Australia's AI Value Gap Is a Measurement Problem First

A new Roland Berger study put a hard number on something many leaders already suspected. More than a third of Australian firms are deploying AI without anything to show for it.

The headline figure: 37% of Australian companies are investing in and deploying AI without showing meaningful value, the highest share among the developed Asia-Pacific markets in the survey (Source: ITBrief Australia). That reading comes from a study of 211 C-suite and technology executives across seven Asia-Pacific economies, including 30 Australian business leaders (Source: ITBrief Australia).

What makes this a story worth sitting with is that only 27% of Australian companies surveyed were successfully turning AI investment and deployment into measurable business value (Source: ITBrief Australia). Meanwhile the budgets keep climbing. 33% expected to increase AI spending in the next year, and 43% planned to spend more than AUD $5 million, up from 33% this year (Source: ITBrief Australia). So spend is accelerating while proof is not. That is the exact shape of wasted spend.

Why spend is outpacing proof#Link to this section

The easy assumption is that Australia is behind on adoption. The data says otherwise. Roland Berger found Australia had by far the highest rate of stalled companies in the region, but also one of the highest ratios of firms that had converted AI into value, which suggests the market and adoption are not the problem (Source: Consultancy.com.au).

The problem is what happens after deployment. The report draws a sharp line between the firms that get returns and the firms that stall. It found that 91% of companies struggling to generate value mainly use AI to augment existing processes, while only 25% redesign processes around autonomy when they convert AI efforts into successful outcomes (Source: ITBrief Australia). In plain terms, bolting AI onto old workflows produces activity, not results.

This matches the global picture from the same research house. Across more than 200 companies, nearly 90% report that financial returns from their AI projects lag behind their investment levels, and only a small share have realised concrete economic benefits (Source: Roland Berger, mynewsdesk). The capital is there. The conversion is not.

What the leaders measure that the stalled firms do not#Link to this section

Here is the part every CEO, CFO and CTO should underline. The single clearest difference between firms getting value and firms burning money is measurement.

Three-quarters of AI leaders use continuous return on investment monitoring, compared with 36% of stalled companies (Source: ITBrief Australia). And the global study is blunter still: 63% of surveyed firms rely solely on one-off measurements or gut feeling instead of continuous, data-driven monitoring, and only a quarter measure the returns of their AI tools automatically and continuously during operation (Source: Roland Berger, mynewsdesk). The report's own summary is that most companies do not know which projects actually generate value and which only generate costs (Source: Roland Berger, mynewsdesk).

The leaders also act on what they measure. Among businesses classed as AI leaders, 88% bring more than half of their proof-of-concept projects into production, compared with 9% of stalled companies (Source: ITBrief Australia). Measurement and follow-through travel together.

As Nawaz Isaji, a Partner at Roland Berger Australia, put it, the country is caught in an AI paradox, holding the highest share of stalled companies in the region, and leaders need to stop treating AI as a shiny add-on and start redesigning core workflows around autonomy (Source: ITBrief Australia).

How to close the gap: what to track and what good looks like#Link to this section

If the difference between value and waste is measurement, then measurement is where a leader should start. A few things are worth tracking, and they do not require a new platform rollout to begin.

Track real adoption, not licence counts. A seat that is paid for is not the same as a tool that is used. Ask your teams, anonymously and often, which AI tools they actually open in a normal week and which they have quietly abandoned. Abandoned licences are the cleanest definition of wasted spend you will find.

Track perceived time saved against cost. For each tool, capture whether people believe it saves them time, how much, and on what tasks. Then set that against the invoice. Good looks like a clear, repeatable line between a tool and the hours it gives back. If nobody can describe where the value lands, that is your answer.

Track conversion from pilot to production. The leaders move more than half of their proof-of-concept work into real use. If your pilots keep piling up without graduating, you are funding experiments, not capabilities.

Measure continuously, not once. One-off measurement after launch is how firms end up flying blind. The firms pulling ahead monitor returns while tools are in use, so they can cut what is not working and double down on what is.

The Australian numbers are not a verdict on whether AI works. They are a verdict on whether firms can prove it. The ones that can measure are the ones converting spend into value, and the gap between the two groups is widening.

If you want to see where your own AI spend is actually landing, our anonymous micro-surveys give you that read in days, not quarters. Start a GAiGE trial and find out what your teams would tell you if you asked them properly.