Australian Businesses Increase AI Spend Despite Weak Returns: A Deeper Dive

A recent survey conducted by Elastic has unveiled a fascinating trend in the Australian business landscape. Despite the lack of tangible returns on investment, many Australian firms are bolstering their artificial intelligence (AI) budgets. This phenomenon has piqued the interest of industry experts and raised questions about the strategic priorities of these businesses.

The survey, which covered over 500 senior AI decision-makers at organisations with at least 50 employees, sheds light on the widening gap between AI adoption and the actual proof of business value. One in three businesses exceeded their AI budget in the last financial year, and a staggering half plan to increase spending again over the next 12 months. This surge in AI investment is not merely a knee-jerk reaction to the buzz surrounding AI; it reflects a deeper recognition of AI's potential to transform business operations.

However, the survey also reveals a troubling mismatch between AI spending and the measurement of its financial returns. Only 8% of respondents said they track AI against revenue growth, cost savings, or productivity gains. Instead, businesses are more likely to monitor prompt volumes, token consumption, and general usage than commercial outcomes. This oversight is already affecting projects, with nearly a third of organisations pausing, cancelling, or scaling back AI deployments due to the lack of justifiable returns.

The findings suggest that finance leaders and boards are beginning to ask harder questions about the justification of AI spending. As businesses move beyond the experimentation phase, they are entering a realm of tighter scrutiny, where budgets, governance, and measurable results are under the microscope. Jeremy Pell, Country Manager ANZ at Elastic, succinctly captures this shift, stating, 'AI has moved very quickly from experimentation to a serious budget line item in Australia. Most businesses are still only measuring AI usage, but we predict that in the new financial year, this is when that changes. Leaders will want to see real value, not just usage reports.'

The implications of this trend are far-reaching, affecting not only AI budgets but also other parts of corporate technology budgets. Among those reallocating funds to support higher AI investment, 16% said the money would come from IT infrastructure and operations, 12% from existing software licensing, 10% from headcount or hiring budgets, and 8% from cybersecurity and information security. This suggests that AI is no longer viewed as a separate innovation project but is beginning to compete directly with core operational and security spending.

Elastic warns that this shift could create fresh risks, particularly where security budgets are reduced as AI use expands. The link between AI investment decisions and broader technology priorities across businesses is a concerning one, especially given the increasing risk of security threats posed by new AI systems. Pell aptly notes, 'The findings also show AI investment decisions are increasingly affecting broader technology priorities across businesses. With AI increasing the risk of security threats, it is concerning to see even a small number of businesses redirecting cybersecurity spend. This is not an area businesses should be deprioritising.'

Data quality emerges as a central issue behind poor AI results, with 32% of respondents blaming poor data quality for AI tools' underperformance, compared to 14% who pointed to limitations in the AI models themselves. Yet, relatively few organisations have treated data readiness as a formal requirement before deployment. Only 28% said they had formally assessed whether their data was ready before rolling out AI tools, while 17% carried out only a light-touch review, and 8% deployed without any formal data quality assessment. These figures underscore the need for businesses to establish robust data governance practices to support their AI endeavors.