Australia has a productivity problem, and artificial intelligence is increasingly being promoted as the solution.
The excitement is understandable. AI can produce reports in minutes, analyse large volumes of information, automate administration and give employees faster access to knowledge. Used well, it can free people from repetitive work and create more time for innovation, decision-making and customer engagement – the human aspects of business.
But implementing AI does not automatically make an organisation more productive.
The real opportunity is a dual model: using AI to deliver immediate improvements while also redesigning the processes, operating models, data and workforce capabilities around it.
This is where AI becomes more than another technology investment. It becomes a catalyst for organisational change.
Faster tasks do not always mean faster organisations
Imagine an employee using AI to prepare a report in half the usual time. That is a genuine efficiency gain.
But if the report still moves through six layers of reviews, relies on disconnected systems and supports a decision that takes three months to approve, the organisation has not captured the full benefit.
The task is faster, but the organisation is not.
The greatest productivity gains will not come from inserting new tools into old ways of working. They will come from combining AI with a deliberate effort to simplify how work gets done and a clear focus on outcomes
This means asking some necessary questions: Why does this process exist? Where are the bottlenecks? Which approvals genuinely manage risk? Which activities add value, and which are simply habits that have accumulated over time?
AI creates a compelling reason to revisit these questions.
Redesign the work, not just the task
Organisations should begin with the business outcome they want to improve, rather than the technology they want to deploy.
That might mean faster customer service, shorter project timeframes, better asset performance, improved regulatory reporting or less administrative effort.
Once the outcome is clear, AI can be combined with process redesign to improve the entire workflow. A customer request, for example, should not simply receive an AI-generated response more quickly. The organisation should also consider how the request is captured, routed, approved and resolved.
The objective is not to automate every existing step. It is to determine which steps should exist at all, then which steps and decisions can be automated, and which need to be performed by employees to maximise the outcomes.
Put people at the centre
AI delivers the greatest value when people understand how it supports their work and feel empowered and experienced in how to use it effectively.
Clear accountability, practical governance and collaboration across business, technology, data and workforce teams are essential. AI is not simply an IT initiative. It is a people-led change requiring new skills, behaviours and ways of working.
Fix poor-quality data
AI cannot overcome poor-quality data. Disconnected systems, duplicated records and inconsistent information will produce unreliable outputs and weaken decisions.
AI provides the catalyst to improve data quality, ownership, integration and governance.
The result is more reliable AI outputs and better information for employees, customers and leaders.
Improving these foundations may ultimately deliver some of the most important productivity gains.
Bring the workforce on the journey
The productivity opportunity for Australia is to create capacity to generating more value for less inputs. It is not about reducing headcount.
When AI reduces the time employees spend searching for information, preparing documents or completing routine administration, people can focus on work requiring judgement, creativity, relationships and problem-solving – the high value work.
That shift will not happen automatically. Employees need practical training and the ability to recognise errors, bias and inappropriate outputs. Leaders must also rethink roles, team structures and performance measures so that the capacity created by AI is deliberately redirected towards higher-value work.
Otherwise, saved time can easily disappear into more meetings, more emails and more low-value activity.
Measure outcomes, not enthusiasm
The number of AI licences issued, employees trained or pilots launched may demonstrate momentum, but it does not demonstrate productivity.
Organisations should measure tangible outcomes: faster processing, fewer errors, reduced costs, increased service capacity, improved customer experiences and better decisions.
Australia does not need to choose between acting quickly on AI and strengthening its organisational foundations. Both can happen together and need to happen together.
Targeted AI initiatives can deliver early wins while exposing the processes, data and capability gaps requiring attention. Improving those foundations then allows AI to generate even greater value.
That is the power of the dual model.
AI will not single-handedly solve Australia’s productivity challenge, but it can provide the spark for organisations to simplify work, improve information, strengthen capability and operate differently.
Technology creates the possibility. Organisational change turns it into productivity.
By Cathara Consulting
