2025 Agenda

• Creating internal oversight mechanisms to ensure AI use aligns with organisational values and external requirements 
• Turning business expectations into practical governance frameworks to drive compliance without slowing innovation 
• Assigning responsibility for AI governance across leadership, risk, and compliance teams 

• Why is an AI governance framework essential and what key elements must it include? 
• How should organisations balance AI risk against innovation when developing & integrating AI governance?  
• What works & what doesn't when embedding AI governance into everyday operations? 

• Implementing training programs for employees at all levels, including developers, leaders, and decision-makers 
• Publishing clear documentation for AI systems, including intended use, limitations, and decision-making logic 
• Integrating ethical checklists and impact assessments into the AI project lifecycle 
• Establishing a continuous monitoring system to track AI performance and flag unintended consequences  

• Addressing consumer and stakeholder expectations through user-facing testing, explanations & visualisations 
• Establishing internal audit protocols and third-party reviews to assess fairness, bias, and compliance 
• Implementing AI disclosures and reporting structures to align with best practice transparency and accountability 

• Conducting organisation-wide AI risk mapping to identify and prioritise potential threats across technical, legal, and ethical domains 
• Embedding AI risk checkpoints into existing risk management processes to ensure oversight during development and deployment  
• Implementing a model governance framework with approval gates, documentation standards, and sunset/review dates for deployed AI 

• What governance structures, policies, and frameworks can organisations implement to detect and eliminate AI bias before regulatory enforcement? 
• What governance-led strategies ensure AI design, development, and deployment align with ethical principles and regulatory expectations? 
• How can organisations develop robust governance to maintain public trust and avoid AI failures that lead to reputational damage and regulatory scrutiny? 

• Developing AI-specific incident response plans to handle system failures, legal breaches, and reputational damage 
• Learning from case studies on AI-related crises, such as biased decision-making, misinformation, or unauthorised data exposure 
• Establishing AI risk mitigation teams and governance protocols to contain, investigate, and rectify AI failures  

• Developing a risk-based governance model that accounts for AI autonomy levels and direct user impact 
• Establishing agentic & customer-facing AI governance to manage compliance breaches, public backlash, and unexpected system failures 
• Implementing continuous AI risk assessments and performance audits to prevent agentic AI-related crises before they happen 

• Ensuring AI models adhere to Australia’s Privacy Act, GDPR, and international data protection laws 
• Implementing robust policies to manage customer and proprietary data, reducing the risk of AI-driven breaches and leaks 
• Best practices for data tagging, anonymisation, and controlled access to maintain integrity and compliance 

• Auditing proprietary datasets to detect compliance risks that could not expose the organisation to privacy breaches, regulatory scrutiny, or liability  
• Strengthening data security to protect against AI vulnerabilities by implementing encryption, access control, and monitoring  
• Enhancing fairness and explainability in AI models to remove hidden biases from historical data  
• Aligning AI-driven decision-making with ethical & transparent business practices 

• How can companies transform AI data governance from risk-management into a value-generating asset? 
• As AI-generated data increases, how can organisations maintain confidence in their data? 
• With evolving data privacy laws, how can organisations leverage AI without over-restricting access to valuable datasets? 
• What emerging technologies, like AI-powered audits, synthetic data, and & federated learning, will define the next decade of data governance? 

This interactive and hands-on masterclass is designed for governance and risk managers and executives seeking practical strategies to embed AI governance into their organisations. Participants will gain actionable insights on aligning AI adoption with regulatory expectations, corporate responsibility, and long-term business resilience. 

Through real-world case studies, interactive exercises, and expert-led discussions, attendees will develop a tailored AI governance and risk management roadmap that ensures AI transparency, accountability, and ethical implementation. 

Learning Outcomes: 

• Understanding AI governance principles to align AI adoption with Australian voluntary AI frameworks, industry standards, and emerging regulations 
• Developing a risk-based approach to AI implementation to mitigate security, compliance, and reputational risks before they become liabilities 
• Establishing internal governance structures to ensure AI accountability across business units, suppliers, and third-party partners 
• Identifying key compliance obligations under AI transparency requirements for federal agencies and corporate suppliers 
• Implementing AI risk assessment frameworks to evaluate AI model performance, data security, and ethical considerations 
• Practising real-world AI governance decision-making through interactive case studies and scenario-based exercises 
• Learning how to operationalise AI governance beyond theoretical principles to drive business value while managing risk 

This masterclass is an essential opportunity to gain clarity, confidence, and control over your AI governance strategy. By investing a single day, participants will leave with a practical, actionable framework to future-proof their AI adoption, protect their organisation, and maintain a competitive edge in an increasingly regulated AI landscape. 

This practical and strategy-focused masterclass is designed for leaders seeking to develop an AI roadmap that delivers measurable ROI and long-term scalability. Participants will gain insight into real-world AI implementation strategies, ensuring AI adoption is both financially viable and operationally effective. 

Through interactive exercises, expert-led discussions, and tailored strategy planning, attendees will leave with a customised AI roadmap that aligns with business priorities, investment capabilities, and future growth objectives. 

Learning outcomes 

• Defining AI business objectives to ensure AI investments are aligned with core business goals, customer needs, and operational priorities 
• Mapping AI opportunities across business functions to identify high-impact, high-value applications for immediate and long-term benefits. 
• Structuring an AI investment roadmap to balance innovation with cost control and financial sustainability 
• Developing a phased AI adoption strategy to minimise disruption, maximise efficiency, and support change management 
• Integrating AI with existing business workflows to enhance decision-making, streamline processes, and avoid costly technology overhauls 
• Measuring AI success through performance metrics that demonstrate ROI, scalability, and long-term business impact 
• Exploring monetisation opportunities by leveraging AI-driven insights, proprietary data, and personalised services for new revenue streams 

This masterclass is essential for business leaders looking to cut through the AI hype and develop a structured, results-driven AI strategy. By investing a single day, participants will gain the knowledge, frameworks, and strategic direction needed to ensure AI adoption that drives sustainable growth and competitive differentiation.