AI.dea is the only programme SMEs need before committing to full-scale AI investment and deployment. The programme focuses on practical, hands-on experimentation to help you identify what truly works for your business. We guide you to design, build and validate a real-world AI proof of concept (PoC) – reducing guesswork, risk and unnecessary cost. 

By the end of the programme, you will walk away with a clear, actionable AI adoption strategy to plan: business aligned use cases, governance and risk frameworks, as well as execution playbooks that prepare your organisation for scalable, sustainable AI implementation. 

What sets AI.dea apart for SMEs

1. Real AI Proof-of Concept, Not Theory 

Apply your learning directly to your own business scenario through a hands-on PoC. Test it safely in a sandbox environment before committing any major investment. 

2. Curated Access to Trusted AI Vendors 

Gain access to a pre-vetted pool of reputable AI solution providers. We help you match with the right vendor to co-develop a PoC tailored to your specific needs. 

3. Guided Strategy to Plan Journey

Leaders are guided through a structured approach to define AI ambition, assess readiness, and chart a clear, risk-aware roadmap for confident organisational adoption.

4. Buit-In AI Governance, Cybersecurity & Legal Readiness

Develop a strong foundation in AI governance, risk management, cybersecurity and legal considerations, ensuring your organisation adopts AI safely and responsibly. 

5. Government-Supported & Cost Efficient 

Benefit from significantly subsidized fees through government support, making AI experimentation affordable and accessible for SMEs.

DAY 1

Module 1: Understanding the Future of Business: AI Adoption Landscape 

1. Landscape introduction to AI and its various applications  
2. Benefits of enhancing your business with AI 
3. Target Audience demographics and behavior 
4. Case studies of successful AI adoption across different industries (e.g. Retail, E-commerce, government organizations, logistics) 

Module 2: Setting AI Strategic Ambition

1. Define AI’s role for the organisation: augmentation vs. automation 
2. Prioritizing business functions (CX, Ops, Finance, etc.) 
3. AI maturity levels and strategic ambition mapping 

Module 3: Reviewing your Organization’s AI Readiness & Capability

1. Gaining a Perspective: Data Maturity, Infrastructure, Talent Gaps 
2. Cross-functional AI Readiness Assessment 
3. Cost-benefit Estimation and Productivity Levers 

Module 4: Tech Architecture and Landscape

1. Tech Architecture and Landscape Overview 
2. AI platforms (cloud/on-prem/hybrid), APIs, data flows 
3. Core vs. edge AI deployment 
4. Interoperability with legacy systems 

Module 5: Business Use Case Design and Evaluation

1. Aligning identified business use cases to strategic objectives 
2. ROI projections & success metrics 
3. Building PoCs and pilot 

DAY 2

Module 6: AI Cybersecurity & Risk Landscape 

1. Threats: model poisoning, prompt injection, data theft 
2. Overview of ‘Securing’: Securing ML pipelines, APIs, and endpoints 
3. Data residency, privacy, and regulatory risks 

Module 7: AI Governance & Ethical Oversight

1. Bias mitigation, transparency, explainability 
2. AI audit readiness and compliance (e.g., PDPA, GDPR) 
3. Governance committee & escalation paths 

Module 8: Execution Plan & KPI Alignment 

1. Road mapping: pilots to enterprise-wide rollout 
2. KPI setting (efficiency, productivity, service) 
3. Culture, change management & communications 

Course Schedule