Executive Programme in Product Innovation with AI & Agentic AI

• How AI Systems Produce Outputs: A Product Leader's Mental Model 

• Breaking Down AI Systems: From Data to Model to Output to UX 

• LLM Capabilities and Their Product Applications 

• Designing Around AI Limitations: Hallucination, Latency, Cost and Reliability 

• Knowledge Architecture: RAG, Semantic Search and When Each Matters 

• AI vs Rules vs No AI: The Product Decision Matrix 

• What Agentic AI Actually Means for Your Product 

• Understanding Agent Workflows: From Trigger to Reasoning to Action

• What Winning Actually Means in AI Markets: Strategy Frameworks for a Landscape Where the Technology Keeps Moving  

• Competitive Moats in AI: What Actually Creates Durable Advantage When the Model Is Not Enough 

• Data Network Effects vs Traditional Network Effects: How AI Platforms Compound Differently 

• The Margin Problem: Designing AI Business Models Where Growth Does Not Destroy Profitability 

• AI Business Models: Designing for Value Capture Without Destroying Margin or User Trust 

• Build vs Buy vs Partner: Making the Architecture Decision That Shapes Your Competitive Position 

• Platform Thinking for AI: Designing Products That Others Build On and Ecosystems That Compound 

• Go-to-Market Strategy for AI Products: Positioning, Timing and the Narrative That Creates Market Pull

• The Data-Product Relationship: Why Every AI Product Decision Is Also a Data Decision 

• Data Quality as a Product Design Constraint: Garbage In, Garbage Out at Product Scale 

• Designing Products That Generate Better Data: The Flywheel That Separates AI Leaders From Followers 

• First-Party, Second-Party and Third-Party Data: Building the Data Architecture Your AI Strategy Requires 

• Data Governance for Product Leaders: Privacy, Consent and the Regulatory Obligations That Shape What You Can Build 

• Synthetic Data and Data Augmentation: Building AI Products When Real Data Is Scarce, Sensitive or Biased 

• The Build vs Buy Decision for Data: When to Own Your Data Infrastructure and When to Rely on External Providers 

• Data Strategy on the Product Roadmap: Making Data Investment Visible, Prioritised and Accountable

• The R&D Continuum: Where AI Product Work Actually Sits 

• AI Development vs Traditional R&D: Six Structural Differences That Change Everything 

• Applied Research in AI: When and How to Invest in Novel Solutions 

• Technical Service in AI: The Fastest Path to Production Value 

• Organising the AI Product Team: Structures, Roles and the Hub-and-Spoke Model

• The Augmentation Imperative: Why the Most Important AI Product Design Decision Is How It Affects the Humans Around It 

• Mapping Human-AI Workflows: Where Intelligence Assists, Where It Decides and Where Humans Must Lead

• Role Redesign in the Age of AI: Creating New Value From Human Capability That AI Releases 

• The Organisational Psychology of AI Adoption: Fear, Identity and the Change Resistance That Data Cannot Overcome 

• Designing AI Literacy Programmes That Actually Change Behaviour: From Awareness to Capability to Habit 

• Leading Teams Through AI Uncertainty: The Communication and Psychological Safety Framework 

• Measuring Human-AI Collaboration Effectiveness: Beyond Productivity to Wellbeing, Quality and Trust 

• The Ethical Obligations of Workforce AI Transformation: What Product Leaders Owe the People Their Decisions Affect

• From Noise to Signal: Why Traditional Customer Discovery Fails at Scale and What AI Changes 

• Mapping Where Customer Truth Lives: Reviews, Tickets, Transcripts and Behavioural Signals 

• Extracting Themes at Scale: Using AI to Surface Patterns from Unstructured Data 

• Sentiment and Intent Analysis: Understanding Not Just What Customers Say But What They Mean 

• Translating Customer Signals into Product Opportunity Hypotheses

• Validating AI-Generated Insights: Where Human Judgment Remains Non-Negotiable 

• Building a Continuous VoC Pipeline: From One-Time Research to Always-On Opportunity Intelligence 

• Prioritising What You Find: Scoring and Sequencing Product Opportunities for Maximum Impact

• From Static Documents to Living User Models: Why Traditional Personas No Longer Hold 

• AI-Assisted Persona Generation: Building Richer Profiles from Mixed Research Inputs 

• Jobs-to-Be-Done Meets Behavioural Data: Understanding What Users Are Actually Trying to Accomplish 

• Behavioural Segmentation: Beyond Demographics and Firmographics 

• Identifying High-Value, At-Risk and Dormant Cohorts: Predictive Segmentation in Practice 

• The Installed Base Effect: How AI Personalisation Builds Switching Costs and Deepens Retention 

• Designing Personalisation-Ready Segments: Structuring User Intelligence for Product Action 

• Avoiding the Traps: Over-Segmentation, Noisy Insights and the Discipline of Actionable Intelligence

• Opportunity Discovery: Mapping Market Gaps, User Pain Points and Unmet Jobs Using AI Signal Analysis 

• From Ansoff to AI Opportunity Horizons: Choosing Between Incremental and Radical Bets 

• Estimating Market Size With Rigour: TAM, SAM and SOM in an AI-Shaped Market 

• From Signal to Decision: Using AI to Synthesise Research, Validate Assumptions and Commit to the Right Problem 

• Validating Problem-Solution Fit Before You Build: AI-Assisted Research as a Risk Reduction Tool 

• Prioritisation Frameworks for Product Leaders: Scoring, Ranking and Defending What Gets Built Next

• Linking Opportunities to Measurable Business Outcomes: The Bridge Between Discovery and Accountability 

• Translating Insights into Actionable Product Roadmaps: From Prioritised Opportunity to Committed Plan

• Empathy at Scale: Understanding Users Deeply Through Research, Observation and AI-Assisted Synthesis 

• From Insight to Idea: Defining the Right Problem and Generating Solutions That Actually Fit 

• Framing User Problems Where Outcomes Are Non-Deterministic 

• Mapping AI Touchpoints in End-to-End User Journeys 

• Human-AI Collaboration Patterns: Assist, Augment, Automate

• Internal Productivity vs Customer-Facing AI: Choosing Where to Apply GenAI First 

• Design Simplification Through GenAI: Doing More With Less Complexity 

• Concurrent Engineering With AI: Collapsing the Loop Between R&D and Marketing 

• Writing PRDs and User Stories With AI Copilots 

• Generating UX Copy, Onboarding Flows and FAQs 

• Automating Competitor Research and Feature Benchmarking 

• AI for Product Documentation and Knowledge Management 

• Estimating Cost vs Value of GenAI Features at Scale

• Why Prompting Is a Product Design Act: The Mental Model Every Product Leader Needs 

• Prompt Anatomy for Product Use Cases: Structure, Specificity and Constraints 

• Few-Shot Design and Output Shaping: Teaching the AI What Good Looks Like 

• Prompting for Personalisation: Designing Dynamic Instructions That Adapt to User Context 

• Chain-of-Thought and Reasoning Prompts: Designing AI Features That Think Before They Answer 

• Guardrails, Safety Prompts and Prompt Injection Defence 

• Prompt Governance: Managing, Versioning and Auditing Prompts as Product Assets

• Evaluating Prompt Performance: How Product Leaders Measure Whether Their Instructions Are Working

• Rethinking the Interface Contract: How AI Changes the Fundamental Relationship Between User, Product and System 

• The Tripartite AI Interface: Designing for End Users, Channel Members and Suppliers Simultaneously 

• The UX of Mental Models: Designing With and Against What Users Already Believe About AI 

• Designing Conversational Flows and Prompt-Driven Interactions 

• Designing for Uncertainty: UX Patterns for Non-Deterministic Outputs 

• Explainability as Interface: Making AI Reasoning Visible Without Adding Cognitive Load 

• Building Trust Through Transparency, Control and Fallback Flows 

• Testing the Untestable: Usability Methods for AI Interactions Where Outputs Vary

• Why AI Products Fail Across Cultures: The Design Assumptions That Travel Badly 

• Linguistic Diversity as a Product Design Challenge: Building AI Products That Work Across Languages and Scripts 

• Cultural Intelligence in AI Product Design: Values, Norms and the Invisible Assumptions in Your Product 

• Inclusive AI: Designing for Users Across Economic, Educational and Connectivity Divides 

• Regulatory Diversity in Global AI Deployment: Navigating Compliance Across Multiple Jurisdictions Simultaneously 

• Localisation vs Personalisation: The Distinction That Changes Everything About Global AI Product Strategy 

• Building Diverse AI Teams: Why the People Who Build the Product Shape What the Product Assumes 

• Global Launch Strategy for AI Products: Sequencing Markets, Building Local Trust and Managing Cultural Risk

• Compress Every Loop: The Philosophy of Rapid Product Development in the AI Era 

• Fidelity Strategy: Matching Prototype Depth to the Question You Are Actually Trying to Answer 

• Prototype Thinking as a Leadership Skill: Why Showing Always Beats Telling 

• Text-to-UI and AI Wireframing: From Written Description to Testable Interface in Minutes 

• Simulating AI Behaviour: Building Digital Twins of Your AI Features Before Engineering Begins

•  No-Code Builders for Functional AI Prototypes: From Concept to Clickable in a Single Session 

• Prototyping Agentic Workflows and AI Feature Interactions 

• From Prototype to MVP: Capturing What You Learned and Converting It Into Build Requirements

• Why Standard Agile Breaks Under AI Conditions: The New Uncertainties Product Teams Must Design For 

• Agile NPD for AI: Designing a Development System That Welcomes Late-Stage Change 

• The AI Product Definition of Done: Rewriting Acceptance Criteria for Non-Deterministic Features 

• AI-Assisted Sprint Planning and Backlog Grooming: Working Smarter at the Ceremony Level 

• Writing Better Tickets and Acceptance Criteria With AI: Precision as a Delivery Accelerator 

• Testing AI Features: Structured Evaluation Scenarios and the Limits of Conventional QA 

• Building AI Observability Into the Product Roadmap: Monitoring as a Shipped Feature 

• Balancing Speed and Reliability: The AI Product Release Framework for Confident, Sustainable Shipping

• The Agent Trust Spectrum: Calibrating Autonomy as a Deliberate Product Design Decision 

• Designing Human-in-the-Loop Checkpoints: Where Oversight Must Be Built In, Not Bolted On

• Automating Complex Research and Cross-Team Workflows With Agents 

• Common Agent Failure Modes and How Product Leaders Prevent Them 

• Agent ROI: Building the Business Case for Autonomous Systems Beyond Cost Reduction 

• Governing Agentic Systems: Accountability, Auditability and the Product Leader's Ongoing Responsibility

• From Vanity to Value: Building the Right Metric Stack for AI Products 

• Behavioural Analytics and the AI Feature Funnel: Seeing How Users Actually Interact With Intelligence 

• Goodhart's Law and Metric Manipulation: When Optimising for the Measure Destroys the Value 

• Designing A/B Tests for AI Features: Where Standard Experimentation Methodology Needs to Evolve 

• Causal Inference for Product Leaders: Moving From Correlation to Decisions You Can Defend 

• Using AI to Generate and Interpret Product Insights: Augmenting Analytical Judgment at Scale 

• Experimentation for Agentic Features: Testing Systems That Act, Not Just Systems That Display 

• Post-Launch Review as a Learning System: Using AI to Validate or Reject the Assumptions You Made in the Fuzzy Front End

• Why Personalisation Is Now a Product Necessity, Not a Premium Feature: The Competitive and Behavioural Case

•  From Rules to AI: Understanding How Recommendation Systems Actually Learn and Decide 

• The Cold Start Problem: Designing Personalisation That Works Before You Know Anything About the User 

• Designing Onboarding and Lifecycle Personalisation Flows: From First Impression to Deep Habituation 

• Contextual Personalisation: Designing Experiences That Respond to the Moment, Not Just the History

• Building Growth Loops Using AI-Triggered Actions: Designing Compounding Retention Mechanisms 

• Balancing Personalisation With Privacy, Autonomy and Trust: The Ethics Product Leaders Must Own 

• Measuring Personalisation: Retention, Diversity, the Engagement Trap and the Filter Bubble Risk

• Connecting the Dots: How Every Product Decision in This Curriculum Is a Responsible AI Decision 

• The Regulatory Imperative: India's AI Policy Framework, the EU AI Act and What Product Leaders Must Build For Now 

• Institutional Accountability: Designing the Organisational Structures That Make Responsible AI Self-Sustaining 

• Leading the Transformation: Driving AI Adoption Across an Organisation Without Leaving Responsibility Behind

• Build an end-to-end AI-powered product across the programme.

• Apply concepts from every module to progressively develop your capstone.

• Enhance your solution each week as you learn new AI product-building capabilities.

• Graduate with a portfolio-ready AI product that showcases your end-to-end product-building skills.

Traditional product management courses focus on roadmaps, requirements, and feature planning. The Executive Programme in Product Innovation with AI & Agentic AI by IIM Kozhikode focuses on building — prototyping AI features, designing agentic workflows, and shipping AI-powered products end-to-end. The curriculum includes prompt engineering, no-code AI prototyping, Agile for AI, and an AI product capstone project, using 20 tools including Figma, ChatGPT, n8n, Amplitude, and Hugging Face.

No. The Executive Programme in Product Innovation with AI & Agentic AI requires no coding or prior technical background. The programme is designed for product managers, UX designers, business professionals, and aspiring product leaders who want to build AI-first products using no-code tools, GenAI platforms, and agentic workflow builders — without writing a single line of code.

Agentic AI refers to AI systems that can reason, plan, and autonomously execute multi-step tasks without constant human input. As of 2025, 51% of AI product teams are already building agentic products (Figma AI Report, 2025). The IIM Kozhikode Product Innovation with AI & Agentic AI programme covers agentic system design, human-in-the-loop checkpoints, autonomous workflow building, and agentic governance — core skills for product managers building the next generation of AI products.

The programme includes 4+ hands-on AI product mini projects: an AI Product Opportunity Sprint, a Voice of Customer (VoC) Insight Engine, a No-Code Product Simulation, an AI Experimentation & Metrics Plan, an Agentic Workflow Builder, and an end-to-end AI Product Capstone. Each project maps to a stage of the AI product lifecycle — from discovery and prototyping to deployment and automation — and together form a portfolio demonstrating AI-first product design and building capabilities.

The programme is designed for product managers and product owners, UX and product designers, engineering managers and AI practitioners, founders and entrepreneurs, and professionals from consulting, marketing, or operations transitioning into product roles. It is built for anyone who needs to innovate, design, build, or lead AI-powered products — no coding experience required.

The IIM Kozhikode Product Innovation with AI & Agentic AI programme includes a dedicated module on prompt engineering as a product design discipline. It covers prompt anatomy, few-shot design, chain-of-thought prompts, guardrails, safety prompts, and prompt governance. Applied topics include writing PRDs with AI copilots, generating UX copy and onboarding flows, automating competitor research, and evaluating prompt performance — practical GenAI skills for product managers in 2026.

Upon successful completion of the Product Innovation with AI and Agentic AI programme and achieving a minimum score of 70%, participants will receive a prestigious digital certificate from IIM Kozhikode. This credential can be showcased on your resume, LinkedIn profile, and professional portfolio as evidence of your ability to design, build, and scale AI-powered products using Generative AI, Agentic AI, and modern product-building approaches.

Yes. The IIM Kozhikode Product Innovation with AI and Agentic AI programme includes dedicated modules on AI product strategy and competitive moats, data as a product asset, evidence-led product leadership, experimentation and A/B testing for AI features, personalisation at scale, and responsible AI governance. The governance module covers India's AI policy framework, the EU AI Act, and how to build accountability structures into AI product teams.

The Executive Programme in Product Innovation with AI & Agentic AI by IIM Kozhikode is 20 weeks long, delivered fully online with live masterclasses. The programme fee is INR 1,55,000 + Applicable Taxes. The next cohort starts on 23 September 2026. Eligibility requires a Bachelor's Degree or Diploma (10+2+3). Weekly time commitment is 4–5 hours. An optional one-day on-campus networking event at IIM Kozhikode campus is available at an additional cost.