Your AI isn’t underperforming. Your data foundation is.

Tokenmaxxing describes the tendency to optimise AI for visible activity rather than genuine business value. The usage dashboards look healthy. The leaderboards are climbing. But the outcomes are not necessarily following.

Our research found 80% of Australian AI decision-makers are concerned that high AI usage is being mistaken for genuine productivity gains. Yet only 8% are tracking whether AI is delivering real revenue or cost savings. 

And the consequences are already showing. Almost a third of businesses (32%) have paused, cancelled, or wound back AI deployments because the output could not justify the cost. Another 28% are currently reviewing deployments for the same reason. Investment and adoption are accelerating faster than the ability to measure return, and as budgets grow, the stakes of getting this wrong grow with them.

When AI tools underperform, decision-makers blame the quality of their underlying data more than anything else (32%) — more than double the share who blame the limitations of the AI models themselves (14%). Yet, only 28% treated data readiness as a formal prerequisite before deploying AI. A further 8% deployed with no formal data quality assessment at all.

The instinct is to treat rising AI costs as a compute problem. But the compute explosion in most enterprises is happening at the data retrieval layer. When an AI system relies on low-quality or poorly scoped data, it forces the model to consume exponentially more tokens to reach a usable answer. You are not just paying to compute; you are paying to compute junk.

Australian organisations do not need a massive flood of tokens for every query. They need the exact, right drop of hyper-contextualised data. AI return on investment is an observability problem before it is a budget problem.

Only 31% of businesses have a centralised view of how many AI agents or autonomous workflows are running across their organisation. Nearly half (47%) are concerned that AI adoption is outpacing their ability to govern it.

The monitoring gap is stark. Only 13% have usage logging in place, 11% conduct regular risk reviews, and just 2% have a formal incident response process for AI. Yet, 50% plan to expand the use of agents in the new financial year. If an AI tool caused a compliance failure tomorrow, only 22% are very confident they could identify what went wrong and why.

To fund AI increases, businesses are redirecting budgets:

• 16% from IT infrastructure and operations

• 12% from existing software licensing

• 10% from headcount or hiring budgets

• 8% from cybersecurity 

That last figure is worth noting. Though a very small number of businesses said they were using security budgets this way, no business should use cybersecurity budgets to fund AI expansion at the very moment AI is increasing the speed and scale of cyber threats. This creates risks that deserve scrutiny at the board level.

AI spend is variable and unbudgeted in ways that legacy software licensing never was. CFOs are scrutinising the entire technology stack differently as a result. The honeymoon period for AI spend is over. Boards want proof of value, not promises.

While the financial metrics are facing intense scrutiny, the research points to a more nuanced workforce story than the one that often dominates headlines.

AI is not just changing how much organisations spend on budgets; it is fundamentally altering how people work. When routine, repetitive administrative tasks are successfully automated, employees can elevate their daily focus. 

The research found that three-quarters (75%) of Australian businesses report their people are redirecting freed-up time toward higher-value initiatives. Instead of managing manual workflows, they are focusing on strategic planning, new product development, deeper customer engagement, and proactive upskilling.  

Furthermore, the data highlights a strong demand for specialised skills. Rather than reducing headcount, 45% of organisations anticipate creating entirely new, AI-focused positions within their businesses, with 18% already recruiting for these roles.  

Ultimately, sustainable AI investment relies heavily on supporting the human element. The businesses that pull ahead in this new financial year will not just build stronger data foundations; they will actively invest in the people who work alongside the technology. This collaboration is where compounding returns live, and where the real competitive gap opens up. 

The new financial year is the right moment to reset. It’s not the time to pull back from AI, but to build the foundation that makes it genuinely work. The businesses that do that now will be considerably better positioned in the future.

Learn more about Elastic’s context engineering capabilities and data retrieval tools.