Australia is investing heavily in AI and workplace technology, yet productivity has stalled. The next gains will depend on redesigning how work gets done and ensuring new tools support that change, from enterprise processes to the frontline.
Australia’s productivity problem is proving stubborn, with output and hours worked both rising by 0.4% in the June quarter of 2026 and leaving labour productivity flat, according to the Productivity Commission. Over the year, labour productivity declined by 0.2%.
Meanwhile, investment in technology is accelerating. Treasury reported in May that Australian business investment in information technology and media equipment grew by more than 50% in 2025, while artificial intelligence (AI), automation and workplace platforms are now central to many organisations’ growth and efficiency plans.
Despite increased technology investment, the economy is producing less for every hour worked. Executives therefore need to understand what must change around the technology for productivity gains to show up in operating performance.
Roger Perry, Managing Partner, Australia at global management consultancy Argon & Co, believes one of Australia’s greatest productivity opportunities lies in getting more from technology and using it to rethink how work gets done.
“So many organisations are experimenting with AI without a clear idea of the problem they’re trying to solve or adding it to processes that are already inefficient,” Perry says. “That creates cost without delivering enough business value.”
Clayton Pyne, CEO of Humanforce, a provider of workforce management and human capital management technology for frontline and flexible workforces, believes AI can make a significant contribution to productivity when it is connected to operational outcomes and the work people already perform.
“Successful organisations begin with the outcome and redesign the work around it,” Pyne says.
Better work design makes technology productive
Perry says the challenge is that technology programs frequently begin with a proposed solution before the business opportunity has been clearly defined. An organisation may digitise a process, add an AI assistant or introduce another specialist platform without first asking whether the underlying work should happen in the same way at all.
“Technology won’t fix a bad process,” Perry says. “If a process is already slow, complicated or poorly designed, digitising it will not suddenly make it productive.”
Perry says this helps explain why promising pilots do not always translate into enterprise-wide returns. “A pilot may look impressive, but the real test is whether it can scale economically, improve performance and change the way people work.”
Achieving that at scale also requires a clear strategy, well-designed processes and an effective approach to implementation and organisational change.
Businesses need a clear AI strategy that identifies where AI can remove a real constraint, deliver a measurable benefit and scale across the organisation.
Roger Perry, Managing Partner Australia, Argon & Co
The stronger performers redesign how their organisations work around clear outcomes, Perry says, then align processes, technology and people to deliver them.
The distinction is particularly important for AI. Perry says AI can save employees time on research, writing and administration every day. Those individual gains are useful, although the larger opportunity lies in applying AI across complete business processes.
“The bigger gains come when AI improves planning, coordinates work across teams, automates repetitive decisions, identifies risks earlier and speeds up service,” Perry says. “Businesses need a clear AI strategy that identifies where AI can remove a real constraint, deliver a measurable benefit and scale across the organisation.”
The frontline productivity gap
Pyne sees another large opportunity in a part of the economy often overlooked in the AI debate – the frontline. Healthcare and social assistance, retail, construction, and accommodation and food services together employed around 6.6 million Australians in March 2026, representing more than two in five workers.
“Much of the AI productivity conversation has focused on knowledge work,” Pyne says. “Australia’s next productivity dividend is on the frontline.”
For frontline-heavy organisations, productivity is shaped by a different set of operational realities. Work must be matched to fluctuating demand across sites and shifts while accounting for qualifications, availability, fatigue, awards, enterprise agreements and different employment types.
Much of the AI productivity conversation has focused on knowledge work. Australia’s next productivity dividend is on the frontline.
Clayton Pyne, CEO of Humanforce
“For large frontline employers, the roster is far more than a calendar,” Pyne says. “It is the operating plan that translates changing demand into costed, compliant work across sites, awards, enterprise agreements, qualifications and employment types.”
The roster is also a major part of the employee experience, determining how much notice people receive, whether work fits around their lives and whether they feel shifts are allocated fairly.
Better roster design can reduce coverage gaps, overtime, unnecessary agency use and the hours managers spend on administration, while giving employees more predictability and control. For frontline organisations, the result is better service or care from every labour hour, fewer avoidable errors and more time for managers to lead their teams and employees to deliver the care, service and connection people value.
“The biggest source of lost productivity is fragmentation across the employment journey,” Pyne says. “In many organisations, that journey is spread across separate products, records, workflows and employee experiences. Each system may work well on its own and still create an expensive enterprise problem between them.”
That friction multiplies at enterprise scale because a change to a worker’s role, qualification, availability, employment condition or location can affect whether they are ready to work, where they can be rostered, which rules apply and how they should be paid. When systems do not share context, managers reconcile the gaps, IT maintains brittle interfaces and employees navigate the resulting confusion.
A Forrester Consulting study commissioned by Humanforce, involving 328 frontline workers and 342 decision-makers across Australia, New Zealand, the UK and the US, found 34% of frontline workers switched between platforms to complete a task. Almost one-third experienced confusion or rework caused by overlapping human capital management software.
The findings reinforce the broader challenge identified by Perry. Adding another system can shift work elsewhere, while connected technology can improve productivity by giving decisions about hiring, readiness, scheduling, work and pay a shared workforce context.
For employees, that connected experience needs to be mobile-first and available in the flow of work, whether they are setting their availability, receiving a roster, swapping a shift, completing training or checking their pay.
Put AI to work on the process
“AI will have the greatest impact when it is embedded into the way frontline organisations operate and connected to the work people are already doing,” Pyne says. “It can forecast demand, recommend better rosters, match people to shifts, identify fatigue or compliance risk, detect payroll variances and remove repetitive administration.”
Role-specific AI agents are expected to become part of this model. An employee might use one to manage shifts, leave and pay. A manager or operations leader could use another to identify coverage, fatigue and compliance risks, while a payroll agent could flag variances before a pay run.
The consequences of an error also become more direct when AI moves into core operations. Decisions affecting labour costs, award interpretation or someone’s pay require greater certainty than a plausible AI answer provides. Pyne says each agent must understand the context and permissions of its role, recommend or take actions within defined boundaries and escalate when human judgement is required.
“Point AI at the paperwork. The shift still belongs to the person working it,” Pyne says. “Rostering, time, compliance and pay need deterministic rules, structured data, audit trails and human oversight.”
Start with the constraint
Perry says businesses should identify where AI can remove a real constraint, deliver a measurable benefit and scale across the organisation. Productivity measures should therefore track improvements in business performance rather than technology adoption alone.
“Businesses need to start by understanding where their productivity opportunities actually are and develop a clear strategy for where technology, including AI, can make the biggest difference,” Perry says. “The priorities will vary by organisation and industry, so there isn’t a single formula.”
For a frontline employer, the full employment journey must remain in view, from attracting and onboarding work-ready people through to rostering, shift fulfilment, time, pay, development, recognition and retention.
“The strongest organisations combine central governance with local responsiveness,” Pyne says. “They set clear rules, permissions and accountability, integrate with the wider enterprise stack, involve employees in the redesign and equip managers to work differently.”
Employee experience also forms part of the productivity equation. Fairer and more predictable rosters, greater control over availability, mobile access to work and pay, and meaningful recognition and development can improve retention and make it easier to fill shifts internally. That can reduce replacement costs and reliance on external labour while improving continuity for customers, patients, residents and guests.
Organisations should measure workforce outcomes including roster preparation time, shift-fill rates, overtime, agency use and payroll exceptions, alongside time to productivity, turnover, engagement, service continuity, customer experience and revenue.
For broader transformation programs, Perry similarly stresses the need to test whether technology can scale economically, improve performance and change the way people work.
Their perspectives point to the same conclusion. Australia can lift productivity when organisations make deliberate choices about the work they want to improve, remove friction from end-to-end processes and give people the capability and authority to use new tools well.
The companies most likely to turn today’s technology investment into measurable returns will treat AI and automation as catalysts for organisational redesign. Technology can accelerate the work, while leadership remains responsible for deciding which work is worth doing, how it should be organised and what better performance looks like.
