AI for non-coders – AI for Business Application

Understanding AI Fundamentals, Concepts & Real-world Application

Build a strong foundation in AI by exploring essential concepts, technologies, and real-world use cases. This session sets the stage for understanding how AI fits into modern business and product strategies.

• Introduction to Artificial Intelligence: What it is and why it matters
• Overview of AI Technologies: Machine Learning, Deep Learning, and Generative AI
• Understanding Large Language Models (LLMs) – e.g., ChatGPT, Claude, Gemini
• Common AI Frameworks & Tools used in product development
• Key Concepts: Training data, models, inference, evaluation, bias
• Business-Driven AI Use Cases across industries
• What makes AI products different from traditional software

 Data-driven Feasibility Validation, Market Research & Competitors Analysis

• Understand how to evaluate AI product ideas using data
• Conduct feasibility assessments to determine if AI is the right solution
• Explore frameworks for validating product-market fit for AI products
• Learn techniques for conducting market research and analyzing competitors
• Identify risks, opportunities, and gaps in the AI product landscape
• How to identify Opportunity Sizing

Project: Choose an AI product idea and evaluate its potential through a structured, data-driven process. This project will guide you through key steps used by real product teams to validate feasibility, assess market fit, and analyze competition

AI Tech Stack, Feature Design & Development Planning

• Develop a strategic roadmap for building AI-powered products
• Understand key components of the AI tech stack (data pipelines, models, APIs, etc.)
• Define core and differentiating features aligned with user needs
• Learn how to scope MVP features for AI solutions
• How to scope AI features that solve real problems
• How to create comprehensive product strategy & roadmap for AI product
• Feature Prioritization Frameworks & Mapping AI features to painpoints
• Explore collaboration between product, data, and engineering teams during development

Project: Create a Comprehensive Strategic Product Roadmap for Your AI Solution

 Agile Methodology, Epics, Sprints & Success Metrics

• Apply Agile principles to AI product development
• Learn how to create and manage epics, user stories, and acceptance criteria
• Define clear success metrics for AI features and iterations
• Plan and execute sprints effectively, tailored for AI workflows
• Navigate key Agile ceremonies: sprint planning, stand-ups, reviews, and retrospectives
• Understand how to adapt Agile to handle model experimentation, iteration, and deployment

Project: Design and manage a 1-month sprint roadmap (1 sprint per week) for your proposed AI product. Define an EPIC, Acceptance Criteria, Success metrics. Use a project management tool like Jira or Trello to organize your backlog.

How ML & Generative AI Power Chatbots and Automation

• Explore Large Language Models (LLMs) like ChatGPT, Claude, and Gemini
• Build AI product using ML & fine-tune Gen AI LLM 
• Step-by-step demo on building and integrating ML models into products
• Learn how AI chatbots and virtual assistants are built and used
• Analyze real-world case studies (e.g., GPT, Spotify AI recommendations)
• Introduction to AutoML tools and their role in product innovation

Project: Build an AI Feature Using No-Code Machine Learning Tools

From AI model to product – Bringing AI features to Life

• Building real AI products: Agentic AI to automate business processes 
• Finetuning LLM using RAG & Prompt-based engineering
• Key decisions in integrating AI into apps, platforms, or workflows
• Understanding APIs, pipelines, and real-time inference
• Infrastructure considerations: cloud tools, data storage, and scaling
• Evaluating AI product performance
• Common pitfalls and how to avoid them in AI product builds

Project: Design and Prototype an Agentic or RAG AI System

Go-to-Market Strategy, Growth Planning & Ethical AI Principles

• Building a Go-To-Market (GTM) plan for AI products
• Elements of a strong GTM strategy for AI products
• Positioning and messaging for AI-powered solutions
• Planning for user onboarding, activation, and engagement
• Elements of a strong GTM strategy for AI products
• Crafting messaging that communicates AI value clearly
• Pricing models and monetization strategies
• Launch planning: internal alignment, user readiness, feedback loops
• Scaling strategies: partnerships, channels, retention, and iteration
• Metrics that matter post-launch
• Measuring product performance and user feedback loops
• Understanding AI risks: bias, fairness, and explainability
• Principles of Responsible AI: transparency, accountability, and safety

Project: Create a clear go-to-market strategy for your AI product and evaluate it through the lens of ethical and responsible AI. You’ll define how to launch, promote, and measure success—while identifying risks related to fairness, bias, and transparency.

Presentation & QA

• Final capstone presentations
• Peer and instructor feedback
• Group reflection on learnings and key takeaways
• Final Q&A: clarify concepts, tools, and next steps
• Guidance on career paths and applying your new skills

• Present your AI product idea
• Walk through the problem, AI solution, GTM plan, and ethical considerations
• Showcase how you’d lead the product’s development and launch