Australia's $73B Bet on AI
And Why Data Has the Inside Track
News
November 3, 2025
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BRISBANE - Australia is making its biggest bet yet on artificial intelligence, as Federal Industry and Innovation Minister Tim Ayres confirmed last week that the National AI Capability Plan will be "broader and more expansive" than originally planned, with up to $73.3 billion in private data centre investment expected by 2028.
Widely-discussed research from MIT reveals that while AI adoption has surged across Australian businesses, a large proportion of businesses are reportedly getting little return on their investments.
The challenge is straightforward. LLMs like ChatGPT and Claude operate fairly simply, drawing on deep wells of structured data to do so. But more complex deployments typically rest on more complex data sets, which means businesses with solid data enjoy a deployment advantage.
Research from Wharton reveals the industry-specific impact of this divide. While 82% of enterprise leaders now use generative AI weekly, actual business transformation varies dramatically by sector: banking and tech report 83-88% positive ROI from AI investments, while manufacturers are sitting at 75%. Retailers lag even further at 54%.
Why Manufacturers Lag
The reason according to both studies is identical: fragmented data, legacy systems, and physical-world friction. Manufacturing companies juggle data across multiple systems that don't talk to each other. CRM over here, ERP over there, HR in another place, finance somewhere else.
"Industries more anchored in the world of real things face fragmented data, legacy systems, and physical-world friction," the Wharton study found. "It's not that AI doesn't work for manufacturers. It's that their data isn't ready for it."
Professor Cori Stewart, CEO and Founder of ARM Hub, sees this pattern daily across Australian manufacturers. "The technology works fine. But if your data lives in five different places with three different versions of the truth, no AI tool will magically fix that."
Government Backing the Transformation
Minister Ayres told business leaders last week that the National AI Capability Plan will be "built on three core principles," starting with "purposefully capturing the opportunities of AI."
Central to the plan is the AI Adopt Program, which has invested $17 million in four AI Adopt Centres to provide practical tools and resources for businesses implementing AI technologies.
"Businesses who have engaged with these centres are already finding ways to improve the world of work and service provision—reducing intensity for some workers and improving clinical accuracy for others," Minister Ayres said.
The broader plan aims to ensure "all Australians share the benefits of AI" while building "trust and confidence" in the technology through proven workplace adoption.
The Fix Is Simpler Than You Think
Good news: manufacturers don't need to become data scientists to solve this.
Brisbane-based UAP cut their project forecasting time from three days to a few hours using ARM Hub AI Adopt Centre to develop better scheduling tools. The Air Conditioning and Mechanical Contractors' Association deployed conversational AI that gives members plain-English answers to compliance questions and automates service reports.
Neither company hired a data science team; using ARM Hub's Data and AI-as-a-Service to consolidate scattered information into systems that actually work together.
"Businesses think AI adoption means a massive transformation project," Professor Stewart said in her keynote at the recent Gladstone Engineering Alliance Major Industry, Energy & Manufacturing Conference in October. The two-day conference brings together industry leaders from across Queensland's energy, manufacturing, and heavy industry sectors.
"Our approach breaks it into manageable steps. Pick one problem that matters to your business. Solve it. Then build from there."
ARM Hub's approach is straightforward:
Start with quality data — Get your information organised first
Begin small with clear ROI — Prove value quickly on one targeted problem
Keep humans in the loop — Your people know the business, keep them involved
Test thoroughly — Make sure it works before you scale
Monitor continuously — Track performance and adjust as needed
This addresses exactly why manufacturing lags behind other sectors. The Wharton study found that data-rich, digital-native industries like banking and tech monetise AI faster because their data is already structured and accessible. Once that challenge is met, the rest can follow.
Questions About AI and Your Data?
If you're struggling with scattered data systems or wondering how to start with AI, the ARM Hub team will be at The Business Show Australia on Thursday, November 6th at ICC Sydney. We're not running a formal session, just around to chat with businesses about practical implementation challenges.
Bring your questions about data fragmentation, getting ROI from AI, or where to start. We've worked through these problems with over 300 Australian companies and can share what actually works.
Sources & Further Reading
MIT State of AI in Business 2025 Report: https://arxiv.org/abs/2507.03748
Wharton/GBK GenAI ROI Study (October 2025): https://www.businesswire.com/news/home/20251028556241/en/82-of-Enterprise-Leaders-Now-Use-Generative-AI-Weekly-Multi-Year-Wharton-Study-Finds-as-Investment-and-ROI-Continue-to-Build
