Day 4 (18 June 2026) - WorkshopsThursday, 18 June 2026 - AT (Arabian Time, GMT+03:00)

Morning Learning Sessions

• Understand the stages of AI implementation and enterprise alignment
• Learn to connect AI objectives with wider business and transformation goals

Interactive Knowledge Check (Individual Participation)

Join a fast-paced, instructor-led review that recaps key implementation principles. You will participate in real-time questioning and discussion to reinforce understanding.

Continued Morning Learning

• Explore resource planning, budgeting, and team composition models
• Review governance checkpoints, risk management, and performance monitoring

Interactive Team Challenge

Collaborate in small groups to address short implementation case scenarios. Each team will share insights on risk, resourcing, or governance alignment, building confidence before the afternoon review.

Lunch Break

Afternoon Integration & Certification

• Participate in a comprehensive review of all four modules to understand how strategy, readiness, roles, and implementation interconnect
• Engage in a team-based practice quiz and confidence check using realistic scenarios with instant feedback
• Receive a detailed briefing on exam structure, timing, and expectations to ensure readiness
• Complete the formal multiple-choice certification exam validating your professional competence
• Conclude the workshop with a closing reflection and practical next steps

Theme: Develop the foresight, capability, and culture to lead AI adoption responsibly and sustain trust in an evolving technological landscape

Session 1: Building Organisational Confidence and Capability

• Executive accountability for responsible AI outcomes
• Developing multi-disciplinary teams and future-ready skill sets
• Embedding continuous professional learning and data-driven culture

Session 2: Leading Through Technological Change

• Managing transformation in governance and compliance structures
• Communicating AI values and ethical expectations internally
• Enabling digital confidence and resilience across the workforce

Session 3: Anticipating the Next Horizon

• Forthcoming ethical and regulatory challenges — agentic AI, autonomy, and adaptive systems
• The evolution of global assurance standards
• Positioning sustainability and ESG alignment within AI strategy

Session 4: From Insight to Action

• Reflection on key insights from the programme
• Crafting a Responsible AI Charter and communication plan
• Defining 90-day implementation priorities for governance, ethics, and compliance maturity

Activities:

• Future Vision Discussion: Groups imagine their organisation’s governance landscape in 2030
• Responsible AI Charter: Teams draft a set of commitments to guide future AI projects
• 90-Day Plan: Each participant writes three concrete steps to implement after the course, shared in peer pairs for accountability. - the content looks really good! Lots to get through. I am just not sure on the overview and title

• Foresight into the next decade of connected living
• Emerging technologies shaping customer and citizen journeys
• The ethics of automation and sustainable intelligence
• Building readiness and capability for future ecosystems

• Unsupervised learning and pattern detection in finance
  • The importance of grouping financial decision making
  • Using clustering tools for grouping
  • The main concepts of clustering
  • Elbow and silhouette scores
  • Hands-on case studies
  • Using clustering to understand credit risk profile segmentation
• Supervised learning for finance
  • Overview of supervised learning
  • Common techniques – Regression, Decision Tree, and Random Forest
  • Applications in credit scoring, default probability, and financial distress forecasting
• Understanding model usefulness
  • Understanding AUC-ROC curves
  • Confusion matrix
• Using logistic regression for predicting financial distress
  • Setting up logistics regression models
  • Optimising logistics regression
  • Application of AUC-ROC to assess model effectiveness
  • Hands-on case studies
• Using Random Forest to predict financial distress
  • Setting up Random Forest Models
  • Interpreting results
  • Hands-on case studies
• Scenario analysis and simulation
  • Overview of scenario analysis and simulation
  • Using scenario analysis and simulation
  • Setting up Monte Carlo simulations
  • Using Monte Carlo simulation to compute Value at Risk
  • Using Monte Carlo simulation in net present value analysis
  • Hands-on case study