Cart 0
Training
Course Information
Course Name
AI-ST: AI Strategy & Transformation for Leaders
Duration
2 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
This course equips leaders with the knowledge, frameworks, and practical tools to successfully adopt, implement, and scale AI within their organizations.
It combines strategy, implementation, governance, and real-world case studies, with a strong focus on Malaysia’s regulatory environment (PDPA) and business realities.
Participants will move from understanding AI concepts to designing a practical AI transformation and implementation plan, balancing innovation, risk, and measurable business value.
Overview
Overview
This course equips leaders with the knowledge, frameworks, and practical tools to successfully adopt, implement, and scale AI within their organizations.
It combines strategy, implementation, governance, and real-world case studies, with a strong focus on Malaysia’s regulatory environment (PDPA) and business realities.
Participants will move from understanding AI concepts to designing a practical AI transformation and implementation plan, balancing innovation, risk, and measurable business value.
Audience Profile
Audience Profile
- C-level executives (CEO, CTO, CIO, COO)
- Business unit leaders
- Product & innovation leaders
- Digital transformation teams
- Senior managers
Prerequisities
Prerequisites
At Course Completion
At Course Completion
By the end of the 2 days, participants will be able to:
- understand AI, LLM, prompt and context engineering from a leadership perspective
- identify high-value AI opportunities across business functions
- develop an AI strategy aligned with business goals
- design AI transformation and implementation roadmaps
- evaluate public vs private AI deployment models
- manage organizational change and workforce transformation
- implement governance, risk, and Malaysia PDPA-compliant AI practices
- measure ROI and business impact of AI initiatives
- understand how AI workflows and semi-autonomous systems (agentic AI) impact operations
Course Outline
Course Outline
Module 1: AI Landscape for Leaders (Modern AI Concepts)
- What is AI, Generative AI, and LLM
- Prompt engineering (business perspective)
- Context engineering (data + instruction quality)
- AI workflow thinking:
- Input → AI → Output → Human validation
- From tools → assistants → semi-autonomous systems (Agentic AI)
- Strengths vs limitations (hallucination, bias, context)
- Case Study 1: Marketing Automation
- AI generates full campaign (content + visuals)
- Impact: speed, cost reduction, scalability
Module 2: AI Impact on Organisation & Workforce
- AI and the future of work
- Role redesign (augmentation vs replacement)
- Skills transformation:
- o Prompting
- o Critical thinking
- o AI supervision
- Productivity vs headcount considerations
- Discussion
- Which roles in your organization will change?
Module 3: AI Use Case Discovery & Prioritisation
- Identifying high-impact vs low-impact use cases
- Value creation framework:
- Efficiency
- Revenue
- Risk reduction
- rioritisation matrix (impact vs feasibility)
- Workshop 1
- Identify and prioritise top 3 AI use cases
Module 4: Data, Context & AI Readiness
- Role of data in AI success
- Context quality = output quality
- Data maturity levels
- Common blockers:
- o Data silos
- o Poor quality data
- Malaysia Compliance
- Personal Data Protection Act 2010 (PDPA)
- What data can/cannot be used in AI
- Risks of sharing sensitive data with public AI
- Exercise
- Assess organizational readiness
Module 5: AI Business Case & ROI
- Justifying AI investment
- Cost components:
- o Tools
- o Integration
- o Training
- Benefit areas:
- o Time savings
- o Revenue growth
- ROI estimation model
- Workshop 2
- Build a simple AI business case
Module 6: AI Implementation Strategy
- Public vs private AI:
- Public (e.g. ChatGPT)
- Enterprise / private AI
- When to use which model
- Account & access strategy:
- Individual vs enterprise accounts
- Role-based access
- Implementation models:
- Ad-hoc usage
- Controlled rollout
- Integrated workflows
- AI-first organisation
- Workshop 3
- Define implementation model for your company
Module 7: AI Integration & Workflow Design
- AI as workflow layer (not just tool)
- Integration with:
- CRM
- Reporting
- Operations
- Multi-step workflows:
- Data → AI → Insight → Action
- Case Study 2: Finance Reporting Automation
- Monthly report automated using AI
- Impact: time reduction, consistency
Module 8: Change Management & Adoption
- Why AI initiatives fail
- Managing resistance
- Upskilling workforce
- Embedding AI into daily workflows
- Workshop 4
- Design AI adoption plan
Module 9: Governance, Risk, Data Privacy & Compliance
- AI risks:
- Bias
- Hallucination
- Data leakage
- Data privacy in AI:
- Risks of public AI tools
- Sensitive data handling
- PDPA compliance
- AI usage policies:
- What employees can/cannot do
- Governance structures
- Case Study 3: Data Leakage Scenario
- Employee uploads customer data to AI
- Risk + mitigation strategy
Module 10: Scaling AI & Continuous Improvement
- From pilot → production → scale
- Monitoring AI performance
- Improving outputs over time
- Building AI-first culture
- Workshop 5
- Define scaling strategy
