About the summit
Now in its third year, the Enterprise AI Global Summit is APAC’s only event built specifically to bring enterprise AI strategy, transformation, engineering and infrastructure together under one roof, with events in Sydney and Melbourne. Enterprise AI is no longer owned by one team. The leaders deciding where to invest, how to govern AI and where it can transform the business now depend on the engineers building the models, agents, platforms and infrastructure needed to make it work at scale. In 2027, the AI Engineering & Infrastructure Summit and the Enterprise AI Summit come together as two dedicated streams within one event. The Enterprise AI stream covers AI adoption, operating models, governance, workforce transformation and proving business value. The AI Engineering & Infrastructure stream covers agents, models, data, inference, platforms and the infrastructure required to run AI in production. Until this summit, the people setting enterprise AI strategy and the teams responsible for delivering it attended separate events, never the same room. The Enterprise AI Global Summit is that room. Across Sydney and Melbourne, Chief AI Officers, CIOs, Chief Data Officers, transformation leaders, Heads of AI, platform leaders, architects and engineers come together to compare programs directly with their peers. Make sure you are part of the discussion at APAC’s only event bringing the strategy and technology behind enterprise AI together under one roof.
Who attends
- Chief AI Officer
- VP / Head of AI
- Chief Data Officer
- Head of ML Engineering
- Director of AI Strategy
- Head of Innovation
What the programme covers
- GenAI at ScaleMoving GenAI from prototype to production-grade systems that remain reliable under real enterprise workloads.
- Agentic WorkflowsMulti-agent orchestration, tool use and the operating layer underneath.
- Data FoundationsRAG, embeddings, semantic layers and the data architecture behind enterprise AI.
- AI Infrastructure and EconomicsBalancing compute, inference, performance and cost as AI workloads scale.
- AI GovernanceModel risk, policy, accountability and the evidence regulators and boards expect.
- AI Operating ModelsDeciding who owns AI, how central teams work with the business and where decisions get made.
- AI Adoption and Workforce TransformationRedesigning work, building adoption and preparing teams for AI across the enterprise.
- AI Value and ROIPrioritising the right use cases, moving beyond pilots and proving measurable business value.