rate, model accuracy, user satisfaction).

  • A/B Testing AI Models and Dealing with Statistical Uncertainty.

  • Workshop: Defining the key metrics for a sample AI product.

Day 5: Strategy, Launch, and Scaling

• AM: Business Strategy and Ethics

  • Monetization Models for AI Products: Subscription, usage-based, API licensing.

  • Cost Management: Understanding and predicting inference costs.

  • Pre-Launch Checklist: Ethical reviews, bias audits, and compliance checks.

• PM: Capstone: The Product Pitch

  • Capstone Project: Teams work through the entire process for a new product idea: ideation, feasibility, prototyping, UX mockup, and metrics.

  • Final presentations: Teams pitch their AI product to a panel of “investors” (instructors and peers).

  • Course Wrap-Up: Building a Culture of AI Innovation and Next Steps.