Brisbane Enterprise AI Summit 2027

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

As enterprise AI moves beyond experimentation, this session will examine how leading organisations are shifting from isolated use cases to enterprise-wide operating models that connect data, governance, automation and accountability to deliver measurable business value at scale.

• How AI leaders are moving from fragmented pilots to enterprise AI operating models that support scale, speed and measurable outcomes.
• What organisations must align across data, risk, technology and business teams to embed trusted AI and automation into core workflows.
• Where 2027 will separate leaders from laggards, from agent-enabled workflows and AI governance to workforce redesign and ROI accountability.

This keynote will explore how evolving Australian standards, regulation and policy expectations are reshaping how enterprises design, govern and deploy AI with confidence.

• What the latest Australian policy direction means for enterprise AI governance, accountability and risk management.
• How standards and compliance requirements are influencing data use, model oversight and vendor selection.
• What leaders should do now to build AI systems that are auditable, responsible and ready for change.

This panel will examine why so many AI initiatives stall after the pilot stage, and what enterprises need to get right in data, governance, operating model and talent to turn promising experiments into durable production capability.

• What changes when AI moves from sandbox to scale, from proof-of-concept enthusiasm to production-grade reliability, support and accountability.
• How resource gaps differ between large enterprises and organisations early in their AI journey, and what each group can realistically prioritise next.
• Where leaders should invest first when budgets, skills and internal support are uneven, so progress does not depend on a handful of AI champions.

AI agents can now read documents, apply policy, update systems and trigger actions rather than simply recommend what a person should do. The difficult decision is where autonomy stops and human authority begins.This session looks at how one organisation set those boundaries in practice, including which actions agents can complete themselves and which deliberately still require someone to approve them.

• Which decisions or actions are safe enough to delegate completely?
• Where should approval limits or human sign-off remain in place?
• What looked suitable for autonomous agents but was rejected once the consequences were tested?

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

As AI moves from experimentation into everyday business operations, organisations are discovering that managing AI costs is very different from managing traditional software spend. More users, longer prompts, reasoning models, agents and tool calls can drive consumption, often without a corresponding purchasing decision.

This case study explores how one organisation approached the challenge of making AI consumption visible, measurable and accountable as adoption scaled.

• What made AI spend difficult to understand and manage as usage grew?
• How did the organisation track, allocate and govern AI consumption across teams and use cases?
• What worked, what didn’t, and how did the organisation decide when to cap usage, challenge consumption or switch to a more cost-effective model?

Learn how simplifying a crowded AI tool landscape, reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organisations can identify overlapping platforms, duplicated workflows and hidden waste across its AI and automation stack.
• The practical steps needed to rationalise tools, streamline ownership and reduce confusion for business and technology teams.
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability.

This panel will explore how organisations can bring employees, executives and customers along the AI journey by aligning communication, trust, training and leadership at every stage of adoption.

• How to build executive confidence in AI programs so leaders champion change.
• What employees need to hear, learn and experience for AI adoption to feel useful, safe and relevant to their day-to-day work.
• How to manage customer expectations when AI changes service, decisioning or engagement models, without damaging trust.
• The change management practices that help AI programs stick, from clear ownership and phased rollout to feedback loops and measurable adoption.

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

AI and automated systems are increasingly influencing decisions about customers, employees and citizens, creating a new challenge for organisations as privacy transparency requirements take effect from 10 December 2026.

This case study explores how one organisation identified where automated decision-making was already occurring, assessed which decisions and personal information fell within scope, and built the evidence needed to support accurate and defensible privacy disclosures as systems and models evolve.

• How did the team identify where automated or AI-assisted decisions were already influencing people across the business?
• How to determine which decisions, personal information and systems need to be considered under the new requirements.
• What processes and documentation were put in place to keep disclosures accurate as models, systems and decision-making processes change?

Customer-facing AI can now handle increasingly complex conversations, but some interactions involve vulnerability, hardship, complaints, financial decisions or situations where getting the answer wrong has real consequences.This session looks at how one organisation designed the boundary between automated assistance and human intervention, and how those escalation rules work once the service is live.

• Which customer situations should trigger an immediate handover?
• How does the AI recognise that the conversation has moved beyond what it should handle?
• What happens when the AI technically completes the task but a human should have stepped in earlier?

This think tank will examine how organisations can align AI priorities, governance and execution across teams, and how to make responsibility for each program clear from the start.

• How to define ownership for AI programs so business, technology, data and risk leaders know who is accountable at each stage.
• What governance model helps avoid duplication, competing priorities and shadow AI initiatives across the enterprise.
• How leadership teams can set shared standards for data, security, compliance and value tracking without slowing delivery.
• What practical decision rights should sit with central teams versus business units when AI programs move from idea to implementation.

Unwind with your peers for a couple of drinks on us!

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

As enterprise AI moves beyond experimentation, this session will examine how leading organisations are shifting from isolated use cases to enterprise-wide operating models that connect data, governance, automation and accountability to deliver measurable business value at scale.

• How AI leaders are moving from fragmented pilots to enterprise AI operating models that support scale, speed and measurable outcomes.
• What organisations must align across data, risk, technology and business teams to embed trusted AI and automation into core workflows.
• Where 2027 will separate leaders from laggards, from agent-enabled workflows and AI governance to workforce redesign and ROI accountability.

This keynote will explore how evolving Australian standards, regulation and policy expectations are reshaping how enterprises design, govern and deploy AI with confidence.

• What the latest Australian policy direction means for enterprise AI governance, accountability and risk management.
• How standards and compliance requirements are influencing data use, model oversight and vendor selection.
• What leaders should do now to build AI systems that are auditable, responsible and ready for change.

This panel will examine why so many AI initiatives stall after the pilot stage, and what enterprises need to get right in data, governance, operating model and talent to turn promising experiments into durable production capability.

• What changes when AI moves from sandbox to scale, from proof-of-concept enthusiasm to production-grade reliability, support and accountability.
• How resource gaps differ between large enterprises and organisations early in their AI journey, and what each group can realistically prioritise next.
• Where leaders should invest first when budgets, skills and internal support are uneven, so progress does not depend on a handful of AI champions.

AI agents can now read documents, apply policy, update systems and trigger actions rather than simply recommend what a person should do. The difficult decision is where autonomy stops and human authority begins.This session looks at how one organisation set those boundaries in practice, including which actions agents can complete themselves and which deliberately still require someone to approve them.

• Which decisions or actions are safe enough to delegate completely?
• Where should approval limits or human sign-off remain in place?
• What looked suitable for autonomous agents but was rejected once the consequences were tested?

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

As AI moves from experimentation into everyday business operations, organisations are discovering that managing AI costs is very different from managing traditional software spend. More users, longer prompts, reasoning models, agents and tool calls can drive consumption, often without a corresponding purchasing decision.

This case study explores how one organisation approached the challenge of making AI consumption visible, measurable and accountable as adoption scaled.

• What made AI spend difficult to understand and manage as usage grew?
• How did the organisation track, allocate and govern AI consumption across teams and use cases?
• What worked, what didn’t, and how did the organisation decide when to cap usage, challenge consumption or switch to a more cost-effective model?

Learn how simplifying a crowded AI tool landscape, reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organisations can identify overlapping platforms, duplicated workflows and hidden waste across its AI and automation stack.
• The practical steps needed to rationalise tools, streamline ownership and reduce confusion for business and technology teams.
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability.

This panel will explore how organisations can bring employees, executives and customers along the AI journey by aligning communication, trust, training and leadership at every stage of adoption.

• How to build executive confidence in AI programs so leaders champion change.
• What employees need to hear, learn and experience for AI adoption to feel useful, safe and relevant to their day-to-day work.
• How to manage customer expectations when AI changes service, decisioning or engagement models, without damaging trust.
• The change management practices that help AI programs stick, from clear ownership and phased rollout to feedback loops and measurable adoption.

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

AI and automated systems are increasingly influencing decisions about customers, employees and citizens, creating a new challenge for organisations as privacy transparency requirements take effect from 10 December 2026.

This case study explores how one organisation identified where automated decision-making was already occurring, assessed which decisions and personal information fell within scope, and built the evidence needed to support accurate and defensible privacy disclosures as systems and models evolve.

• How did the team identify where automated or AI-assisted decisions were already influencing people across the business?
• How to determine which decisions, personal information and systems need to be considered under the new requirements.
• What processes and documentation were put in place to keep disclosures accurate as models, systems and decision-making processes change?

Customer-facing AI can now handle increasingly complex conversations, but some interactions involve vulnerability, hardship, complaints, financial decisions or situations where getting the answer wrong has real consequences.This session looks at how one organisation designed the boundary between automated assistance and human intervention, and how those escalation rules work once the service is live.

• Which customer situations should trigger an immediate handover?
• How does the AI recognise that the conversation has moved beyond what it should handle?
• What happens when the AI technically completes the task but a human should have stepped in earlier?

This think tank will examine how organisations can align AI priorities, governance and execution across teams, and how to make responsibility for each program clear from the start.

• How to define ownership for AI programs so business, technology, data and risk leaders know who is accountable at each stage.
• What governance model helps avoid duplication, competing priorities and shadow AI initiatives across the enterprise.
• How leadership teams can set shared standards for data, security, compliance and value tracking without slowing delivery.
• What practical decision rights should sit with central teams versus business units when AI programs move from idea to implementation.

Unwind with your peers for a couple of drinks on us!