Core focus: AI concepts, models, and leadership implications and cultural, ethical, and workforce considerations.

Session Breakdown:

1. AI & Automation Fundamentals in Leadership Context

• Types of AI (predictive, generative, automation systems)

• Strengths vs limitations of AI models (bias, hallucination, data dependency)

• Real-world leadership use cases

2. Organisational Impact of AI Adoption

• Culture, job redesign, wellbeing implications

• Workforce transformation vs displacement

• Change management considerations

3. Ethical & Responsible AI

• Bias, fairness, transparency, accountability

• Regulatory considerations (UK/EU AI governance landscape)

• Responsible AI frameworks

4. Horizon Scanning & Emerging Trends

• AI trends shaping industries (automation, co-pilots, autonomous systems)

• Evaluating vendor claims vs reality

Practical Activity:

Case study: AI implementation failure vs success (root cause analysis) 

Group task: Identify ethical risks in a simulated AI rollout 

Outputs: Ethical risk log and emerging trends briefing.

Core focus: Leadership responsibility in AI adoption and building organisational AI strategy.

Session Breakdown:

1. Leadership Role in AI Strategy

• Setting organisational vision and values for AI

• Governance structures and accountability

• Aligning AI to business objectives

2. Building the Business Case for AI

• ROI vs Risk vs Reputation

• Staff engagement and organisational trust

• Sustainability and long-term value

3. Developing AI Strategy & Roadmaps

• Strategic planning frameworks

• Workforce development planning

• Risk management approaches

4. Stakeholder Engagement

• Gaining buy-in from leadership and operational teams

• Communicating AI strategy effectively

Practical Activity:

• Develop an AI strategy outline for a chosen organisation

• Stakeholder mapping and influence planning

Outputs: Draft AI strategy and stakeholder engagement plan.

Core focus: Feasibility, procurement, and evaluation of AI solutions.

Session Breakdown:

1. Assessing AI Solution Viability

• Feasibility analysis: Cost, time, data quality and maturity

• Build vs Buy decisions

2. Testing & Evaluation Methodologies

• Test environments and test data

• Performance metrics and validation

• User acceptance testing (UAT)

3. Risk Assessment & Unintended Consequences

• Workforce displacement risks

• Ethical and operational risks

• Scenario planning

4. Data-Driven Decision Making

• Using qualitative and quantitative data

• Interpreting evaluation outputs

Practical Activity:

• Evaluate a mock AI vendor solution

• Conduct risk and feasibility assessment

Outputs: AI solution evaluation report and risk assessment matrix.

Core focus: Policies, Governance and Human-AI integration.

Session Breakdown:

1. Data Governance & Security

• Policies for AI and data usage

• Compliance and regulatory considerations

• Information and security frameworks

2. Human Oversight in AI Systems

• Human-in-the-loop vs Human-on-the-loop

• Accountability frameworks

• Decision escalation processes

3. Human-AI Collaboration Models

• Augmentation vs Automation

• Designing workflows that optimise human + AI performance

4. Continuous Improvement & Monitoring

• Performance monitoring frameworks

• Feedback loops and optimisation

• QA and evaluation cycles

Practical Activity:

• Design a governance framework for AI adoption

• Create a Human-AI workflow model

Outputs: AI governance framework and Human-AI collaboration model.