• Examining the transition from a collection-led data model to an AI-powered product-led model for internal optimization

• Defining the key performance indicators (KPIs) used to measure internal value creation, moving beyond infrastructure cost reduction

• Scalable insights for Australian markets to overcome fragmentation and technical architecture barriers

• Data and AI privacy and compliance priorities for 2026

• Which reforms matter most over the next year, and why

• Digital ID and CDR - managing consent, minimisation and trust in practice

• Oversight of digital tools and AI systems as they scale

• How leaders should think about data access, standards and competition impacts

Connect with fellow CDAOs, EGMs and data leaders to benchmark strategies and share challenges around AI adoption and data monetisation

• What will genuinely change how organisations create and prove value from data, analytics and AI over the next 12 months, and what’s just noise?

• Which governance or regulatory shifts should leaders be preparing for now, even if they’re not getting much attention yet?

• If budgets tighten, what data and AI investments should leaders protect, and which should they seriously question?

• If you were advising a board today, what would you tell them to stop doing, start doing and double down on?

• Leveraging an AI-driven generator to automate clinical documentation and resident aged care planning

• Emphasizing integrated data as the critical foundation to replace fragmented and siloed legacy spreadsheets

• Defining a precise project scope to maintain focus on specific functional and operational objectives

• Aligning transformation timing with the genuine appetite and readiness of the wider business stakeholders

Breakout A: How organisations move AI from pilots into production while delivering trusted, measurable business value

Breakout B: Why modern data governance is becoming a competitive advantage rather than a constraint

Breakout C: How leading organisations prove ROI from data, AI and technology investments

Breakout D: How organisations turn data into revenue while operating safely and responsibly

• What does a "product-led approach" to data monetisation actually look like in practice?

• What are the biggest challenges you've faced in turning data into tangible products, and how did you overcome them?

• How do you get buy-in from leadership and other teams to support a product-led data strategy?

• What's the most important lesson you'd share with other organisations looking to monetise their data?

• How operating models must evolve to support integrated, tech-driven business execution

• Why transformation can’t be siloed, and how to build the culture, mindset, and risk tolerance to support it

• The COO’s role in aligning people, process and platforms for speed without sacrificing governance

• Lessons learned from leading change across fast- and slow-moving environments in both public and private sectors

• Moving beyond personal AI productivity to deliver scalable, enterprise-wide value

• Establishing a robust Data foundation for responsible, sustainable adoption

• Creating advantage through early AI governance and Data literacy

• Capturing lessons learned and embedding AI literacy for career ready graduates

• Where has governance genuinely accelerated innovation in your organisation?

• What guardrails did you relax to move faster, and what did you tighten?

• How does governance change when data becomes a product, not just an asset?

• What advice would you give to teams redesigning governance for AI right now?

• Why conventional assurance and compliance approaches break down at modern delivery speeds

• How to turn risk appetite into clear, actionable, and enforceable guardrails for teams

• What it means to embed assurance into everyday delivery workflows

• What changed in practice and the pragmatic steps other organisations can apply immediately

• Use the Kite AI framework to embed equity and inclusion by establishing best practices for privacy and trust across the organization

• Balance innovation with strict governance guardrails by defining a risk appetite

• Elevate AI strategy from technical MLOps to a leadership-driven model centred on accountability and organizational assurance

• Scale sustainable AI practices by applying the UN-adopted abstraction framework to ensure long-term safety and business value

• Where have you successfully moved from "AI that talks" to "AI that acts”, and what was the specific trigger for that trust?

• When choosing an LLM, what is the one commercial "red flag" that makes you walk away from a model?

• What is the single biggest thing a leader can do to stop Risk and Commercial teams from working against each other?

• If you had to pick one "future-proof" investment to make today to be ready for 2030, what would it be?

• How nbn translated responsible AI principles and policy into practical, day-to-day delivery decisions across teams

• The operating models, guardrails and governance mechanisms that enabled innovation while maintaining trust and accountability

• What changed once responsible AI moved from a compliance exercise to a shared organisational capability

• Key lessons for leaders looking to embed responsible AI into real products, platforms and decision-making, not just frameworks

Trade small talk for real solutions. In this structured session, you will pitch your biggest work challenge to a small group of senior peers. Using key learnings from the summit’s speakers and case studies, your group will brainstorm practical, three-step plans to overcome the room’s toughest hurdles. You’ll leave with proven advice from people in your shoes, a clear action plan for Monday morning, and stronger professional connections.

• How can the CDO prove they are a "revenue centre" rather than a "cost centre"?

• What is the "Red Flag" that stops you from funding a pilot’s full-scale rollout?

• How do you distinguish between genuine AI value and "AI-washing" in a business case?

• What is the single most important KPI you want to see from the CDO?

• Prioritising a success model that balances internal efficiency with improved customer outcomes

• Moving beyond solely tracking financial metrics so both staff and customer impact remains a core measure of success

• Focusing on high-value use cases that drive tangible customer benefit