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Australia’s automation opportunity
Powerful new automation technologies such as machine learning, artificial intelligence, and advanced robotics are already transforming the Australian economy, workplace, education system and community. These technologies present an enormous opportunity to restore momentum to the Australian economy and extend the nation’s 30-year economic boom in an inclusive way. In a new report, Australia’s automation opportunity: Reigniting productivity and inclusive income growth [PDF-6.7MB], McKinsey outlines the impacts of automation across three scenarios, including slow-paced adoption, mid-point adoption and fast-paced adoption. The report sets out why and how Australia must push for the win-win scenario of inclusive growth by pursuing actions that both accelerate automation and adoption and share its benefits. Automation and AI will be disruptive, just as other technology adoptions have been disruptive in the past.
As automation technologies integrate into the workforce, the mix of skills required in all jobs will shift. If Australia embraces automation with an eye to national interests, the nation will be able to develop new business opportunities, boost productivity, and create better-paying jobs-keeping employment high and reinvigorating per capita income growth sufficiently to offset the impact of an aging population. Policy makers, business leaders and educational institutions will need to respond to a range of implications of automation and AI, including rising unemployment and income inequality, growing disparities between cities and rural communities, shifts in the global competitiveness of our export-oriented sectors, and reskilling and redeploying a large number of displaced workers. With foresight, proactive leadership from the government, business, and education sectors, and a commitment to act, Australia can capture the opportunity offered by automation, manage the transition, and ensure the gains are broadly shared. The economy will adjust and new jobs will flow from the higher productivity that automation generates, helping to return the economy to close to full employment…
If seized, this opportunity could add between $1.1 trillion and $4 trillion to the economy over the next 15 years, providing every Australian with $4,000 to $15,000 in additional income per year by 2030. Without proactive leadership to manage the transition to the new steady state, automation could have disruptive distributional impacts. Without retraining for vulnerable workers and those in vulnerable regions, income inequality could widen by up to 30%…. Download a summary of the report or the full report, Australia’s automation opportunity: Reigniting productivity and inclusive income growth [PDF-6.7MB]..
Workplace automation: how AI is coming for your job
For around 40 per cent of inquiries, the system – which has been trained to recognise residents’ questions by using machine learning, a form of artificial intelligence – presents Mr Radwell and other customer support officers with a series of potential, pre-written responses. The council’s machine learning system – provided by Digital Genius, a San Francisco-based specialist in customer service systems – has put it at the forefront of a transformation under way in millions of white-collar jobs worldwide. The growing power of software such as Digital Genius has opened up the possibility that new, intelligent systems will vastly improve the productivity of a range of office jobs from clerical to professional roles – which will reduce some of the drudgery involved in menial tasks but could lead to some people losing their positions. There are undoubtedly fields where machine learning systems, which are trained to analyse and quickly spot patterns in pools of data too big for humans to detect, have given employers extraordinary new capabilities. Among the benefits of RPA have been that human resources staff no longer spend three days at the start of every month reconciling ledgers of the scheduled deductions from staff’s pay packets with the actual deductions the system is due to make.
Alastair Robertson, Zurich’s head of continuous improvement and automation for the UK, is one of many in the field to express doubt about whether current systems are up to the task. A number of insurers – including Japan’s Fukoku Mutual Life – have said they are handling routine claims processing to machine-learning systems. While Aylesbury Vale District Council is confident that Digital Genius has paid for itself in reduced salaries and improved service to local residents, the council faced a huge challenge feeding the system with sufficient data about residents’ needs. Maryvonne Hassall, the council’s assistant director for digital transformation, says it was only after four months of pilot programmes that the system started to show enough understanding to be a useful tool. For more specialist professional services firms, the effort required to train a system is often unjustifiably high, says Mr Allgrove.
They saw them as similar to blue-collar roles, where a robot could often be inserted into a process without disrupting the wider system. Mr Allgrove acknowledges that lawyers increasingly rely on machine-learning systems capable of scanning huge numbers of relevant legal cases to assess their chances of a success in a given case.
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