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AI.dea is the only programme that enterprises will 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.Ìý

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.Ìý

What sets AI.dea apart for Enterprises

1. Real AI Proof-of Concept, Not TheoryÌý

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.Ìý

2. Curated Access to Trusted AI VendorsÌý

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.Ìý

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. Built-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.Ìý

5. Government-Supported & Cost EfficientÌý

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

DAY 1

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

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

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Module 2: Setting AI Strategic Ambition

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

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

1. Gaining a Perspective: Data Maturity, Infrastructure, Talent GapsÌý
2. Cross-functional AI Readiness AssessmentÌý
3. Cost-benefit Estimation and Productivity LeversÌý

Module 4: Tech Architecture and Landscape

1. Tech Architecture and Landscape OverviewÌý
2. AI platforms (cloud/on-prem/hybrid), APIs, data flowsÌý
3. Core vs. edge AI deploymentÌý
4. Interoperability with legacy systemsÌý

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Module 5: Business Use Case Design and Evaluation

1. Aligning identified business use cases to strategic objectivesÌý
2. ROI projections & success metricsÌý
3. Building PoCs and pilotÌý

DAY 2

Module 6: AI Cybersecurity & Risk LandscapeÌý

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

Module 7: AI Governance & Ethical Oversight

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

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Module 8: Execution Plan & KPI AlignmentÌý

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

Course Schedule