Data and AI Fundamentals: From Strategy to Functional Prototypes

Program Description

This two-day workshop equips non-technical executives with a foundational yet high-impact understanding of the modern AI ecosystem. Participants will move from basic terminology to building functional, no-code AI workflows that solve real-world business challenges across Manufacturing, Banking, Retail, and Operations.
The program bridges the gap between complex data science and executive execution by focusing on a Hybrid AI approach - leveraging Traditional AI (ML/DL) for predictive precision and Generative AI for creative scale.
This strategic program culminates in a "No-Code Pilot" roadmap designed to accelerate go-to-market speed and strengthen the organization’s competitive edge through data-backed insights.

While this outline serves as a foundational framework with use cases from multiple industries and functions, the final program is fully customized to your industry and internal workflows.

Participants work on real-world problems, not generic examples. We engage in a pre-workshop alignment to inject your specific organizational datasets, pain points, and proprietary use cases directly into the curriculum.

Learning Objectives

Master AI Fundamentals & Prompt Engineering: Identify high-value use cases and use structured prompt patterns to generate tailored business assets and responses.
Differentiate Predictive vs. Generative AI: Understand when to use Machine Learning (ML) for forecasting and Generative AI for content creation.
Build No-Code Predictive Models: Execute basic sales projection and demand forecasting simulations without writing a single line of code.

Program Details

Content

Day 1: The Hybrid AI Landscape & Prompt Mastery

Module 1: AI Fundamentals & The "Engine" Under the Hood

A non-technical breakdown of Traditional AI (Patterns/Predictions) vs. Generative AI (Creation), focusing on realistic expectations for brands.
Scenario (Banking): A leadership team evaluates using Deep Learning (DL) to detect fraud patterns in real-time while using GenAI to draft personalized customer remediation emails.
Hands-on: Practice “Structured Prompting” (Role + Task + Context + Constraints) to turn AI into a strategic thinking partner for market research.
Expected Impact: Immediate improvement in output quality through better prompting; foundation for safe AI usage.

Module 2: GenAI for Marketing Strategy & Consumer Personas

Overview of how LLMs support campaign ideation, themes, and competitor analysis specifically for pharmacy and retail channels.
Demo: Turn a brand brief (e.g., new supplement launch) into audience personas and a SWOT analysis using no-code tools.
Hands-on: Input a mock brand brief → AI generates campaign angles, taglines, and key messages adapted to regulatory and brand tone constraints.
Expected Impact: Campaign proposal preparation in half the time; more strategic, data-backed campaign angles.

Module 3: AI for Presentation & Executive Deck Preparation

Using GenAI to structure proposal decks, campaign recaps, and HQ submission storylines in half the time.
Scenario: Input campaign results and an email brief → AI generates a slide outline, titles, and talking points for a retailer presentation.
Hands-on: Create a 6–8 slide outline for a retailer campaign proposal, including objectives, insights, and activation ideas.
Expected Impact: 30–50% reduction in time spent preparing decks; better-structured, more polished executive presentations.

Module 4: AI for Customer Experience & Multilingual CRM

Summarize repetitive customer questions and generate response templates while maintaining brand-safe tone.
Scenario: Input WhatsApp/email samples → AI drafts multilingual responses (EN/BM/Chinese) with “professional” or “warm” tones.
Hands-on: Build a reusable library of response prompts for CS teams, ready to plug into WhatsApp or CRM workflows.
Expected Impact: Faster response time; more consistent brand tone; reduced CS fatigue and drafting time.

Day 2: Data, Predictive Modeling & Governance

Module 5: Traditional AI – No-Code Predictive Analytics

Introduction to Machine Learning (ML) for non-coders, focusing on how to use historical data for demand forecasting and OOS (Out-of-Stock) prediction.
Scenario (Manufacturing/Supply Chain): Input simplified performance tables → Use a no-code ML tool to identify patterns and predict volume spikes for the next quarter.
Hands-on: Execute a “What-If” ROI simulation – comparing the potential uplift of different media and trade budget allocations.
Expected Impact: Data-backed sales and ROI projections for executive-level decision-making; improved resource allocation.

Module 6: Creative Production – Images & Video Support

Leverage AI image and video tools to create moodboards, lifestyle mockups, and pre-shoot concepts without a design team.
Demo: Turn a text concept (e.g., “family wellness at home”) into visual moodboards and storyboard ideas.
Hands-on: Generate visual concepts for a POSM (Point-of-Sale Material) campaign and produce simple lifestyle mockups using no-code creative tools.
Expected Impact: Lower production cost; reduced outsourcing for simple design/video tasks; faster content turnaround.

Module 7: Responsible AI & Data Sovereignty (PDPA)

Defining guardrails around data privacy (PDPA), health claims, and brand voice to ensure safe AI usage in Malaysia.
Scenario (Finance/HR): Establishing review workflows for health-related claims to ensure they remain compliance-safe for pharmacy channels.
Hands-on: Co-create a “GenAI Playbook” outlining do’s/don’ts, approval steps, and “human-in-the-loop” checkpoints.
Expected Impact: Reduced risk of regulatory or medical claim breaches; 100% consistency in brand tone.

Module 8: Brainstorming High-Impact AI Use Cases & Value Mapping

Identifying and prioritizing AI opportunities that align with the organization’s specific business goals.
The Framework: Evaluating ideas based on Feasibility (No-code ease) vs. Business Value (ROI or time saved).
The “Pain-Point” Audit: Mapping current team bottlenecks – such as slow deck preparation or summarizing repetitive customer queries – to specific Hybrid AI solutions.
Expected Impact: A prioritized “AI Backlog” of projects ready for a 3–6 month rollout plan.

List of Deliverables

Upon completion of the program, participants will have produced a tangible “AI Portfolio” including:

Custom Brand DNA Bot: A personalized AI assistant pre-loaded with the organization’s specific tone-of-voice and guidelines.
Master Prompt Library: A centralized repository of high-performing prompts for marketing, trade, and CS departments.
Retailer Proposal & Campaign Decks: Ready-to-use slide outlines and executive summaries for partner submissions.

Proprietary GenAI Playbook: A co-created framework outlining data privacy rules and "human-in-the-loop" checkpoints.
Predictive Simulation Models: Data-backed ROI simulations for upcoming campaigns based on no-code ML models.
LinkedIn & GitHub Showcase: All mini-projects generated are designed to be "portfolio-ready," allowing executives to showcase their AI proficiency on professional platforms.

Prerequisites

Technical Knowledge: No prior coding or technical AI experience is required; the program is strictly designed for business and creative professionals.
Essential Equipment: Participants must bring a laptop capable of accessing web-based tools (ChatGPT, Claude, etc.) and have access to mock corporate data.
Mindset: A willingness to experiment with "thinking partner" AI workflows and a focus on corporate growth.

Who Should Attend

Training Methodology

No-Code Ecosystem Lab: Hands-on application using actual brand briefs and performance tables with tools like ChatGPT, Claude, and predictive modeling platforms.
Predictive Simulation & Prompt Engineering: Interactive labs focusing on audience reaction modeling and clinical-to-human translation frameworks.
Executive Intelligence Co-Design: Group sessions to build the GenAI Playbook and a phased 3–6 month adoption roadmap.

100% HRDC-Claimable

This program is fully registered and compliant with HRDC (Human Resource Development Corporation) requirements under the SBL-Khas scheme, allowing Malaysian employers to offset the training costs against their levy.

Certification of Completion

Participants who successfully complete the program will be awarded a “Professional Certificate in Strategic AI Transformation“.

Post-Workshop Consulting (Optional)

For organizations looking to bridge the gap between training and execution, we offer optional, paid consulting services. These engagements provide expertise and technical support for specific pilot development or full-scale operational integration of the data- and AI-driven use cases established during the program.