Programme Outline

Start Date

Wednesday, July 8, 2026

Learning outcomes

• Describe the shift from AI as a search/answer tool to AI as a digital teammate in modern work environments.
• Explain how conversational and human-like AI interfaces change the way individuals interact with technology.
• Identify ways AI can augment creativity and ideation through generative tools.
• Demonstrate basic interaction with AI tools to collaborate on simple work tasks or creative activities.

Start Date

Wednesday, July 15, 2026

Learning outcomes

• Describe key technological developments that led to the current AI inflection point.
• Explain the role of foundation models and large language models in enabling modern AI capabilities.
• Identify examples of industries experiencing disruption due to AI advancements.
• Interpret the implications of AI-native economics such as increased productivity and reduced time-to-output.

Start Date

Wednesday, July 22, 2026

Learning outcomes

• Define Agentic AI and differentiate it from traditional automation and generative AI tools.
• Explain the core building blocks of agentic systems and their role in enabling autonomous workflows.
• Describe how multi-agent systems collaborate to complete complex tasks within enterprise workflows.
• Apply basic workflow orchestration tools to conceptualise simple agent-based processes
• Translate common workplace inefficiencies into potential agentic workflow opportunities.

Start Date

Wednesday, July 29, 2026

Learning outcomes

• Describe the concept of vibe coding and how AI-assisted app creation differs from traditional software development.
• Identify suitable beginner-friendly tools and platforms for creating simple AI-powered applications.
• Build a basic web app prototype using natural-language prompting in a no-code or low-code AI tool.
• Differentiate between realistic capabilities and common misconceptions surrounding vibe coding and AI-generated software.
• Apply basic risk-mitigation practices related to testing, security, privacy and reliability when using AI-generated applications.

Start Date

Wednesday, August 5, 2026

Learning outcomes

• Compare different vibe coding platforms based on use case, complexity and integration capabilities.
• Explain how APIs enable applications to interact with external services and data sources.
• Build a simple AI-powered application that uses an external API to retrieve or process information.
• Design a basic multi-step application workflow that combines user inputs, AI processing and outputs.
• Identify how connectors, tools and data sources can extend AI applications beyond standalone prototypes into functional workflows. 

Start Date

Wednesday, August 12, 2026

Learning outcomes

• Describe the structure of a workflow, including triggers, decisions, handoffs and outputs.
• Differentiate between personal, team, and organisational workflows and their redesign implications.
• Apply computational thinking concepts such as decomposition, pattern recognition, abstraction, and algorithms to workflow problems.
• Map an existing workflow to identify friction points, decision points and opportunities for automation or augmentation.
• Design a basic workflow algorithm that clearly defines triggers, steps, decision gates, outputs and human–AI responsibilities.

Start Date

Wednesday, August 19, 2026

Learning outcomes

• Explain the difference between a demo and a real-world pilot for AI-enabled workflows.
• Identify characteristics of effective AI workflow pilots, including scope, reversibility and measurable success criteria.
• Describe how AI agents, connectors, and multi-agent systems support workflow automation across organisations.
• Apply trigger–action workflow logic to design simple automation chains across tools and systems.
• Evaluate when human oversight, escalation and checkpoints are necessary in automated workflows.

Start Date

Wednesday, August 26, 2026

Learning outcomes

• Explain why many AI pilots fail to scale successfully in organisational settings.
• Differentiate between centralised, federated and hybrid approaches to managing AI workflows.
• Identify operational requirements needed to scale AI workflows safely, including monitoring, ownership, rollback plans and SOP updates.
• Apply appropriate KPIs and governance checks to evaluate AI-enabled operational workflows.
• Describe the stages of AI workflow maturity from experimentation to scaled operational use.

Start Date

Wednesday, September 2, 2026

Learning outcomes

• Identify high-impact agentic AI use cases across operations and sales, based on real-world case studies.
• Analyse workflow patterns that distinguish successful enterprise deployments from failed or experimental ones.
• Evaluate readiness gaps in workflows, data and governance required for effective agentic AI adoption.
• Apply five strategic principles to design and scale agentic AI initiatives within their own organisational context.

Start Date

Wednesday, September 9, 2026

Learning outcomes

• Identify key agentic AI use cases across marketing and customer experience from real enterprise deployments.
• Analyse workflow redesign patterns that enable successful AI integration rather than surface-level adoption.
• Evaluate the role of personalisation, brand integrity and empathy in AI-driven customer interactions.
• Assess how CRM integration and data workflows support scalable, effective agentic AI implementations.

Start Date

Wednesday, September 16, 2026

Learning outcomes

• Identify high-impact AI use cases across HR and finance based on recent real-world deployments.
• Analyse workflow patterns that consistently drive successful outcomes in functional AI adoption.
• Evaluate accountability frameworks that reduce risk and prevent failure in AI-led initiatives.
• Apply a practical management checklist to guide adoption and scaling within HR and finance teams.

Start Date

Wednesday, September 23, 2026

Learning outcomes

• Describe how the widespread availability of AI-driven intelligence changes the nature of managerial decision-making.
• Explain why leadership judgment becomes the primary constraint when AI accelerates execution and analysis.
• Interpret the WHY–WHAT–HOW framework for guiding strategic direction in AI-enabled environments.
• Apply the WHY–WHAT–HOW framework to frame problems and guide AI-supported decision-making.
• Recognise how increased decision and learning velocity reveals leadership capability within organisations.

Start Date

Wednesday, September 30, 2026

Learning outcomes

• Define the leadership roles of Orchestrator, Architect and Steward in an agentic organisation.
• Explain how these three leadership roles enable coordination between humans and AI systems.
• Describe how the interaction between the roles strengthens organisational performance and governance.
• Apply the three-role framework to analyse leadership responsibilities in AI-enabled organisations.
• Identify shifts in leadership responsibilities from execution oversight to system and workflow design.

Start Date

Wednesday, October 7, 2026

Learning outcomes

• Define the concepts of cognitive capital and cognitive debt in the context of AI-enabled work.
• Explain how AI adoption influences task displacement, role redesign, and workforce capability development. 
• Describe how organisational elements such as workflows, decision rights, and incentives must evolve for agentic scale.
• Apply the concept of AI as a thinking amplifier to enhance learning, questioning, and reflection in professional contexts.
• Identify strategies for redesigning work practices to build cognitive capital in AI-enabled organisations.

Start Date

Wednesday, October 14, 2026

Learning outcomes

• Describe the AI Paradox, where high AI adoption does not translate into significant organisational impact.
• Explain why organisational and structural barriers often prevent AI initiatives from delivering business transformation.
• Identify common failure patterns in AI initiatives, such as pilots that fail to scale or AI systems remaining advisory. 
• Interpret how organisational resistance and role protection can act as barriers to AI-driven change.
• Apply a leadership lens to diagnose structural challenges that limit the impact of AI adoption within organisations.

Start Date

Wednesday, October 21, 2026

Learning outcomes

• Define the principles of Responsible AI in the context of agentic systems.
• Describe common AI failure modes, including hallucinations and reliability risks in generative systems.
• Explain how governance models evolve from traditional approval processes to boundary-based guardrails and monitoring systems.
• Apply governance frameworks to support the responsible deployment of AI agents at organisational scale.
• Identify strategies for scaling AI initiatives from isolated pilots to enterprise-wide systems.

Start Date

Wednesday, October 28, 2026

Learning outcomes

• Describe the shift from episodic organisational change to continuous adaptation in AI-driven environments.
• Explain how iteration, experimentation, and versioning support sustained AI-enabled transformation.
• Identify leadership practices required to manage organisations operating with continuously evolving AI systems.
• Apply strategies to embed ongoing learning and iterative improvement into organisational change initiatives.
• Recognise emerging trends shaping the future of agentic AI ecosystems and organisational AI capabilities.

Start Date

Wednesday, November 4, 2026

This week you will submit the capstone project.