Our Programs

Learning Objectives

• Explain how core AI and agentic AI technologies work, and where they create real business value
• Separate genuine opportunity from hype across the current AI landscape
• Assess AI's strategic impact on their industry, business model, and competitive position
• Build a phased AI roadmap that ties technology investment to measurable business outcomes
• Size AI investment accurately and evaluate build-vs-buy-vs-partner decisions
• Establish AI governance, risk, and compliance structures aligned to emerging regulation
• Ask the right questions of technology and legal teams, and hold them accountable
• Lead the workforce, culture, and operating-model shift required to scale AI organization-wide

Learning Objectives

• Apply AI and generative AI tools confidently in day-to-day professional work
• Use AI securely and responsibly within corporate data, privacy, and compliance boundaries
• Master prompt engineering and structured techniques for reliable, high-quality output
• Identify high-value AI use cases within your own function and workflows
• Build and test simple AI workflows and prototypes without deep technical skills
• Draft, analyze, and produce professional work at significantly higher speed and quality
• Run effective change management to drive AI adoption across your team
• Build a culture of AI fluency that compounds over time

Learning Objectives

• Scope the agent: define exactly what it should do, and what should stay outside its remit.
• Identify requirements: every system and data source the agent needs, and how each can be connected safely.
• Build the agent: configure model choice, instructions, tools, guardrails, evaluation, and checkpoints together.
• Plan the roll-out strategy: how to go from pilot to large-scale implementation.
• Go live: deploy the agent connected to real data and systems, with clear governance and security protocols in place.

Learning Objectives

• Make the case for coding agents in business terms — and be clear about where they don't yet deliver.
• Apply a selection framework to choose the right agents and models for your context, including build-versus-buy and exit risk.
• Route work to the appropriate model by task class, managing context and token consumption as a deliberate cost and quality lever.
• Decompose engineering work into agent-sized tasks and run multiple agents in parallel without creating integration or merge chaos.
• Identify what in your repositories, tests, CI, and tooling needs to change before agents can perform reliably, and leave with a backlog of those changes.
• Apply a review and verification standard for agent-generated code, recognize the failure modes that pass a casual read, and size review capacity to throughput.
• Evaluate deployment topology, data residency, IP, and vendor terms against your regulatory and risk appetite.
• Define who gets access, at what autonomy tier, with which approval gates and audit trail.
• Choose outcome metrics and cost controls that show whether adoption is actually working — and reject the vanity metrics that suggest it is.
• Position coding agents inside your organization's wider AI platform and governance model, rather than as shadow tooling.