Queensland Treasury Technographics

Discover the latest software purchases and digital transformation initiatives being undertaken by Queensland Treasury and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 1541 Queensland Treasury employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.

During our research, we have identified that Queensland Treasury has purchased the following applications: Aurion HR Employee Self Service for Employee Self Service in 2021, SAP Leonardo IoT for IoT Platform in 2018, SAP Customer Retention, powered by Leonardo ML for ML and Data Science Platforms in 2018 and the related IT decision-makers and key stakeholders.

Our database provides customer insight and contextual information on which enterprise applications and software systems Queensland Treasury is running and its propensity to invest more and deepen its relationship with Aurion , SAP , ELMO Software or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.

We have been analyzing Queensland Treasury revenues, which have grown to $459.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Queensland Treasury intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.

About Customer

Queensland Treasury is at the heart of the Queensland Government, driving and facilitating better financial and economic outcomes for Queensland. We work to improve the quality of life for Queenslanders by growing and strengthening the economy, creating jobs and improving services.

Scope and Challenges

“We don’t want a system where the machine is making decisions. But we do want the machine to offer up next best action recommendations to our staff, that they have the option to follow – or not – based on their experience and knowledge of how the legislation should be applied… We’d also like a system that can ingest big data and take action within certain parameters. Natural disaster, the machine might be able to find out which customers are impacted and replace debt collection notices with proactive letters giving additional time to pay”.-said Liz Goli, Commissioner of Queensland’s Office of State Revenue (OSR).

Outcome and Implications

Within the transformation, OSR is pursuing a tax insights project, initially as a proof-of-concept but now as a full production deployment. The application built on SAP Leonardo machine learning will be progressively rolled out across OSR to all tax lines, commencing July 2018. A more responsive and tailored approach to individual taxpayers will lead to a more timely collection of revenue and reduction in debt across all tax lines, and additional revenue to fund essential services for Queenslanders. Machine learning application had been designed to propose the next best approach, payment suggestion, or debt management procedure to best support.