AI Investment Grows from an Average of $28M to $68M: ROI Is Still Missing
In this Community Intelligence Report, ADAPT uncovers why AI projects stall between proof of concept and production, despite record investment levels.
Published Jun 23, 2026 in
Articles
Data & AI Strategy
Contributors
Pooja Singh
AI investment is surging across Australia, yet ROI remains elusive.
ADAPT’s Data & AI Edge Survey shows the average Data and AI budget has increased from $28M to $67.3M in a single year, while many organisations still struggle to translate AI activity into measurable business outcomes.
This report reveals why AI projects continue to stall between proof of concept and production, and what separates organisations generating real value from those still chasing demonstrations.
Three gaps are limiting AI returns
The challenge is not the technology.
Organisations are selecting platforms before defining workflows, building governance after deployment, and launching tools without creating clear value for the people expected to use them.
Andrew Dome, Chief Digital Information Officer at Uniting, demonstrates how starting with business problems can drive adoption and measurable outcomes.
Jenny Francis, Executive Manager of Digital at Hunter Water, shows why governance and sequencing matter in critical infrastructure environments.
Syed Ahmed, Director of Digital Strategy at Momen Labs and Chair of the AI Advisory Committee at the University of Wollongong, argues that many organisations are still trying to commercialise solutions for problems they have not properly defined.
The window for undisciplined AI experimentation is closing.
What you’ll learn in this report
- Why AI projects stall between pilot and production.
- The use case, governance, and adoption gaps limiting AI value.
- How leading organisations are turning AI activity into measurable outcomes.
- What boards are increasingly expecting from AI investment programs.
AI investment is surging across Australia, yet ROI remains elusive.
ADAPT’s Data & AI Edge Survey shows the average Data and AI budget has increased from $28M to $67.3M in a single year, while many organisations still struggle to translate AI activity into measurable business outcomes.
This report reveals why AI projects continue to stall between proof of concept and production, and what separates organisations generating real value from those still chasing demonstrations.
Three gaps are limiting AI returns
The challenge is not the technology.
Organisations are selecting platforms before defining workflows, building governance after deployment, and launching tools without creating clear value for the people expected to use them.
Andrew Dome, Chief Digital Information Officer at Uniting, demonstrates how starting with business problems can drive adoption and measurable outcomes.
Jenny Francis, Executive Manager of Digital at Hunter Water, shows why governance and sequencing matter in critical infrastructure environments.
Syed Ahmed, Director of Digital Strategy at Momen Labs and Chair of the AI Advisory Committee at the University of Wollongong, argues that many organisations are still trying to commercialise solutions for problems they have not properly defined.
The window for undisciplined AI experimentation is closing.
What you’ll learn in this report
- Why AI projects stall between pilot and production.
- The use case, governance, and adoption gaps limiting AI value.
- How leading organisations are turning AI activity into measurable outcomes.
- What boards are increasingly expecting from AI investment programs.
