Product Management with AI

For experienced leaders in product, engineering, data, and innovation seeking to formalise their AI expertise, evaluate emerging technologies, and drive responsible AI adoption across their organisations.

How this programme delivers value:

• Bridges technical AI expertise with overarching product strategy and measurable commercial outcomes.

• Develops frameworks to evaluate AI investments, workflows, ROI, and business value.

• Equips you to lead responsible AI development while staying current with trends like LLMs and Multimodal AI.

• Supervised, unsupervised and reinforcement learning: what PMs need to know

• Recommendation systems, AI search ranking and personalisation engines for digital products

• LLMs and agentic systems: reasoning, planning and tool use

• RAG, fine-tuning, prompt engineering and agentic tool use: when to use each

• Multimodal AI: image, video and voice applications in e-commerce and digital experiences

• Data science and ML collaboration: model lifecycle, evaluation metrics and bias awareness

• Agentic AI: from copilots to autonomous, goal-directed systems

• Anatomy of an AI agent: planning, memory, tool use and reasoning loops

• Autonomous product workflows: guardrails, escalation paths and success metrics

• Multi-agent systems and orchestration in digital product experiences

• Autonomy levels: copilots, semi-autonomous and fully autonomous systems

• Customer research methods: ethnography, interviews, surveys and session recordings

• Jobs-to-be-done framework for digital and AI product discovery

• AI-powered and agentic customer research: sentiment analysis, review mining and behavioural analytics 

• Market sizing for digital and AI products: TAM, SAM and SOM

• Competitive intelligence using AI research agents: automated benchmarking and desk research

• Customer segmentation and AI-assisted persona building

• Product opportunity gaps and the problem–find–solve approach

• Ideation frameworks: design thinking, How Might We and AI-assisted ideation

• AI-assisted product concepts: the need–form–technology nexus

• Scoping AI and digital product opportunities: feasibility, viability and desirability

• Minimum Viable Product development

• P&L interpretation for product managers

• Unit economics for digital and AI products: CAC, LTV, GMV, margins and cost per agent task  

• Product portfolio strategy and lifecycle management for digital and AI products

• Positioning digital and AI products: competitive differentiation, category creation and uniqueness

• AI-enabled market and competitive intelligence for product strategy

• AI-assisted roadmapping with agentic planning copilots

• Prioritisation frameworks: RICE, MoSCoW and opportunity scoring with AI

• AI product opportunity prioritisation: value, feasibility, data readiness, risk and strategic alignment

• AI product monetisation and business models: freemium, subscription, usage-based and outcome-based agent pricing

• Dynamic and AI-driven pricing for e-commerce and digital platforms

• Conversational and action-based agent UX patterns

• Product-vision communication with UX design teams using agentic AI design tools

• Product-vision development through real-time user feedback

• Prototyping with Figma for digital and agentic product flows

• Instrumentation fundamentals: event tracking, funnels and session data for digital products

• SQL for product managers and natural-language and agentic querying

• North Star metrics, KPIs and AI and agent performance metrics: accuracy, latency and task-success rate

• AI and agent readiness: data infrastructure, quality and governance prerequisites

• Data-informed product dashboards

• Lean and agile product development for AI and agentic feature delivery

• Agile Scrum philosophy and methodology for AI and digital product teams

• Cross-functional collaboration: engineering, data science, design, legal and finance

• Agentic systems in agile workflows: planning, quality assurance and delivery

• Risk, accountability and control in agent-assisted product execution

• Go-to-market strategy for AI-powered and agentic digital products

• Product-led growth principles and AI- and agent-driven acquisition loops

• Market understanding: AI opportunity sizing and competitive benchmarking at launch

• Launch metrics, success criteria and post-launch iteration planning

• Case study: go-to-market strategies for AI-first and agentic digital products in Southeast Asia

• AI product performance measurement through product, model and agent metrics

• AI feature evaluation across accuracy, quality, latency, cost and task success

• AI feature monitoring and performance diagnosis

• AI-specific failure modes and uncertainties

• User feedback, experimentation and online evaluation

• AI feature optimisation: behaviour, guardrails and fallback experiences

• Product decisions based on performance evidence and trade-offs

• Rapid prototyping using LLM APIs, no-code AI builders and agent frameworks

• AI workflow design and multi-step orchestration

• API, retrieval, tool and enterprise-system integration

• MVP strategy for AI features: building the smallest useful intelligence

• User validation of AI features and pre-production iteration

• Personalisation using user context, embeddings and behavioural signals

• Recommendation and ranking systems for engagement and discovery

• Continuous learning using feedback loops

• Experimentation for AI features: A/B testing, contextual bandits and online evaluation

• GenAI and agentic personalisation: conversational recommendations and next-bestaction experiences

• Business-impact measurement: engagement, conversion, retention, LTV and task success

• Translation of selected product ideas into AI features and product-level specifications

• AI product requirements across the model, data and context, user and product-system components

• AI-native PRDs as a method for formalising product requirements and dependencies

• Product-manager-led prototyping for testing product assumptions, feasibility and user value

• Iterative validation and refinement of AI features before production

• AI-specific failure modes and uncertainties in product development

• Product-requirement and design refinement based on prototyping insights

• Algorithmic bias and fairness in recommendation, search and pricing systems

• Dark patterns and consumer protection in AI-driven digital products

• Data privacy and regulatory compliance

• AI and agent security: adversarial attacks, prompt injection and tool-misuse risks

• Transparency, explainability and user consent in AI and agentic features

• Case studies involving organisations such as Amazon, the FTC and the SEC

• Platform examples including Microsoft Copilot and ChatGPT

• Strategic thinking and business acumen for senior PM influence

• AI and agentic capabilities translated into business value and executive communication

• Stakeholder management: alignment across engineering, design, legal, finance and leadership

• Product operations: tools, processes and rituals for high-performing AI and digital product teams

• AI-first and agentic-first product-team development and mentoring

• Product-impact measurement and communication: revenue, retention and adoption

• Platform thinking: scaling digital and AI products and building ecosystem value

• Multi-agent ecosystems and platform-scale orchestration systems

• Self-improving product systems powered by autonomous feedback loops

• Innovation frameworks for continuous product evolution

• Market understanding: the evolving competitive landscape for AI-first digital products

• Agent-native product organisations and operating models

• End-to-end agentic product-system design and prototyping from discovery to deployment

• Product discovery, strategy, design, development, validation and governance integration

• Product concept, feature specifications, prototype and implementation-roadmap presentation

This programme suits mid-career professionals transitioning from traditional to AI-first product management; and senior product, technology and AI leaders scaling AI adoption. It is delivered by SMU in Singapore and well suited to professionals based in Singapore and across Asia-Pacific. 

Agentic AI refers to autonomous, goal-directed systems that plan, use tools, and complete multi-step tasks with minimal human input. Agentic AI for product managers means designing workflows, guardrails, and escalation paths around these systems. A dedicated module in this programme covers agentic AI and autonomous product workflows in depth. 

Becoming an AI product manager typically means building on product management fundamentals with AI-specific skills: agentic workflows, AI-native PRDs, prompt engineering, and AI business economics. This programme is structured to build exactly this capability over 18 weeks, whether you are transitioning from a traditional PM role or starting out in a digital or AI-adjacent function. 

An AI product manager designs and ships AI-powered features, evaluates AI and agent performance metrics like accuracy and task-success rate, and builds business cases around AI infrastructure costs and responsible AI practices such as bias mitigation and governance, alongside the usual product discovery, roadmapping, and stakeholder work of a traditional product manager. 

This programme gives hands-on exposure to 15+ enterprise AI tools spanning product management (Productboard, Craft.io), analytics (Mixpanel, Amplitude, Google Analytics), AI models (Claude, ChatGPT, Gemini, Perplexity), no-code building (Lovable, Bolt.new), automation (n8n, Zapier, Make), and design (Figma), the practical toolkit modern AI product leaders are expected to use.   

No coding background is required. The programme is designed for product, digital and business professionals at any career stage, from mid to senior leadership, and builds technical fluency in AI concepts, tools, and agentic workflows as part of the curriculum itself, rather than assuming it going in. 

Participants build an AI-first and agentic product manager mindset, learn to run AI-powered product discovery, build business cases using AI infrastructure ROI and unit economics, design and ship AI features through AI-native PRDs, and apply responsible AI governance: the core skill set behind current AI product manager roles. 

Participants complete an end-to-end capstone project where they conceptualise, design, and prototype an AI-first digital product addressing a real-world opportunity, applying discovery, strategy, prototyping, and responsible AI governance skills built across the 18-week programme. 

Participants who successfully complete the programme receive a verified digital certificate of completion from Singapore Management University, along with SMU Associate Alumni Status and its benefits, including discounts on future SMU Executive Development programmes, for professionals looking to build AI-first product capabilities backed by a recognised university.