Agenda & Speakers

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

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

Enterprise software has traditionally been built for people to use. Now, AI agents are starting to make decisions and take action across systems, changing how software needs to be designed, built and managed.

This keynote explores how AI is reshaping enterprise technology, what engineering leaders need to prepare for and where to invest to stay ahead.

We’ll cover:

• How software and systems need to change as AI agents take on more tasks and decisions.
• What AI means for existing technology, engineering teams and how software is delivered.
• Where engineering leaders should invest now to prepare for the next three years.

MCP makes it much easier for agents to connect to enterprise systems and tools. That also means a poorly controlled server or connection can give an agent access to far more than the team originally intended.

This session looks at what changes when MCP moves from developer experimentation into a managed enterprise environment with real permissions, users and production systems behind it.

We'll cover:

• Who is allowed to add or approve an MCP server?
• How do existing user permissions carry through when an agent accesses another system?
• What needs to be logged, versioned and revoked when an MCP connection changes?

As more teams build with AI, they need many of the same things, access to models, tools, testing, deployment and security controls.

This panel explores what should be provided through a shared AI platform, and what should stay in the hands of individual teams.

We'll discuss:

• What should every developer be able to access easily?
• What should be standard across the organisation, and where should teams have flexibility?
• How do you make the platform useful without creating another bottleneck?

As more AI agents are deployed across an organisation, they need access to the same trusted information. Otherwise, different agents can give different answers to the same question.

This session looks at how one organisation built a shared context layer so agents can access consistent, up-to-date information without every team building its own solution.

Key takeaways:

• Decide what knowledge should be shared across all agents.
• Keep information current as systems and data change.
• Make sure agents only access information they're authorised to use.

Recharge with refreshments and structured networking with your peers.

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

As organisations deploy more AI agents, it becomes harder to know what is running, what each agent can access and how to stop one when something goes wrong.

This session looks at how one organisation built a central control layer to manage AI agents across the business.

Key takeaways:

• Discover and track AI agents across the organisation.
• Quickly pause, restrict or stop an agent when needed.
• Understand what systems, tools and data each agent can access.

Moving agentic AI from a small pilot into core business functions brings new challenges around scale, security, and governance. These challenges become even more important when AI agents begin working with sensitive financial, employee, and business data.

This case study explores how one organisation expanded agentic AI workflows across finance, HR, and other business functions, while putting the right controls in place and demonstrating measurable value.

• How they designed agentic workflows that could scale beyond the initial use case
• What guardrails and controls they introduced when agents began accessing sensitive business data
• The measurable impact on productivity, efficiency, and business outcomes after rollout

How a model is served now matters as much as which model you pick. Inference architecture (routing, serving, optimisation and hardware) increasingly decides latency, cost and reliability in production.

This panel looks at how engineering teams are designing the inference and model layer behind their AI applications.

We’ll discuss:

• When does a smaller, distilled or fine-tuned model beat a frontier model in production?
• How are teams using routing, caching, batching and GPU utilisation to balance latency and cost?
• How do you build a model layer that lets you swap models, providers or hardware without re-engineering your applications?

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.

Enjoy a complimentary lunch while connecting with fellow attendees.

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 models can change in performance over time as customer behaviour, data, and business conditions evolve. Without the right monitoring in place, these changes can go unnoticed until they begin affecting customers, operations, or revenue.

This case study explores how one team discovered a model drift issue that nearly became a significant business problem, and how they built better observability into their AI pipeline to identify and respond to issues earlier.

• How a model drift incident exposed gaps in their monitoring and almost resulted in a measurable business impact
• How they introduced the right metrics, alerts, and performance thresholds to identify model drift before it became a bigger problem
• How improved AI observability helped the team identify and respond to issues in days rather than weeks

Changing a prompt, model, retrieval source or tool can alter an AI system without breaking a normal unit test. Engineering teams need a repeatable way to know whether the new version is actually better, worse or simply different.

This session looks at how one team built evaluations into its development process and turned AI quality into something that can be tested before release.

We'll cover:

• What should sit in a useful evaluation set rather than a collection of hand-picked examples?
• How do you test outputs that do not have one objectively correct answer?
• What result should be bad enough to stop a release?

Engineering teams are making long-term infrastructure decisions in a market that changes every quarter. Today’s GPU commitments, platform choices and vendor deals could become tomorrow’s technical debt.

This think tank challenges attendees to defend the infrastructure bets they’re making now, and to admit which ones they’re least sure about.

This interactive session puts these questions directly to the audience. Participants vote live on a series of real-world scenarios, explore the results together, and vote again as perspectives shift through the discussion.

We’ll discuss:

• Is buying or reserving GPU capacity a smart move or an expensive mistake as model efficiency improves?
• Will most enterprises end up running their own models, or is self-hosting a distraction?
• Are today’s AI platforms and frameworks built to last, or will we be ripping them out within two years?
• How much vendor lock-in is acceptable in exchange for speed?
• What’s the infrastructure decision you made last year that you’d reverse today?

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

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

See you at Enterprise AI Global Sydney on 11 August 2027.

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

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

Most enterprises have bolted AI onto existing workflows and called it transformation. This keynote makes the case that real advantage comes from rebuilding the operating model itself: how decisions get made, how teams are structured, how work is sequenced, and how value is measured. Drawing on first-hand experience leading this shift, we will walk through what changed, what broke, and what leaders underestimated when AI stopped being a tool and became infrastructure for how the business runs.

We'll cover:

• Why treating AI as a productivity add-on caps its impact, and what it means to instead redesign processes, roles, and decision rights around it from the ground up.
• How team structures, reporting lines, and skill requirements have to shift when AI takes over parts of the workflow, and the change-management reality of getting people to work differently, not just faster.
• Moving beyond efficiency metrics to redefine what success looks like in an AI-native operating model, and how leadership should govern and audit decisions that are now partly automated.

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

"Enterprise-grade" has become a label everyone claims and few can define. This panel brings together senior leaders who've had to make the real call; what to deploy, what to hold back, and what standards actually matter when AI touches core business processes. Rather than another product showcase, the conversation will dig into the unglamorous but decisive factors: reliability at scale, security and compliance in regulated environments, integration with legacy systems, and the governance structures needed to trust AI-driven decisions.

• What governance, auditability, and accountability standards should organisations actually hold AI systems to now that they're embedded in critical workflows?
• Why do so many AI deployments stall between pilot and scale, and what separates the organisations that crossed that gap from the ones still stuck running demos.
• How procurement, vendor evaluation, and internal build-vs-buy decisions should look different in 2027 compared to the early hype cycle, and what red flags leaders have learned to spot.

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?

Recharge with refreshments and structured networking with your peers.

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?

AI isn't only arriving through sanctioned pilots; it's already inside the CRM, the ticketing system, the marketing platform, and dozens of other third-party tools your teams use every day, often switched on by default and invisible to IT and risk teams. This session looks at the real process of auditing a sprawling vendor stack to surface hidden AI features, the surprises they found along the way, and how they built a repeatable process for staying ahead of shadow AI rather than discovering it after the fact.

We'll explore:

• The audit approach used to systematically identify AI features buried in existing vendor tools, and the gaps between what procurement approved and what's actually running.
• What's actually at stake once hidden AI was found and how the team triaged and prioritised what to fix first.
• How the process was turned into an ongoing practice, including vendor questioning, contract language, and internal ownership, so shadow AI doesn't keep resurfacing.

Agentic AI promises systems that plan, decide, and act with far less human oversight than the chatbots and copilots that came before it, but most enterprises' infrastructure, governance, and culture weren't built for that. This panel brings together leaders grappling with the practical realities of agent readiness: who's accountable when an agent acts autonomously, what guardrails actually hold up under pressure, and what has to change in data, identity, and process architecture before agents can be trusted with real responsibility. Expect a grounded conversation about the difference between an impressive demo and an enterprise that's genuinely ready to let agents run.

• What "ready for agents" requires in data quality, system access, and identity management, and why most enterprises underestimate this foundational work.
• Who owns the outcome when an autonomous agent makes a consequential decision, and what governance and audit trails need to look like when humans are no longer in every loop.
• How organisations are sequencing agent rollout and what signals tell leaders an agent has earned more responsibility.

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.

Enjoy a complimentary lunch while connecting with fellow attendees.

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?

AI is changing what software is for, and by extension, what enterprises should be paying for. As AI agents absorb workflows once locked inside dedicated SaaS tools, and as vendors race to bolt AI features onto pricing models built for a pre-AI world, many organisations are sitting on a SaaS strategy that no longer reflects how work actually gets done. This think tank session gets participants working through the hard questions together: which tools in the stack are becoming redundant, which are becoming more valuable, and how procurement and IT should be rethinking vendor strategy before the decision gets made for them.

We'll discuss

• Which categories of SaaS tools are most exposed to being absorbed or displaced by AI capabilities, and the early warning signs leaders are missing.
• How AI add-ons and consumption-based pricing are reshaping vendor costs, and what enterprises should be renegotiating now rather than later.
• How to approach vendor consolidation and tool rationalisation strategically, so the SaaS strategy that emerges is built for an AI-native workflow rather than just a smaller version of the old one.

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

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

See you at Enterprise AI Global Sydney on 11 August 2027.