²ÝÝ®ÊÓƵ research reveals the time saved by AI is silently lost to rework. Learn how CHROs in Australia can reinvest the 'AI tax' back into their people.
Melissa Bowden
Senior HR Director
²ÝÝ®ÊÓƵ APAC & Japan
While three-quarters of Australian employees remain convinced that AI has made them more productive over the last 12 months, the reality is a complex trade-off.?
Our data shows that while save between four and seven hours a week using AI, more than a third are simultaneously losing one to two hours checking, correcting, or rewriting low-quality outputs.
At scale, this pattern translates into millions of lost hours in large organisations. This is Australia¡¯s AI tax.
To address this issue, organisations are turning, not to CIOs, but to CHROs.?
Because our research shows that low-quality AI output appears wherever AI is adopted without corresponding changes to role design, enablement and development.
In fact, addressing the AI tax requires action in three areas of .
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AI is being layered onto jobs designed for a different era. Employees are using 2026 tools inside 2016 job structures and workflows.
Nearly 9 in 10 organisations report that fewer than half of their roles have been updated to .
That means AI is being layered onto jobs designed for a different era. Employees are using 2026 tools inside 2016 job structures and workflows.
The result is a growing gap between what AI can do and what an organisation¡¯s systems, policies and role design allow people to do.
Until roles are updated, AI will continue to sit awkwardly on top of work, likely speeding up chunks, but not actually improving outputs.?
CHROs should be directing their teams to formally rewrite job descriptions to include AI competencies ¨C especially in high-friction departments.
This is likely to require conducting role reviews so job descriptions can be updated to clarify where AI is expected to assist, where and how success is measured.
One of the biggest drivers of Australia¡¯s AI tax is that the use of AI tools is uneven and therefore unsupported.
Only 21% of Australians say they use AI tools once a day or more. Most are more likely to use them a few times a week (51%).
In cases like this, where AI use is not yet in enterprise-wide use, employees are often left to experiment alone ¨C without guidance or support.
The research found ¨C those running fastest toward the tools ¨C are experiencing the most friction.?
This is especially true where AI has upended the traditional 'apprenticeship pathway'.
Reducing rework and AI-generated stress is just as important as offering flexible work arrangements or wellbeing initiatives.
Graduates are increasingly expected to come up to speed to tackle strategic or high-value work faster than ever, just because they have access to AI tools.
But without structured training, that expectation is unfair.?
And the danger is not just lost productivity, but workforce churn. Instead of AI accelerating early-career development, it can create a new kind of pressure.
Organisations that don¡¯t provide sufficient AI training risk burnout, disengagement and attrition in the very cohort they are relying on to build the future workforce.
CHROs can address this by building an enablement layer:
Training that covers more than 'how to prompt'
Role-based examples of how AI fits into their workflow
In a modern organisation, building this layer to reduce rework and AI-generated stress is just as important as offering flexible work arrangements or wellbeing initiatives.
For me, this is the biggest takeout from the research: the real return comes not from time saved, but from how that time is reinvested. Our Australian data highlights a critical gap.
A massive 86% of leaders say their organisation has prioritised reinvestment of AI-driven efficiency gains into .
Yet only 52% of employees say they have experienced increased investment in new or expanded skills training programs.
That mismatch is a major reason the AI tax persists.
If employees are saving time but not being equipped to use it well, productivity gains will plateau. And frustration will grow.
Employees want less busy work ¨C and more time for meaningful work: problem-solving, creativity, customer outcomes and team connection.
The organisations getting this right are using efficiency gains to create space for leadership training, change management and .
CHROs should push to evaluate productivity in terms of value created ¨C accounting for both time saved and time lost to rework.
They are actively encouraging teams to focus on collaboration, innovation and connection to purpose.?
People managers should be explicitly authorising their teams to use saved time for activities that improve collaboration, learning, and strategic thinking ¨C not just to increase task volumes.
Measuring success solely by hours saved obscures the real impact of AI on work quality and outcomes.
Instead, CHROs should push to evaluate productivity in terms of value created ¨C accounting for both time saved and time lost to rework.
In practice, this means over speed:
In HR, emphasising quality-of-hire over time-to-fill
In finance, focusing on forecast accuracy rather than transaction speed
In operations, valuing first-pass yield over total output volume
It¡¯s important to understand that the financial impact of rework doesn¡¯t just slow an individual down.
It compounds across teams, creates inconsistency and drives downstream cost through errors, delays and duplicated effort.
Nowhere is this more evident than in HR, where the research shows rework is high ¨C and the risk of low-quality output is significant.
HR professionals represent the largest share (38%) of employees experiencing the highest levels of AI-related rework.
Their work involves people decisions, communications, and compliance-sensitive processes, where 'good enough' output is rarely acceptable.
As a result, HR teams audit AI-generated work with exceptional rigour, absorbing the time cost required to ensure accuracy, tone and fairness.
AI delivers its greatest value when are paired with reinvestment in skills, role design and human judgment.
Otherwise, when organisations focus solely on speed, employees absorb the cost through rework and fatigue.
Paying a high tax on AI efficiency is not inevitable. It¡¯s the cost of implementing AI without investing in the humans who use it.
When we reinvest in people, AI becomes a durable supporter of better work ¨C not a hidden workload.
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