CDAIO Sydney schedule

During this 5-minute networking session, participants can build their network. Have fun!

• As AI accelerates across the enterprise, how is the role of the CDAO evolving?
• What responsibilities should sit within the data and AI function—and what should not?
• How can leaders balance innovation, governance, value creation, and business partnership?
• What capabilities, operating models, and leadership skills will define successful CDAIOs over the next five years?

Speakers
Sid Baidwan Chief AI and Data Officer Colonial First State
Harpreet Khalsa Chief Data Officer Edith Cowan University 

• Which business processes are ready for full autonomy?
• When should humans remain in the loop?
• How do organisations govern autonomous decision making?
• What operating models emerge when AI becomes a digital workforce? 

As organisations scale AI, many are realising the economics behave very differently than expected—where inference cost explosion, token consumption, and open access can quickly lead to uncontrolled spend. This panel brings together data and AI leaders to explore the real cost drivers of AI, the trade-offs between model choices and infrastructure, and the guardrails needed to prevent missteps—while still enabling innovation at pace. At its core, this is a leadership challenge of maintaining the right balance between cost, risk, and value as AI adoption accelerates.

Key questions:

• Where are organisations overspending on AI today, and which levers actually make a meaningful difference in controlling cost?
• How are leaders managing inference cost explosion and token consumption without slowing innovation?
• What guardrails and controls are needed to prevent uncontrolled enterprise-wide AI spend?
• How are organisations measuring and demonstrating tangible business value from AI investments, and which use cases are delivering the strongest returns?

Speakers
Michael J Brown GM | Applied Intelligence Queensland Airports
Joanna Knox Executive Data & AI Telstra
Dr Tom Gao Chief Technology and Digital Services Officer City of Sydney
Jeremy Hubbard Chief Data and Technology Officer Rest 

• How can organisations reduce the environmental impact of AI workloads without limiting growth?
• What metrics should leaders track when assessing AI sustainability?
• How do organisations balance performance, cost, and environmental responsibility? 

• Creating an environment where trusted data can thrive
• Embedding quality, governance, and accountability across the organisation
• Transforming data from a by-product into a strategic asset
• Enabling better decisions through stronger data foundations

• No data, no AI? How synthetic data is unlocking innovation where privacy, regulation, or limited datasets create roadblocks
• Can you trust what isn't real? Addressing quality, accuracy, and bias when training AI models with synthetic data
• From experimentation to enterprise scale: How leading organisations are using synthetic data to accelerate AI development, testing, and deployment 

• Has AI increased the importance of trusted customer, product and operational data?
• How are organisations modernising MDM approaches for highly distributed environments?
• What business risks emerge when core entities cannot be trusted?
• What does successful MDM look like in 2027?

• Measuring AI's true business impact
• Balancing innovation ambitions with governance requirements
• Understanding the hidden costs and risks of AI adoption
• Building a sustainable framework for value creation at scale 

• As foundation models become increasingly accessible, where does sustainable competitive advantage come from?
• How can organisations identify and monetise unique data assets?
• What strategies are leading companies using to build defensible AI capabilities around proprietary data?

• Why do organisations continue to struggle with accountability for data?
• How can business-led ownership improve trust, quality and compliance?
• What governance models create ownership without creating bureaucracy? 

• What behaviours distinguish truly data-driven organisations?
• Why do transformation programmes often fail to change decision-making habits?
• How can leaders embed data and AI into everyday business processes and incentives?

• Why is metadata emerging as the connective tissue between governance, quality, discovery, and AI readiness?
• How can organisations use metadata to improve trust, accessibility, and understanding of enterprise data assets?
• What capabilities are required to scale metadata management across increasingly complex data environments? 

• How was the agent designed around a real business problem rather than a technology demonstration?
• What data, workflows and governance foundations were required for success?
• How did the organisation measure adoption, value and business outcomes?

• Why do so many AI initiatives achieve technical success but struggle to change behaviours?
• What separates organisations that achieve widespread adoption from those stuck in pilot mode?
• How should leaders measure adoption beyond simple usage metrics?
• What change management approaches are proving most effective for AI?

• How AI is reshaping executive decision-making
• What leadership capabilities become more important as AI adoption grows
• Balancing experience, intuition and AI recommendations
• Building AI-ready leadership teams

• Defining success beyond adoption and experimentation
• Measuring business impact, productivity, and ROI
• Tracking value creation across teams and functions
• Aligning AI outcomes with strategic objectives 

• Explore practical examples of how your organisation can turn data governance strategy into business value, delivering measurable impact and accelerating AI readiness.
• Case Study 1: Shifting from data teams being responsible for data management to leaders accountable for business outcomes
• Case Study 2: Scaling Data Governance for Impact

• How are organisations creating new revenue streams and business models from their data assets?
• What differentiates successful data monetisation strategies from those that fail to deliver value?
• How can leaders balance commercial opportunities with customer trust, privacy, and governance obligations?

• Why is more value now being created through data foundations rather than report production?
• How can data teams improve the quality, structure and connectivity of enterprise data assets?
• What capabilities should employers prioritise when hiring and developing future data talent?

• Why treating data as a product - not a project - is essential for scaling analytics and AI
• Building product thinking into data: ownership, accountability, user experience, and value creation
• How data products are breaking down silos and enabling faster, self-service access to trusted data 

• How should organisations think about data as a supply chain rather than a collection of systems?
• Where are the biggest bottlenecks impacting trust, quality and accessibility?
• What practices improve resilience across the end-to-end data lifecycle? 

• Why is enterprise knowledge often fragmented across systems, teams, and individuals?
• How can organisations transform documents, conversations, and expertise into AI-ready knowledge assets?
• What governance and ownership models are needed to maintain trusted knowledge at scale?

Speakers
Venkata Valluri General Manager Data and Analytics Aussie Broadband Limited
Chantal Velasque Head of Data & Analytics Uniting
Sam Stark Head of Capabilities – Data & AI Endeavour Group
Shikha Gherlenda Global Head of Technology & Enterprise Data & AI Governance
Lendlease 

• What Sovereign AI means in practice - and why it matters now
• The growing importance of sovereign compute and AI infrastructure
• Navigating data sovereignty in an increasingly complex regulatory landscape
• Can AI growth and sustainability coexist?
• The economic winners and losers of AI at scale

• What evidence shows that a data culture is genuinely taking hold?
• As AI becomes embedded in critical decisions, how can organisations establish end-to-end visibility across data, models, and outcomes?
• What role does data lineage play in improving trust, governance, and regulatory compliance?
• How can leaders balance transparency requirements with the complexity of modern data architectures and AI ecosystems? 

• How do successful teams secure stakeholder buy-in and organisational commitment?
• What role do business champions play in driving adoption and value realisation?
• How can technology leaders communicate outcomes in language that resonates with executives?

Speakers
Aman Dayal GM - Data, AI, Architecture Coates
Kate Carruthers Lecturer AGSM @ UNSW Business School
Joshua Rutland Head of Data Trust & Products Bendigo Bank 
David Silvestre Director - Data and Digital Bupa Villages and Aged Care Australia 

• Data architecture for AI
• Governance as an enabler, not a constraint
• Data quality and accessibility
• Building trusted data products 

• What practical actions have proven effective in improving gender diversity and inclusion?
• Which common talent challenges are organisations still failing to address?
• How can leaders recruit, retain and develop the next generation of data and AI professionals?

Speakers
Andrew Spiegelman Head of Data & Analytics Service NSW 

• The AI attack surface is expanding: How organisations can protect models, agents, and sensitive data from emerging threats
• From prompt injection to model poisoning: Understanding the most critical AI security risks and vulnerabilities
• Securing autonomous AI agents while maintaining trust, governance, and human oversight
• Building an AI security strategy that keeps pace with rapid innovation without slowing business adoption

• How can organisations extract value from documents, emails, images, video, and audio?
• What are the challenges of governing and securing unstructured data for AI use?
• Which use cases are delivering measurable business impact from previously untapped information?

• How should responsibilities be divided between business, technology, data and AI teams?
• Which governance models best support innovation at scale?
• How can organisations avoid fragmented AI ownership?
• What operating structures are emerging among leading enterprises?