Strategic Leadership With AI

• Demystifying Deep Learning for Business Leaders

• Neural Networks without the math

• Text generation and language models

• Understand AI and Large Language Models (LLMs)

• How GPT, Claude and Gemini work

• Capabilities and limitations of LLMs

• How and why training and LLM work, without the math

• Customising an LLM, via training and fine-tuning

• Evaluating the LLM after training or customising without training

• Deploying a customised LLM

• Understand RAG architecture and why it matters for enterprise AI

• Master vector databases, embeddings, and retrieval strategies

• Evaluate RAG system performance with key metrics

• Design and deploy RAG for your organisation

• What defines an autonomous AI agent

• Agent design: memory, planning, feedback loops

• Tool orchestration: APIs, LLMs, systems

• Multi-agent collaboration and coordination

• Strategic AI Opportunities in HR (talent, workforce planning, productivity)

• Strategic AI Opportunities in Finance (forecasting loops, risk, performance)

• Strategic AI Opportunities in Marketing & Sales (signals, orchestration, conversion)

• Strategic AI Opportunities in Supply Chain (forecasting, routing, exception handling)

• Cost vs Revenue Prioritisation for Functional AI Initiatives

• Designing Cross-Functional AI Workflows and Intelligence Loops

• Breaking Functional Silos Through Agent-Enabled Decision Flows

• Leadership Actions for Aligning Functional AI to Enterprise Transformation

• Gen AI and Agentic AI Applications in Marketing, Sales, and Customer Experience

• Enhancing Finance, Risk Management, and Supply Chain with AI and Agentic AI

• Leveraging AI and Agentic AI for HR Transformation and Talent Strategy

• Leading AI and Agentic AI Initiatives Across Business Functions

• Driving Operational Efficiency Through AI and Applied Agentic AI

• Transitioning to AI-Enhanced Services and Offerings

• Building Product-as-a-Service Models enabled by Intelligence Layers

• AI for Smarter Logistics, Inventory, and Adaptive Operations

• Scaling AI Across Products and Service Lines (platform architecture)

• Design for Hyper-Personalisation and AI-Augmented Product Innovation

• Agent-Based Recommendation Engines and Self-Optimising Delivery

• Designing Data Feedback Loops to Improve Products Continuously

• Industry-level GenAI use cases and competitive dynamics (Retail, CPG, BFSI, Manufacturing, Public Sector)

• Prioritisation frameworks for GenAI portfolio selection (value, feasibility, risk, data readiness)

• Enterprise integration patterns: RAG, retrieval quality, workflow orchestration, validation layers

• Model strategy: open-source, API, hybrid, sovereign cloud, on-prem trade-offs

• Data, infrastructure, and operational dependencies (security, latency, cost, compliance)

• Guardrails for accuracy and reliability (hallucination controls, sandboxing, quality gates)

• Governance alignment with local guidelines, compliance framworks and enterprise AI policies

• Enterprise roadmap: pilots → platform → scale; capability maturity and operating model choices

• AI as a Central Business Strategy

• Crafting an AI Vision and Strategic Narrative

• AI-Driven Business Model Reinvention

• AI Portfolio Strategy: where to play and how to win

• Agent-first Strategy: redesigning decision flows and leadership responsibilities

• Building strategic moats in the AI-driven economy

• Enterprise readiness for AI-led reinvention

• AI Value Chains and National Strategies

• AI as Narrative Amplifier (and Distorter)

• Weaponisation of AI Technologies

• Building Resilient, Sovereign AI Systems

• Practical Sovereignty for Corporates: What ‘Sovereign AI’ Means at the Enterprise Level

• Scaling AI Projects from Pilots to Enterprise Platforms

• Building a Robust Data Strategy for AI (quality, lineage, governance, accessibility)

• Operational Readiness for AI Deployment (integration, monitoring, security)

• Structuring AI Teams & Operating Models (CoE, federated, hub-and-spoke, platform)

• Cross-Functional Collaboration Models

• Measuring AI Success (ROI, KPIs, adoption, performance)

• Agent Lifecycle Management (monitoring, controlling, orchestrating autonomous systems)

• Leading Organisational Transformation in the Age of AI

• Fostering a Culture of Curiosity, Experimentation, and Psychological Safety

• Addressing Fear, Resistance, and AI Anxiety

• Communicating AI Initiatives with Clarity and Credibility

• Embedding AI into Change Programs and Leadership Rituals

• Redesigning Leadership Roles in Hybrid Human–Agent Organisations

• Building Trust in Agentic Systems: Transparency, Accountability, and Boundaries

• Designing AI-Augmented Teams and Hybrid Roles

• Preparing for Job Evolution in the Age of AI

• Building AI-Centric Upskilling and Reskilling Strategies

• Redesigning Performance and Talent Metrics for the AI Era

• Human–Agent Collaboration Models: AI as colleagues, not just tools

• Identifying and Mitigating AI Bias, Fairness, and Ethical Risks

• Navigating Privacy Regulations and Compliance in AI

• Promoting AI Transparency and Explainability

• Ethical risks in Agentic AI — runaway autonomy, decision accountability

• Establishing Effective AI Governance Frameworks

• Build a comprehensive AI strategy for a real-world business challenge in a 1-Week capstone project.

Yes. The curriculum is designed specifically for AI for business leaders and senior executives. It focuses on Enterprise AI Strategy and governance rather than coding, bridging the gap between technical possibilities and strategic business execution without requiring any prior programming experience.

Unlike broad overviews, this rigorous AI executive education programme provides deep strategic application. By analysing key tools and frameworks, participants move beyond theory to understand and evaluate enterprise-grade AI architectures for global business environments.

Governance is a core pillar. The programme leverages Singapore’s forward-looking regulatory environment, teaching you to navigate the NIST Risk Management Framework and IMDA AI governance. You will learn to implement a compliant Sovereign AI strategy that balances rapid innovation with accountability.

The programme is structured for busy senior professionals. It requires a manageable commitment of 4-6 hours per week over 17 weeks. This allows you to gain proficiency in Agentic AI for leaders and complete the practical 1-week capstone without disrupting your executive responsibilities.

Absolutely. As a premier AI for business leaders online experience, the 17-week format is designed for global accessibility. While anchored in robust governance frameworks like ASEAN and IMDA guidelines, the strategic focus and global case studies apply universally to global enterprises.

Yes. The learning journey includes live, interactive sessions led by distinguished SMU faculty. This ensures you receive expert, industry-relevant insights while acquiring the skillset to build a comprehensive Enterprise AI Strategy tailored specifically to your organization's real-world challenges.

The ultimate goal is moving from experimentation to orchestration. Strategic leadership with AI empowers you to redefine capital allocation, redesign operating models, and build an intelligent, secure enterprise, positioning your organization at the forefront of the algorithmic decade.