Brisbane Enterprise AI and Automation 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.

Hear how an enterprise reduced fear, confusion and resistance around AI by addressing the concerns of employees, executives and customers with clear communication, visible safeguards and hands-on support.

• How the team identified where hesitation was coming from, whether it was job security, trust, unclear value or fear of change.
• What messages, education and engagement tactics helped executives, employees and customers feel more confident about AI adoption.
• The change management lessons other organisations can use to make AI feel useful, safe and worth embracing.

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

Hear how one organisation tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots.
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation.
• The lessons learned for other organisations trying to build trust in AI while managing regulation, privacy and internal risk expectations.

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 the 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.

Explore how leaders can evaluate AI programs honestly, recognise when a use case is underperforming, and make the hard call to pause, simplify or scale down before more time and budget are lost.

• The early warning signs that an AI program is not delivering, from weak adoption and poor data quality to unclear value and rising operational drag.
• How to build a practical review framework for deciding whether to fix, pause or stop a program, rather than endlessly funding a weak initiative.
• How to reframe scaling down as a strategic decision that frees resources for higher-value AI use cases.

This keynote will explore why education, understanding and training are essential to successful program adoption, and how organisations can equip both employees and customers to use new tools with confidence.

• Why training is not a tick-box activity, but a core enabler of adoption, value realisation and long-term program success.
• How to design learning that meets different needs across workforce roles, leadership levels and customer segments.
• What gets in the way of understanding new programs, from jargon and poor communication to lack of confidence and inconsistent support.
• How organisations can turn education into a competitive advantage by building trust, improving usage and reducing resistance to change.

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.

Hear how an enterprise reduced fear, confusion and resistance around AI by addressing the concerns of employees, executives and customers with clear communication, visible safeguards and hands-on support.

• How the team identified where hesitation was coming from, whether it was job security, trust, unclear value or fear of change.
• What messages, education and engagement tactics helped executives, employees and customers feel more confident about AI adoption.
• The change management lessons other organisations can use to make AI feel useful, safe and worth embracing.

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

Hear how one organisation tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots.
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation.
• The lessons learned for other organisations trying to build trust in AI while managing regulation, privacy and internal risk expectations.

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 the 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.

Explore how leaders can evaluate AI programs honestly, recognise when a use case is underperforming, and make the hard call to pause, simplify or scale down before more time and budget are lost.

• The early warning signs that an AI program is not delivering, from weak adoption and poor data quality to unclear value and rising operational drag.
• How to build a practical review framework for deciding whether to fix, pause or stop a program, rather than endlessly funding a weak initiative.
• How to reframe scaling down as a strategic decision that frees resources for higher-value AI use cases.

This keynote will explore why education, understanding and training are essential to successful program adoption, and how organisations can equip both employees and customers to use new tools with confidence.

• Why training is not a tick-box activity, but a core enabler of adoption, value realisation and long-term program success.
• How to design learning that meets different needs across workforce roles, leadership levels and customer segments.
• What gets in the way of understanding new programs, from jargon and poor communication to lack of confidence and inconsistent support.
• How organisations can turn education into a competitive advantage by building trust, improving usage and reducing resistance to change.

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!