AI Leadership and Innovation Training Course

Participants can prioritise AI initiatives and build a sequenced portfolio linked to strategic priorities.

• Ambition-to-value logic links strategic priorities, AI bets and measurable outcomes
• Three-horizons portfolio balances efficiency, growth and exploratory AI initiatives
• Prioritisation matrix scores value, feasibility, risk, data readiness and adoption
• Build-buy-partner decisions assess capability, cost, control and vendor dependence
• Stage-gate roadmap sequences discovery, pilot, scale and retirement decisions
• Investment thesis states assumptions, resource needs, benefits and decision criteria

Participants can establish proportionate governance, risk controls and accountability for AI initiatives.

• NIST AI RMF functions structure Govern, Map, Measure and Manage activities
• ISO/IEC 42001 controls assign AI management-system responsibilities and evidence
• Risk register captures harm scenarios, likelihood, impact, owners and controls
• Human oversight design defines escalation thresholds, review authority and intervention
• Model documentation records purpose, data, limitations, testing and approvals
• Third-party due diligence assesses provider security, terms, provenance and monitoring

Participants can identify the data, privacy, security and assurance conditions needed for responsible AI use.

• Data product thinking assigns owners, quality rules, access and lifecycle decisions
• Data lineage traces source, transformation, feature use and model outputs
• Privacy impact assessment identifies personal-data processing risks and safeguards
• Bias testing compares error rates across relevant user or customer groups
• Retrieval-augmented generation grounds responses in approved organisational knowledge
• Prompt injection and data-exfiltration controls protect generative AI workflows

Participants can redesign team roles, accountabilities and management routines for AI-enabled work.

• Role redesign separates human judgment, AI assistance and automated execution
• RACI matrix clarifies accountabilities for product, data, risk and operations
• Psychological safety practices surface errors, doubts and unintended consequences early
• Skills matrix identifies AI literacy, technical, domain and assurance gaps
• Managerial coaching uses task decomposition, feedback loops and decision reviews
• Operating cadence sets forum purpose, decision rights, metrics and escalation

Participants can structure AI innovation experiments that produce evidence before significant investment.

• Design thinking moves from user evidence to testable AI service concepts
• Opportunity solution trees connect customer problems, outcomes and AI experiments
• Hypothesis statements specify users, expected behaviour, measures and assumptions
• Minimum viable product scope protects learning while limiting operational exposure
• Experiment design defines comparison groups, success thresholds and stopping rules
• Service blueprint maps frontstage, backstage, data and model touchpoints

Participants can plan adoption activities that support changed workflows, skills and employee confidence.

• Stakeholder mapping identifies sponsors, affected groups, blockers and advocates
• ADKAR planning addresses awareness, desire, knowledge, ability and reinforcement
• Workflow analysis redesigns handoffs, exceptions, approvals and accountability
• Communication plan distinguishes business rationale, user guidance and risk boundaries
• Training needs analysis segments audiences by role, task and exposure
• Adoption dashboard tracks active use, completion, satisfaction and workarounds

Participants can make and govern AI-related decisions under uncertainty, competing interests and ethical constraints.

• Decision rights distinguish recommendations, approvals, delegations and autonomous actions
• Calibrated trust aligns reliance with evidence, uncertainty and task criticality
• Structured dissent tests AI proposals through red-team challenge and counterevidence
• Scenario planning examines workforce, customer, regulatory and competitive consequences
• OECD AI Principles inform stakeholder, fairness, transparency and accountability analysis
• Ethical decision memo documents stakeholders, trade-offs, rationale and safeguards
• Conflict-resolution protocol addresses disputes over outputs, ownership and accountability

Participants can measure AI value, monitor performance and make evidence-based scale or stop decisions.

• Benefits realisation map links outputs, adoption, outcomes and financial value
• Balanced scorecard measures customer, process, people, risk and innovation results
• Model performance monitoring tracks quality, drift, latency, cost and incidents
• Leading indicators reveal adoption barriers before benefits targets are missed
• Post-implementation review captures lessons, controls gaps and scale decisions
• Portfolio review reallocates funding using evidence, risk exposure and strategic fit

Participants can prepare a practical, accountable plan for progressing an AI initiative after the course.

• AI initiative charter defines problem, sponsor, users, scope and intended outcomes
• 90-day action plan sequences decisions, stakeholders, resources and review points
• Executive narrative connects evidence, uncertainty, investment and organisational purpose
• Board briefing structure presents opportunity, controls, accountability and required decisions
• Peer review tests assumptions, dependencies, risks and implementation readiness
• Personal leadership commitments specify behaviours, conversations and governance actions