Melbourne AI in Government Summit 2027

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

As Australian government agencies accelerate AI adoption across service delivery, policy, and operations, this opening keynote examines why the private sector's "move fast and iterate" approach to AI doesn't transfer cleanly to government, where public trust, once eroded, is far harder to rebuild than in a commercial relationship.

• Understanding the heightened scrutiny and lower tolerance for error that comes with government's unique position of compulsory service and public accountability.
• How agencies are balancing genuine caution earned from past automated decision-making controversies against the risk of falling behind on AI's genuine benefits for citizens.
• What responsible AI adoption actually looks like at government scale, and how agencies build citizen trust into AI systems from the ground up rather than retrofitting it after deployment.

Cutting through both the hype and the scepticism surrounding AI in government, this keynote examines where AI has demonstrably improved how citizens interact with government services, and honestly assesses where high-profile pilots have failed to deliver on their promise.

• Evidence-based examples of AI deployments across federal, state, and local government that have measurably improved service speed, accuracy, or accessibility for citizens.
• Honest analysis of common failure patterns, from underestimating data quality issues to overestimating what off-the-shelf AI tools can handle in complex policy contexts.
• How agencies are ensuring AI-driven efficiency gains don't come at the cost of accessibility for vulnerable or digitally excluded populations.

As AI increasingly informs decisions affecting welfare, compliance, and citizen services, this panel brings together public sector leaders, legal experts, and digital transformation heads to examine where accountability genuinely sits when an algorithmic decision causes harm, and whether current governance structures are equal to the task.

• What Australia's history with automated government decisions reveals about the gaps in oversight, escalation, and review that allowed harm to go undetected for too long.
• How responsibility is being clarified between the agencies deploying AI systems, the vendors building them, and the ministers ultimately accountable to Parliament.
• What genuine, usable pathways for citizens to challenge or seek review of an AI-influenced decision look like in practice, beyond a theoretical right to complain.

In the wake of the Robodebt Royal Commission's findings on the human cost of unchecked automated debt recovery, one agency leader was tasked with rebuilding an automated compliance and eligibility system from the ground up, this time with the safeguards, oversight, and legal defensibility the original system never had.

• How the team translated recommendations around procedural fairness and human oversight into concrete technical and process requirements, rather than treating them as a compliance memo.
• The specific design choices that ensured a caseworker could actually scrutinise and override an automated recommendation, rather than facing pressure to simply approve high volumes of AI-generated decisions.
• How the agency communicated the changes to affected communities and advocacy groups who had every reason to distrust automated government decision-making.

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

Facing a procurement process so slow that approved AI vendors were often outdated by the time contracts were signed, one procurement leader redesigned the process to move at a pace that matched the technology, without abandoning the probity and scrutiny government contracting demands.

• How the team distinguished essential probity checkpoints from legacy process steps that no longer served their original purpose.
• How contract structures were redesigned to accommodate vendor capability changes without requiring a full re-tender.
• How the team brought traditionally cautious stakeholders into the redesign early, so the faster process had their genuine buy-in rather than their reluctant compliance.

Concerned about data sovereignty and long-term dependency on a small number of global AI vendors, one agency capability leader built an internal AI capability uplift programme across the APS workforce, reducing reliance on external vendors for the agency's most sensitive AI use cases.

• How the team mapped the extent of dependency on vendor-hosted models and infrastructure for functions handling sensitive citizen data.
• How the team prioritised building in-house or locally-hosted capability for the highest-sensitivity use cases, while continuing to use commercial vendors elsewhere.
• How the programme built genuine technical depth through partnerships with universities and cross-agency secondments, rather than one-off training that left long-term capability unchanged.

As government services increasingly channel citizens toward AI-powered digital interfaces, this panel examines how agencies are ensuring efficiency gains don't come at the expense of accessibility for older Australians, people with disability, remote communities, and others who face barriers to digital engagement.

• How agencies are building visibility into which citizen segments are struggling to access AI-driven services, rather than relying on aggregate satisfaction metrics that mask exclusion.
• The difference between a phone number that exists on paper and a human-assisted channel that's actually resourced and accessible when digital self-service fails.
• How agencies are managing the tension between budget pressure to shift citizens toward cheaper AI-driven channels and their obligation to serve everyone equitably.

Small-group, discussion-based sessions where you'll work through real public sector 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.

Facing the Commonwealth Ombudsman's guidance that automated decisions affecting entitlements must be genuinely explainable and challengeable, one agency's digital transformation leader built a payment eligibility system designed for explainability from day one, avoiding the pattern that drew scrutiny in past automated compliance programmes.

• How the team used the Ombudsman's published expectations on lawfulness and transparency as an active design constraint throughout development, not a final compliance check.
• Where the team deliberately selected a simpler, more transparent model over a marginally more accurate but harder-to-explain alternative, given what was at stake for citizens.
• How the team validated that decision explanations were genuinely comprehensible to affected citizens and the legal aid services who would help them challenge decisions.

With government agencies increasingly using AI to inform or make decisions that affect welfare payments, visa outcomes, compliance actions, and other high-stakes determinations, this keynote examines what genuine algorithmic accountability requires when the decision-maker is a system rather than a caseworker.

• Drawing lessons from past automated decision-making controversies in the Australian public sector about what oversight and safeguards were missing.
• What government agencies are building to ensure AI-assisted decisions can be genuinely explained and challenged, not just technically justified.
• How agencies are designing human-in-the-loop processes that provide genuine scrutiny of AI recommendations, rather than a token review step that simply approves whatever the system suggests.

As agencies increasingly rely on external vendors to build, deploy, and even explain the AI systems used in government, this interactive discussion examines what it will take to build genuine internal capability, so agencies can understand, govern, and eventually reduce dependency on the vendors delivering their AI systems.

• How far behind public sector AI literacy and technical capability sits relative to the pace of deployment, and what's driving the gap.
• How agencies are building enough internal technical fluency to genuinely scrutinise vendor claims, rather than accepting them at face value due to capability gaps.
• Longer-term strategies agencies are pursuing to build durable internal expertise, from targeted recruitment to partnerships with universities and shared capability across agencies.

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 Australian government agencies accelerate AI adoption across service delivery, policy, and operations, this opening keynote examines why the private sector's "move fast and iterate" approach to AI doesn't transfer cleanly to government, where public trust, once eroded, is far harder to rebuild than in a commercial relationship.

• Understanding the heightened scrutiny and lower tolerance for error that comes with government's unique position of compulsory service and public accountability.
• How agencies are balancing genuine caution earned from past automated decision-making controversies against the risk of falling behind on AI's genuine benefits for citizens.
• What responsible AI adoption actually looks like at government scale, and how agencies build citizen trust into AI systems from the ground up rather than retrofitting it after deployment.

Cutting through both the hype and the scepticism surrounding AI in government, this keynote examines where AI has demonstrably improved how citizens interact with government services, and honestly assesses where high-profile pilots have failed to deliver on their promise.

• Evidence-based examples of AI deployments across federal, state, and local government that have measurably improved service speed, accuracy, or accessibility for citizens.
• Honest analysis of common failure patterns, from underestimating data quality issues to overestimating what off-the-shelf AI tools can handle in complex policy contexts.
• How agencies are ensuring AI-driven efficiency gains don't come at the cost of accessibility for vulnerable or digitally excluded populations.

As AI increasingly informs decisions affecting welfare, compliance, and citizen services, this panel brings together public sector leaders, legal experts, and digital transformation heads to examine where accountability genuinely sits when an algorithmic decision causes harm, and whether current governance structures are equal to the task.

• What Australia's history with automated government decisions reveals about the gaps in oversight, escalation, and review that allowed harm to go undetected for too long.
• How responsibility is being clarified between the agencies deploying AI systems, the vendors building them, and the ministers ultimately accountable to Parliament.
• What genuine, usable pathways for citizens to challenge or seek review of an AI-influenced decision look like in practice, beyond a theoretical right to complain.

In the wake of the Robodebt Royal Commission's findings on the human cost of unchecked automated debt recovery, one agency leader was tasked with rebuilding an automated compliance and eligibility system from the ground up, this time with the safeguards, oversight, and legal defensibility the original system never had.

• How the team translated recommendations around procedural fairness and human oversight into concrete technical and process requirements, rather than treating them as a compliance memo.
• The specific design choices that ensured a caseworker could actually scrutinise and override an automated recommendation, rather than facing pressure to simply approve high volumes of AI-generated decisions.
• How the agency communicated the changes to affected communities and advocacy groups who had every reason to distrust automated government decision-making.

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

Facing a procurement process so slow that approved AI vendors were often outdated by the time contracts were signed, one procurement leader redesigned the process to move at a pace that matched the technology, without abandoning the probity and scrutiny government contracting demands.

• How the team distinguished essential probity checkpoints from legacy process steps that no longer served their original purpose.
• How contract structures were redesigned to accommodate vendor capability changes without requiring a full re-tender.
• How the team brought traditionally cautious stakeholders into the redesign early, so the faster process had their genuine buy-in rather than their reluctant compliance.

Concerned about data sovereignty and long-term dependency on a small number of global AI vendors, one agency capability leader built an internal AI capability uplift programme across the APS workforce, reducing reliance on external vendors for the agency's most sensitive AI use cases.

• How the team mapped the extent of dependency on vendor-hosted models and infrastructure for functions handling sensitive citizen data.
• How the team prioritised building in-house or locally-hosted capability for the highest-sensitivity use cases, while continuing to use commercial vendors elsewhere.
• How the programme built genuine technical depth through partnerships with universities and cross-agency secondments, rather than one-off training that left long-term capability unchanged.

As government services increasingly channel citizens toward AI-powered digital interfaces, this panel examines how agencies are ensuring efficiency gains don't come at the expense of accessibility for older Australians, people with disability, remote communities, and others who face barriers to digital engagement.

• How agencies are building visibility into which citizen segments are struggling to access AI-driven services, rather than relying on aggregate satisfaction metrics that mask exclusion.
• The difference between a phone number that exists on paper and a human-assisted channel that's actually resourced and accessible when digital self-service fails.
• How agencies are managing the tension between budget pressure to shift citizens toward cheaper AI-driven channels and their obligation to serve everyone equitably.

Small-group, discussion-based sessions where you'll work through real public sector 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.

Facing the Commonwealth Ombudsman's guidance that automated decisions affecting entitlements must be genuinely explainable and challengeable, one agency's digital transformation leader built a payment eligibility system designed for explainability from day one, avoiding the pattern that drew scrutiny in past automated compliance programmes.

• How the team used the Ombudsman's published expectations on lawfulness and transparency as an active design constraint throughout development, not a final compliance check.
• Where the team deliberately selected a simpler, more transparent model over a marginally more accurate but harder-to-explain alternative, given what was at stake for citizens.
• How the team validated that decision explanations were genuinely comprehensible to affected citizens and the legal aid services who would help them challenge decisions.

With government agencies increasingly using AI to inform or make decisions that affect welfare payments, visa outcomes, compliance actions, and other high-stakes determinations, this keynote examines what genuine algorithmic accountability requires when the decision-maker is a system rather than a caseworker.

• Drawing lessons from past automated decision-making controversies in the Australian public sector about what oversight and safeguards were missing.
• What government agencies are building to ensure AI-assisted decisions can be genuinely explained and challenged, not just technically justified.
• How agencies are designing human-in-the-loop processes that provide genuine scrutiny of AI recommendations, rather than a token review step that simply approves whatever the system suggests.

As agencies increasingly rely on external vendors to build, deploy, and even explain the AI systems used in government, this interactive discussion examines what it will take to build genuine internal capability, so agencies can understand, govern, and eventually reduce dependency on the vendors delivering their AI systems.

• How far behind public sector AI literacy and technical capability sits relative to the pace of deployment, and what's driving the gap.
• How agencies are building enough internal technical fluency to genuinely scrutinise vendor claims, rather than accepting them at face value due to capability gaps.
• Longer-term strategies agencies are pursuing to build durable internal expertise, from targeted recruitment to partnerships with universities and shared capability across agencies.

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