Sydney Finance Transformation and Technology Summit 2027

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

As AI-native infrastructure, real-time payments, and embedded finance mature, 2027 marks the year finance shifts from digitised-but-siloed systems to fully autonomous, interoperable financial ecosystems, reshaping how institutions operate and how customers experience money.

• AI agents move beyond chatbots and fraud alerts into autonomous roles: reconciling accounts, executing treasury decisions, and negotiating terms, forcing finance teams to redesign governance and human oversight models.
• Instant payments, real-time settlement, and continuous audit become the default rather than the exception, compressing cash cycles and raising customer expectations across banking, insurance, and capital markets.
• As finance becomes more automated and interconnected, institutions face heightened scrutiny on algorithmic accountability, data privacy, and systemic risk, making trust architecture a competitive differentiator rather than a compliance checkbox.

Despite record spending on ERP modernisation, most organisations in 2027 are still treating transformation as a technology swap rather than an operating model redesign, and the gap between investment and value is widening.

• Organisations often lift and shift legacy processes into new ERP platforms instead of redesigning workflows around the system's native capabilities, recreating old inefficiencies in new software and forfeiting most of the promised ROI.
• Technical rollout timelines dominate planning while user adoption, training, and data cleansing and governance are treated as afterthoughts, leading to poor data integrity, shadow systems, and employee workarounds post go-live.
• Many transformations lack a clear, empowered business owner accountable for outcomes, resulting in scope creep, vendor-driven decisions, and a failure to tie the ERP program to measurable business objectives.

As technology investment decisions increasingly determine competitive advantage, CFOs and finance leaders are stepping beyond budget oversight to take direct accountability for enterprise-wide technology strategy, vendor selection, and transformation outcomes, a shift redefining the boundaries of the finance function itself.

• Finance leaders are moving from approving technology spend after the fact to co-designing transformation roadmaps upfront, embedding ROI discipline and business-case rigour into decisions that were once the sole domain of IT and operations.
• As finance takes on oversight of AI, cloud, and platform investments, leaders face pressure to develop enough technical literacy to ask the right questions and challenge vendor claims, raising questions about talent, training, and where CFOs should draw the line.
• With finance now co-owning transformation success metrics, leaders are grappling with how to measure and report on technology outcomes with the same rigour traditionally reserved for financial performance, and what happens when a transformation underdelivers on their watch.

An industry-agnostic breakdown of how one finance team re-engineered its close process using automation and workflow redesign, offering a blueprint since adapted by teams in manufacturing, tech, and financial services.

• How process mining and time-tracking revealed the close was being slowed by manual handoffs and approval chains, not system limitations.
• Why automating reconciliations and journal entries first, rather than reporting, created compounding time savings, and how the team prioritised which tasks to automate versus redesign.
• How the close framework was stripped of company-specific dependencies so it could be picked up by finance teams with different systems, sizes, and regulatory environments.

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

A finance leader shares how they broke down years of fragmented reporting across ERP, CRM, and spreadsheet-based planning tools to build a single, trusted data layer, a challenge nearly every finance team faces regardless of industry.

• How the team identified that the issue wasn't a lack of data but a lack of shared definitions, ownership, and system-of-record clarity across departments.
• The practical tradeoffs weighed between building custom data infrastructure versus integrating existing platforms, and what actually drove the final call.
• How the team avoided the new dashboard, old habits trap by embedding the single source of truth into daily workflows and decision rights.

As finance teams adopt AI tools faster than governance frameworks can keep up, one leader developed a lightweight, industry-agnostic AI oversight model now being used as a template across finance functions navigating the same risk-versus-speed tension.

• How the framework avoided the trap of either blocking AI adoption entirely or approving tools without adequate scrutiny, striking a workable middle ground.
• Translating abstract principles such as explainability, auditability and data privacy into a simple checklist and approval workflow that non-technical finance staff could apply themselves.
• How the framework was built to flex across different jurisdictions and compliance regimes, making it exportable to finance teams operating under different regulatory pressures.

As finance systems become more interconnected through APIs, cloud ERPs, and third-party integrations, the attack surface for financial data has expanded dramatically. This panel explores how finance leaders are partnering with security teams to protect critical financial infrastructure without slowing transformation.

• Moving beyond siloed IT security ownership to shared accountability between CFOs and CISOs for financial data protection.
• Managing exposure as finance functions rely on growing networks of fintech vendors, cloud providers, and integration partners.
• How leaders avoid security becoming a bottleneck to real-time finance initiatives, without compromising on protection.

Small-group, discussion-based sessions where you'll work through real finance transformation 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.

With finance data scattered across ERPs, spreadsheets, and point solutions, one leader built a data standardisation approach that any finance team, regardless of industry or existing systems, can use to create a single source of financial truth.

• Why solving the fragmentation problem required agreeing on shared definitions and taxonomies before investing in new integration technology.
• How the solution worked with existing legacy systems instead of requiring a costly rip-and-replace, making it viable for organisations at different stages of technology maturity.
• Once standardised, how the same data foundation enabled forecasting and scenario planning that hadn't been possible before, turning a compliance fix into a strategic asset.

While finance leadership touts AI as a strategic priority, adoption on the ground remains slow and uneven, not due to a lack of tools but due to unresolved anxiety among finance professionals about job security, trust in AI outputs, and what their roles become next.

• Addressing the underlying anxiety that AI adoption threatens headcount, and reframing the conversation around how roles are shifting from transactional work toward analysis, judgment, and oversight, while being honest about where displacement is genuinely happening.
• Why reskilling programs often lag behind AI rollout timelines, leaving finance staff expected to supervise AI outputs without adequate training in data literacy, prompt evaluation, or model limitations.
• Creating a culture where finance professionals feel safe flagging AI errors or pushing back on automated outputs, rather than blindly trusting the system or rejecting it outright.

Financial planning and analysis is undergoing its biggest shift in decades, moving from static, backward-looking reporting toward continuous, forward-looking decision support powered by real-time data and predictive modelling. This panel looks at what that transition demands of people, process, and technology.

• What it takes to move an organisation away from quarterly and annual planning cycles toward rolling, real-time forecasting models.
• How FP&A teams are building self-service tools and dashboards that let business units act on financial insight without waiting on finance to interpret it.
• The shift from traditional financial modelling expertise toward data science literacy, storytelling, and cross-functional influence as core FP&A competencies.
• As AI-driven forecasts start informing real decisions, how leaders validate model accuracy, manage bias, and maintain human oversight without slowing decision-making.
• How the role is evolving from producing reports for leadership to actively partnering with sales, operations, and product teams to shape strategy in real time.

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 AI-native infrastructure, real-time payments, and embedded finance mature, 2027 marks the year finance shifts from digitised-but-siloed systems to fully autonomous, interoperable financial ecosystems, reshaping how institutions operate and how customers experience money.

• AI agents move beyond chatbots and fraud alerts into autonomous roles: reconciling accounts, executing treasury decisions, and negotiating terms, forcing finance teams to redesign governance and human oversight models.
• Instant payments, real-time settlement, and continuous audit become the default rather than the exception, compressing cash cycles and raising customer expectations across banking, insurance, and capital markets.
• As finance becomes more automated and interconnected, institutions face heightened scrutiny on algorithmic accountability, data privacy, and systemic risk, making trust architecture a competitive differentiator rather than a compliance checkbox.

Despite record spending on ERP modernisation, most organisations in 2027 are still treating transformation as a technology swap rather than an operating model redesign, and the gap between investment and value is widening.

• Organisations often lift and shift legacy processes into new ERP platforms instead of redesigning workflows around the system's native capabilities, recreating old inefficiencies in new software and forfeiting most of the promised ROI.
• Technical rollout timelines dominate planning while user adoption, training, and data cleansing and governance are treated as afterthoughts, leading to poor data integrity, shadow systems, and employee workarounds post go-live.
• Many transformations lack a clear, empowered business owner accountable for outcomes, resulting in scope creep, vendor-driven decisions, and a failure to tie the ERP program to measurable business objectives.

As technology investment decisions increasingly determine competitive advantage, CFOs and finance leaders are stepping beyond budget oversight to take direct accountability for enterprise-wide technology strategy, vendor selection, and transformation outcomes, a shift redefining the boundaries of the finance function itself.

• Finance leaders are moving from approving technology spend after the fact to co-designing transformation roadmaps upfront, embedding ROI discipline and business-case rigour into decisions that were once the sole domain of IT and operations.
• As finance takes on oversight of AI, cloud, and platform investments, leaders face pressure to develop enough technical literacy to ask the right questions and challenge vendor claims, raising questions about talent, training, and where CFOs should draw the line.
• With finance now co-owning transformation success metrics, leaders are grappling with how to measure and report on technology outcomes with the same rigour traditionally reserved for financial performance, and what happens when a transformation underdelivers on their watch.

An industry-agnostic breakdown of how one finance team re-engineered its close process using automation and workflow redesign, offering a blueprint since adapted by teams in manufacturing, tech, and financial services.

• How process mining and time-tracking revealed the close was being slowed by manual handoffs and approval chains, not system limitations.
• Why automating reconciliations and journal entries first, rather than reporting, created compounding time savings, and how the team prioritised which tasks to automate versus redesign.
• How the close framework was stripped of company-specific dependencies so it could be picked up by finance teams with different systems, sizes, and regulatory environments.

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

A finance leader shares how they broke down years of fragmented reporting across ERP, CRM, and spreadsheet-based planning tools to build a single, trusted data layer, a challenge nearly every finance team faces regardless of industry.

• How the team identified that the issue wasn't a lack of data but a lack of shared definitions, ownership, and system-of-record clarity across departments.
• The practical tradeoffs weighed between building custom data infrastructure versus integrating existing platforms, and what actually drove the final call.
• How the team avoided the new dashboard, old habits trap by embedding the single source of truth into daily workflows and decision rights.

As finance teams adopt AI tools faster than governance frameworks can keep up, one leader developed a lightweight, industry-agnostic AI oversight model now being used as a template across finance functions navigating the same risk-versus-speed tension.

• How the framework avoided the trap of either blocking AI adoption entirely or approving tools without adequate scrutiny, striking a workable middle ground.
• Translating abstract principles such as explainability, auditability and data privacy into a simple checklist and approval workflow that non-technical finance staff could apply themselves.
• How the framework was built to flex across different jurisdictions and compliance regimes, making it exportable to finance teams operating under different regulatory pressures.

As finance systems become more interconnected through APIs, cloud ERPs, and third-party integrations, the attack surface for financial data has expanded dramatically. This panel explores how finance leaders are partnering with security teams to protect critical financial infrastructure without slowing transformation.

• Moving beyond siloed IT security ownership to shared accountability between CFOs and CISOs for financial data protection.
• Managing exposure as finance functions rely on growing networks of fintech vendors, cloud providers, and integration partners.
• How leaders avoid security becoming a bottleneck to real-time finance initiatives, without compromising on protection.

Small-group, discussion-based sessions where you'll work through real finance transformation 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.

With finance data scattered across ERPs, spreadsheets, and point solutions, one leader built a data standardisation approach that any finance team, regardless of industry or existing systems, can use to create a single source of financial truth.

• Why solving the fragmentation problem required agreeing on shared definitions and taxonomies before investing in new integration technology.
• How the solution worked with existing legacy systems instead of requiring a costly rip-and-replace, making it viable for organisations at different stages of technology maturity.
• Once standardised, how the same data foundation enabled forecasting and scenario planning that hadn't been possible before, turning a compliance fix into a strategic asset.

While finance leadership touts AI as a strategic priority, adoption on the ground remains slow and uneven, not due to a lack of tools but due to unresolved anxiety among finance professionals about job security, trust in AI outputs, and what their roles become next.

• Addressing the underlying anxiety that AI adoption threatens headcount, and reframing the conversation around how roles are shifting from transactional work toward analysis, judgment, and oversight, while being honest about where displacement is genuinely happening.
• Why reskilling programs often lag behind AI rollout timelines, leaving finance staff expected to supervise AI outputs without adequate training in data literacy, prompt evaluation, or model limitations.
• Creating a culture where finance professionals feel safe flagging AI errors or pushing back on automated outputs, rather than blindly trusting the system or rejecting it outright.

Financial planning and analysis is undergoing its biggest shift in decades, moving from static, backward-looking reporting toward continuous, forward-looking decision support powered by real-time data and predictive modelling. This panel looks at what that transition demands of people, process, and technology.

• What it takes to move an organisation away from quarterly and annual planning cycles toward rolling, real-time forecasting models.
• How FP&A teams are building self-service tools and dashboards that let business units act on financial insight without waiting on finance to interpret it.
• The shift from traditional financial modelling expertise toward data science literacy, storytelling, and cross-functional influence as core FP&A competencies.
• As AI-driven forecasts start informing real decisions, how leaders validate model accuracy, manage bias, and maintain human oversight without slowing decision-making.
• How the role is evolving from producing reports for leadership to actively partnering with sales, operations, and product teams to shape strategy in real time.

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