V.ISC.2606 - Priority Use Cases for Analytics Solutions to Support MLA’s Data Strategy

Summary

Meat & Livestock Australia (MLA) sought to identify high-value opportunities to enhance its advanced analytics and artificial intelligence capabilities and unlock greater value from its data assets for the red meat industry.

Through its subsidiary Integrity Systems, MLA engaged the University of Technology Sydney Data Science Institute (UTS DSI) to review its data strategy and capabilities, consult stakeholders, identify priority use cases, and develop two AI proof-of-concept demonstrators.

The project demonstrated the potential for AI to simplify sheep breeding decisions through natural language access to genetics information and improve the efficiency and effectiveness of LPA audits through automated data analysis and audit preparation.

The findings also identified key actions to support future AI adoption, including continued investment in data assets, capability development, and responsible AI governance.

Objectives

The project aimed to identify how Meat & Livestock Australia (MLA) could enhance the value and impact of its Data Strategy through advanced analytics and artificial intelligence. Objectives included reviewing MLA's existing data assets, systems and capabilities, consulting with internal stakeholders to identify priority challenges and opportunities, and assessing potential AI and data science use cases for the red meat industry. The project also sought to identify actions required to strengthen data and AI capability, support responsible AI adoption, and develop two proof-of-concept demonstrators to showcase practical applications that could deliver benefits to producers, auditors and the broader red meat sector.

Key findings

The review found that MLA have strong foundations for advancing data-driven decision making and AI adoption, but continued investment in data assets, data quality and governance will be important to maximise value from industry datasets.

Stakeholder consultation identified a range of high-value AI and analytics opportunities for the red meat sector, leading to the prioritisation of two proof-of-concept demonstrators focused on sheep genetics and LPA audits.

The sheep genetics demonstrator showed how natural language interfaces can make complex genetic information more accessible, helping producers identify and select animals that best meet their breeding objectives.

The AI Auditor demonstrator showed the potential to improve audit preparation by automating access to and analysis of multiple data sources, providing more timely insights and creating opportunities for more targeted and efficient assurance processes in the future.

Benefits to industry

The project identified and tested practical AI applications that could deliver tangible benefits to Australia's livestock and red meat sector. By simplifying access to sheep genetics information and automating aspects of audit preparation, the proof-of-concept demonstrators highlighted opportunities to improve producer outcomes, reduce compliance costs and support more efficient, data-driven industry assurance processes.

MLA action

MLA has used the project outcomes to help shape its approach to advanced analytics and AI adoption. The demonstrators provide practical examples of how AI can deliver value to industry and are informing future investment decisions, capability development and the evaluation of additional AI use cases for the red meat sector.

Future research

Further research should investigate and pilot additional high-priority AI and analytics use cases identified through stakeholder consultation to validate their value and scalability for the red meat industry.

Continued development and evaluation of the sheep genetics and AI Auditor proof-of-concepts is recommended to assess operational deployment pathways, user adoption and long-term industry benefits.

Ongoing investment in data quality, data infrastructure and workforce capability development will help maximise the effectiveness of future AI-enabled solutions and support broader adoption across MLA and the industry.

Future work should also explore responsible AI governance frameworks, verified credentials and hybrid approaches to AI capability development to ensure trusted, secure and sustainable implementation of AI technologies.