AI is Australia's top priority. Why aren't the results showing up?

Ambition is running ahead of execution

The Mallesons 2026 Directions Survey put a number on something a lot of us have been watching play out. Drawing on more than 200 senior Australian executives, it found AI is now the leading priority for both short and medium-term business strategy. The catch is in the follow-through. (Source: Solicitors Journal, 'Australian businesses invest in AI growth', 2026)

More than 70 percent of respondents consider AI a prime opportunity for productivity and growth, yet only 46.5 percent have achieved tangible productivity improvements since adopting it. (Source: Solicitors Journal, 'Australian businesses invest in AI growth', 2026) As Mallesons Corporate M&A Partner Rhys Casey put it, businesses are betting heavily on AI, but "the honeymoon period is ending" and attention is turning to measurable returns. (Source: Solicitors Journal, 'Australian businesses invest in AI growth', 2026)

The wider Australian picture tells the same story. KPMG's Global AI Pulse survey of C-suite leaders found Australian businesses lead the world on AI governance but rank last among covered markets on productivity gains, with just 34.7 percent reporting gains from automating workflows against a global average of 42.3 percent. (Source: heygentic, 'Australia Leads the World on Responsible AI', 2026) Deloitte's 2026 State of AI in the Enterprise found 61 percent of Australian companies report improved efficiency, but only 30 percent are using AI to deeply rethink how work gets done. (Source: Deloitte, 'State of AI in the Enterprise', 2026)

So the gap is real, and it is not an AI problem. It is an adoption problem.

Why the results are not showing up

When gains fail to appear, the cause is usually one of four things, and often all four at once.

The first is process. Most firms drop a tool into an existing workflow and expect it to pay off. The Reserve Bank's November 2025 survey of 105 medium-to-large firms found two-thirds had adopted AI in some form, but nearly 40 percent described minimal use, and few reported material productivity gains, which the RBA attributed to adoption being shallow and often led by individual employees rather than the business. (Source: frontrowtech, 'AI adoption statistics Australia', 2026) Automating a bad process just gets you to a bad outcome faster.

The second is measurement. If you never set a baseline for how long a task took or what it cost before AI, you have no way to prove a gain afterwards. Hours saved by a team quietly disappear into other work unless someone is counting and deciding what to do with the freed-up capacity.

The third is skills. Deloitte's finding that efficiency is up but deep redesign is rare points to the same thing: people are using AI to do the old job slightly faster, not to do the job differently. (Source: Deloitte, 'State of AI in the Enterprise', 2026) That is a capability gap, and it does not close on its own.

The fourth is accountability. When AI is everyone's priority and no one's job, pilots stall. The Mallesons survey noted a growing imperative for accountability as organisations shift from treating AI as a novelty to demanding returns on real investment. (Source: Solicitors Journal, 'Australian businesses invest in AI growth', 2026)

What the firms seeing gains do differently

The pattern is consistent across the research: people and process beat tools. KPMG found that organisations scaling AI alongside workforce investment are nearly four times more likely to report meaningful business value, 77 percent against 20 percent. (Source: KPMG, 'Global AI Pulse survey', 2026) The tool is rarely the difference. The operating model around it is.

It is worth noting the fear of job cuts is not what is driving this either. The Mallesons survey found 60.2 percent of respondents are not pursuing AI as a tool for headcount reduction, and are instead focused on productivity and performance. (Source: Solicitors Journal, 'Australian businesses invest in AI growth', 2026) That is the right instinct. The firms getting value are redeploying capacity, not stripping it out.

Here is a practical checklist to close the gap:

• Pick workflows, not tools. Start with two or three high-volume processes where speed or quality actually move the business, and redesign them around how AI and people work together.
• Set a baseline first. Record current time, cost and error rates before you start. If you cannot measure the before, you cannot claim the after.
• Name an owner. Every use case needs one accountable person with the authority to change the process, not just run a trial.
• Invest in the people, not only the AI tools. Budget for training and time to practise. Capability is what separates efficiency from genuine change.
• Govern as you go. Australia's strength in responsible AI is an asset. Clear rules on data, review and sign-off let teams move faster with confidence, not slower.
• Decide what the saved time is for. Gains only count when freed-up capacity is deliberately pointed at higher-value work.

None of this is glamorous. That is rather the point. The 46.5 percent are not using better models than everyone else. They are doing the ordinary work of redesign, measurement and ownership that turns a tool into a result.