Speakers at EDGE 2024 highlighted a growing vibrancy in the local artificial intelligence sector, driven by a grassroots transformation that has put Australian employees at the top of the generative AI adoption curve.
While experimentation is surging, many organisations hesitate to move models into production due to funding constraints, risk appetite, and a lack of full machine learning operations stacks. Transitioning these initiatives into scalable assets requires businesses to proactively support their workforce through structured upskilling and robust data architectural frameworks.
Key Efficiency Gains:
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Microsoft reported an 84% usage rate of generative AI among Australian knowledge workers.
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Research from LinkedIn identifies nearly 500 tasks that generative AI can augment or automate.
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Close to 800 tasks within the workplace are identified as exclusively human skills.
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Surveys indicate that 97% of workers expect their companies to teach them about new AI tools.
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Small and medium-sized enterprises make up 99.8% of Australian businesses and employ 70% of the workforce.
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More than 80% of the AI skills required by small and medium-sized enterprises are currently being outsourced.
Overcoming Funding Roadblocks and Scaling Data Foundations
Historically, the starting point for any successful technology deployment relies on establishing a solid data foundation and maintaining good data practices. In the last 12 months, the majority of client projects focused heavily on testing, experimentation, and proving concepts. To successfully scale these models, enterprise leaders must select high-return use cases that justify cloud, data, and digital infrastructure investments to the CFO. Clarifying core business objectives, identifying high-value internal use cases, and building monitorable production stacks allows organisations to manage deployment risks effectively while securing the necessary budget.
“Nowadays, there are many off-the-shelf products available that can be used without vast amounts of data. However, as a general rule, having a solid data foundation and good data practices is crucial. For example, if you’re building your own generative AI, your solution is only as good as your data. Poor data will yield poor results.”
Emma BrometPartner - Data | Mantel
