Course Overview
Agentic AI workflows have fundamentally changed how organisations build their next-generation products. Business leaders who understand how to harness AI — not just conceptually, but operationally — will define the next decade of productivity, service quality, and competitive advantage.
This workshop equips executives and managers with the knowledge and practical skills to gather user requirements, create product specifications, analyse complex data, and build prototypes using AI tools.
Learning Objectives
AI Landscape & Fundamentals
Understand the AI landscape, LLM fundamentals, and how AI models and harnesses work together.
AI-Augmented Product Discovery
Apply AI-augmented product discovery: user interviews, PRD writing, and data analysis.
Prototyping with AI
Build low-fidelity and dynamic prototypes with AI tools and iterate on real user feedback.
Governance, Security & Pricing
Navigate governance, security, and pricing considerations for responsible AI adoption.
Course Outline
Introduction & Approaches
- Foundational knowledge of LLMs — key concepts and prompting strategies
- Difference between AI model and harness: how do they work together?
Product Discovery
- Product management best practices for user interviews, PRD writing, data analysis, and prototyping
- How Agentic AI workflows can augment product discovery and development: creating your first custom agent
Creating a Low-fidelity Prototype
- General guidelines for creating a low-fidelity prototype
- Using an Agentic AI workflow to create a low-fidelity prototype and iterate on it based on user feedback
Understanding Data with AI
- Creating sample data files for your product discovery and development
- Using AI to understand and analyse data: from data cleaning to insights
Creating a Dynamic Prototype with AI
- Dynamic prototype versus real product: how to have clear boundaries?
- General guidelines for creating a dynamic prototype
- Claude Design, v0, bolt.new: benchmark of available tools for creating a dynamic prototype with AI
- Using an Agentic AI workflow to create a dynamic prototype and iterate on it based on user feedback
User Testing and Feedback
- How to get feedback from users on your prototype
- Role play: giving feedback and receiving feedback on a prototype
Governance and Guardrails
- How vibe coding differs from production-ready development, and why developers are enhanced, not replaced, by Agentic AI workflows
- Key considerations for secure and responsible AI development and deployment
Pricing and the AI Coding Bill
- Why AI vendors are changing their pricing models and what is really driving the surge in AI development costs
- Practical ways to reduce costs without slowing developers down
Certificate Obtained and Conferred by
Certificate of Completion from Zenika
Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Zenika.
Your Trainer

Jolyn Chuah
Jolyn works in product management with a strong technical foundation in data analytics. She brings together business acumen, stakeholder management and systems thinking to help teams translate complexity into clearer product and delivery decisions.
- Experience across corporate real estate, insurance, banking, finance & risk and government agencies
- Strong in stakeholder alignment, product framing and data-informed decision making
- Helps teams connect user needs, business goals and delivery realities
What to Prepare
A short technical setup is required before the session. Detailed instructions will be sent to confirmed participants in advance — no preparation is needed at the point of registration.
Fees
| Participant | Standard Fee | Special Rate |
|---|---|---|
| Per participant (per pax) | $1,250 | $900 |

