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The spatial and productivity implications of AI disruption

Posted November 01, 2026

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Alison Holloway, Jayden Perry

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SGS Economics and Planning Spatial AI Implications2

SGS has mapped where AI is most likely to change work across Australia, and what this means for cities. The main points are:

  • Exposure to AI-driven job automation is highest in major city centres, especially inner-city Sydney and Melbourne.
  • Higher exposure means work is more likely to change, not disappear.
  • AI may also lift productivity, create new occupations, and open new sources of economic growth.
  • The effects will reach beyond jobs to how cities are planned, including commercial floorspace, housing and transport, along with how we experience places and connect.
  • Without coordinated planning and investment, AI's productivity benefits will not be shared evenly.

The world is in the midst of another economic shift.

Artificial Intelligence (AI) is set to disrupt how work gets done at a pace and scale few industries have faced before. While much of the public conversation focuses on how AI will change jobs and what it can do, there has been less exploration of where those changes will occur and what they mean for the future shape and function of Australian cities and regions.

Source: Jobs and Skills Australia (2025) [1]

Australian workforces, cities and regions will all be shaped by how AI gets adopted in the years ahead. This SGS research on the geography of exposure to AI-driven job automation explores where disruption may occur. The research contributes to discussions about how to plan for and adapt future cities and regions with AI.

Exposure to AI-driven distruption is highest in Australia's major city centres, especially inner Sydney and Melbourne, a reversal of past waves of automation

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Exposure to AI-driven job automation by local government area. Exposure measures how much job tasks could change, not whether the job will disappear. Source: SGS Economics and Planning, based on Jobs and Skills Australia and ABS 2021 Census data.

Australia's economy has undergone structural changes and transformation before.

At the turn of the 20th century, Australia’s economy and national wealth were driven by agricultural activities and exports. By mid-century, the industrial economy had grown, built on manufacturing and physical production. Over the past 30 years, the economy shifted in response to reduced trade barriers and globalisation, with a rise of knowledge-intensive services: finance, professional services, technology and the broader information economy.

More recently, employment and economic activity in education and healthcare have increased, driven in part by an ageing population and by more people gaining formal employment qualifications. Each of these transitions has left its mark across the country, with different locations feeling the shifts. This has created a patchwork economy in which growth, decline and opportunity vary widely between and within regions. High-value urban economic activity has concentrated in highly connected, diverse, high-amenity locations and around major education and health assets, within reach of deep, diverse and skilled labour forces.

Structural change in Australian economy 1901-2026

Preview
Agricultural eraManufacturing eraServices era05101520253035190119261951197620012026Per cent share of GDP1901Agriculture19.4%Manufacturing12.1%Mining10.3%Business services1.9%1902Agriculture17.2%Manufacturing11.7%Mining9.2%Business services1.9%1903Agriculture19.9%Manufacturing11.4%Mining10.2%Business services2.0%1904Agriculture29.0%Manufacturing10.3%Mining9.4%Business services1.8%1905Agriculture29.0%Manufacturing10.9%Mining9.7%Business services1.8%1906Agriculture28.7%Manufacturing11.4%Mining9.3%Business services1.8%1907Agriculture30.6%Manufacturing11.4%Mining8.9%Business services1.7%1908Agriculture27.4%Manufacturing12.1%Mining8.5%Business services1.9%1909Agriculture27.2%Manufacturing12.3%Mining7.2%Business services1.8%1910Agriculture29.3%Manufacturing12.2%Mining5.6%Business services1.7%1911Agriculture26.3%Manufacturing13.1%Mining5.7%Business services1.7%1912Agriculture23.2%Manufacturing13.9%Mining6.0%Business services1.7%1913Agriculture23.0%Manufacturing14.1%Mining6.0%Business services1.7%1914Agriculture23.6%Manufacturing13.4%Mining5.2%Business services1.6%1915Agriculture17.5%Manufacturing14.9%Mining5.2%Business services1.9%1916Agriculture25.1%Manufacturing13.6%Mining4.8%Business services1.8%1917Agriculture30.7%Manufacturing12.3%Mining4.6%Business services1.6%1918Agriculture31.2%Manufacturing12.9%Mining4.5%Business services1.8%1919Agriculture27.6%Manufacturing12.4%Mining3.6%Business services1.8%1920Agriculture23.6%Manufacturing13.6%Mining3.0%Business services1.8%1921Agriculture28.2%Manufacturing12.4%Mining2.4%Business services1.6%1922Agriculture23.0%Manufacturing13.8%Mining2.3%Business services1.9%1923Agriculture22.5%Manufacturing14.4%Mining2.3%Business services1.9%1924Agriculture22.5%Manufacturing14.6%Mining2.5%Business services2.0%1925Agriculture26.4%Manufacturing13.4%Mining2.3%Business services1.9%1926Agriculture22.4%Manufacturing14.5%Mining2.3%Business services2.1%1927Agriculture21.3%Manufacturing15.1%Mining2.5%Business services2.2%1928Agriculture20.4%Manufacturing16.3%Mining2.1%Business services2.1%1929Agriculture21.3%Manufacturing16.8%Business services2.1%Mining1.9%1930Agriculture19.8%Manufacturing17.8%Business services2.2%Mining1.9%1931Agriculture20.3%Manufacturing15.7%Business services2.3%Mining1.9%1932Agriculture22.8%Manufacturing15.3%Business services2.1%Mining2.0%1933Agriculture23.2%Manufacturing15.7%Mining2.3%Business services1.9%1934Agriculture25.8%Manufacturing15.4%Mining2.4%Business services1.9%1935Agriculture22.3%Manufacturing15.7%Mining2.6%Business services2.1%1936Agriculture23.5%Manufacturing16.2%Mining2.8%Business services2.2%1937Agriculture24.9%Manufacturing16.3%Mining3.0%Business services2.2%1938Agriculture22.9%Manufacturing16.8%Mining3.1%Business services2.4%1939Agriculture19.6%Manufacturing18.6%Mining3.3%Business services2.5%1940Agriculture19.9%Manufacturing19.3%Mining3.2%Business services2.6%1941Agriculture20.1%Manufacturing20.0%Mining3.1%Business services2.8%1942Manufacturing20.6%Agriculture20.4%Mining3.1%Business services2.9%1943Manufacturing21.3%Agriculture20.6%Business services3.1%Mining3.0%1944Manufacturing21.9%Agriculture20.9%Business services3.2%Mining2.9%1945Manufacturing22.6%Agriculture21.1%Business services3.4%Mining2.8%1946Manufacturing23.3%Agriculture21.4%Business services3.5%Mining2.7%1947Manufacturing23.9%Agriculture21.6%Business services3.7%Mining2.7%1948Manufacturing24.6%Agriculture21.9%Business services3.8%Mining2.6%1949Manufacturing25.3%Agriculture22.1%Business services4.0%Healthcare2.7%Mining2.5%1950Agriculture25.0%Manufacturing24.0%Business services4.1%Healthcare2.6%Mining2.2%1951Agriculture30.2%Manufacturing22.1%Business services4.0%Mining2.2%Healthcare2.1%1952Manufacturing26.1%Agriculture19.2%Business services4.1%Healthcare3.0%Mining2.8%1953Manufacturing25.2%Agriculture20.8%Business services4.1%Healthcare3.0%Mining2.3%1954Manufacturing26.6%Agriculture18.6%Business services4.0%Healthcare3.0%Mining2.2%1955Manufacturing27.2%Agriculture16.6%Business services4.1%Healthcare3.4%Mining2.2%1956Manufacturing27.5%Agriculture15.9%Business services4.0%Healthcare3.7%Mining2.3%1957Manufacturing27.2%Agriculture16.4%Business services4.0%Healthcare3.8%Mining2.3%1958Manufacturing28.8%Agriculture12.9%Business services4.3%Healthcare4.1%Mining2.0%1959Manufacturing28.3%Agriculture14.1%Business services4.5%Healthcare4.3%Mining1.8%1960Manufacturing28.8%Agriculture13.3%Business services4.7%Healthcare4.4%Mining1.8%1961Manufacturing28.2%Agriculture12.8%Business services4.9%Healthcare4.8%Mining1.8%1962Manufacturing27.8%Agriculture12.0%Healthcare5.1%Business services5.1%Mining1.7%1963Manufacturing25.6%Agriculture11.9%Business services7.0%Healthcare5.2%Mining1.7%1964Manufacturing25.1%Agriculture12.9%Business services7.0%Healthcare5.2%Mining1.8%1965Manufacturing25.5%Agriculture11.6%Business services7.2%Healthcare5.2%Mining1.7%1966Manufacturing25.4%Agriculture9.7%Business services7.6%Healthcare5.2%Mining1.9%1967Manufacturing24.6%Agriculture10.8%Business services7.6%Healthcare5.2%Mining2.0%1968Manufacturing25.0%Business services8.0%Agriculture7.9%Healthcare5.3%Mining2.1%1969Manufacturing24.5%Agriculture9.1%Business services8.2%Healthcare5.3%Mining2.3%1970Manufacturing24.4%Business services8.6%Agriculture7.4%Healthcare5.3%Mining3.2%1971Manufacturing24.1%Business services9.0%Agriculture6.2%Healthcare5.3%Mining3.2%1972Manufacturing23.1%Business services9.1%Agriculture6.3%Healthcare5.4%Mining3.5%1973Manufacturing22.3%Business services9.5%Agriculture7.2%Healthcare5.4%Mining3.4%1974Manufacturing21.7%Business services9.1%Agriculture8.1%Healthcare5.4%Mining3.6%1975Manufacturing20.9%Business services8.7%Agriculture5.8%Healthcare5.4%Mining3.9%1976Manufacturing20.2%Business services8.9%Healthcare5.5%Agriculture5.1%Mining3.9%1977Manufacturing19.8%Business services8.7%Healthcare5.5%Agriculture5.0%Mining4.0%1978Manufacturing19.6%Business services8.5%Healthcare5.5%Agriculture4.5%Mining4.0%1979Manufacturing18.7%Business services8.9%Agriculture6.4%Healthcare5.5%Mining4.0%1980Manufacturing19.0%Business services9.1%Agriculture6.5%Healthcare5.6%Mining4.2%1981Manufacturing19.4%Business services9.1%Healthcare5.6%Agriculture5.4%Mining4.1%1982Manufacturing19.1%Business services8.8%Healthcare5.6%Agriculture5.0%Mining3.8%1983Manufacturing18.0%Business services9.6%Healthcare5.6%Mining4.3%Agriculture3.5%1984Manufacturing18.0%Business services10.0%Healthcare5.7%Agriculture5.0%Mining4.6%1985Manufacturing17.8%Business services9.7%Healthcare5.7%Mining4.8%Agriculture4.4%1986Manufacturing17.3%Business services10.2%Healthcare5.7%Mining5.2%Agriculture4.2%1987Manufacturing16.3%Business services11.1%Healthcare5.8%Mining4.8%Agriculture4.2%1988Manufacturing15.9%Business services12.6%Healthcare5.7%Mining4.4%Agriculture4.3%1989Manufacturing15.3%Business services13.4%Healthcare5.7%Agriculture4.5%Mining3.8%1990Manufacturing15.0%Business services13.6%Healthcare5.0%Mining4.9%Agriculture4.6%1991Manufacturing13.7%Business services13.7%Healthcare5.5%Mining5.2%Agriculture3.5%1992Manufacturing13.7%Business services13.5%Healthcare5.6%Mining5.1%Agriculture3.3%1993Business services14.0%Manufacturing13.9%Healthcare5.6%Mining5.0%Agriculture3.5%1994Manufacturing14.3%Business services14.0%Healthcare5.5%Mining4.7%Agriculture3.6%1995Business services14.6%Manufacturing14.3%Healthcare5.5%Mining4.7%Agriculture3.3%1996Business services14.9%Manufacturing13.9%Healthcare5.5%Mining5.0%Agriculture3.7%1997Business services15.1%Manufacturing13.4%Healthcare5.5%Mining4.7%Agriculture3.5%1998Business services15.7%Manufacturing13.6%Healthcare5.3%Mining4.7%Agriculture3.3%1999Business services15.7%Manufacturing13.0%Healthcare5.5%Mining4.5%Agriculture3.3%2000Business services16.2%Manufacturing12.6%Healthcare5.6%Mining4.6%Agriculture3.4%2001Business services16.7%Manufacturing12.1%Healthcare5.7%Mining5.5%Agriculture3.8%2002Business services16.5%Manufacturing11.5%Healthcare5.7%Mining5.3%Agriculture4.3%2003Business services16.5%Manufacturing11.8%Healthcare5.9%Mining4.9%Agriculture3.2%2004Business services16.3%Manufacturing11.8%Healthcare6.0%Mining4.3%Agriculture3.4%2005Business services16.5%Manufacturing11.1%Healthcare5.9%Mining5.3%Agriculture3.1%2006Business services16.7%Manufacturing10.5%Mining7.1%Healthcare5.9%Agriculture3.0%2007Business services17.3%Manufacturing9.9%Mining7.6%Healthcare5.9%Agriculture2.4%2008Business services17.4%Manufacturing9.7%Mining7.6%Healthcare6.2%Agriculture2.5%2009Business services17.5%Mining9.6%Manufacturing8.9%Healthcare6.1%Agriculture2.5%2010Business services17.8%Manufacturing8.5%Mining7.8%Healthcare6.5%Agriculture2.4%2011Business services17.4%Mining9.8%Manufacturing7.8%Healthcare6.6%Agriculture2.5%2012Business services17.6%Mining9.4%Manufacturing7.4%Healthcare6.5%Agriculture2.4%2013Business services18.1%Mining8.1%Manufacturing6.9%Healthcare6.8%Agriculture2.4%2014Business services17.9%Mining8.9%Healthcare6.9%Manufacturing6.7%Agriculture2.4%2015Business services18.6%Healthcare7.3%Mining7.1%Manufacturing6.6%Agriculture2.5%2016Business services18.8%Healthcare7.4%Manufacturing6.4%Mining6.2%Agriculture2.6%2017Business services18.7%Mining8.1%Healthcare7.4%Manufacturing6.1%Agriculture2.9%2018Business services18.6%Mining8.7%Healthcare7.5%Manufacturing6.0%Agriculture2.7%2019Business services18.3%Mining10.4%Healthcare7.7%Manufacturing5.8%Agriculture2.3%2020Business services18.4%Mining10.9%Healthcare7.8%Manufacturing5.9%Agriculture2.2%2021Business services18.4%Mining11.4%Healthcare8.1%Manufacturing5.8%Agriculture2.5%2022Business services17.9%Mining14.4%Healthcare8.1%Manufacturing5.7%Agriculture2.9%2023Business services17.6%Mining14.5%Healthcare7.9%Manufacturing5.6%Agriculture2.7%2024Business services18.1%Mining11.7%Healthcare8.4%Manufacturing5.7%Agriculture2.2%2025Business services18.3%Mining9.9%Healthcare8.8%Manufacturing5.7%Agriculture2.4%2026Business services18.4%Mining9.5%Healthcare9.0%Manufacturing5.4%Agriculture2.6%Business servicesMiningHealthcareManufacturingAgriculture

Source: SGS Economics and Planning, based on multiple ABS datasets.
Also see the SGS Cities and Regions Wellbeing Index

Understanding which occupations are exposed to AI disruption

The economy is transitioning again as we enter an era where AI can perform an expanding range of tasks once reserved for highly skilled trades and professionals. While previous waves of automation focused on routine, manual occupations, AI is increasingly capable of supporting or automating cognitive tasks knowledge workers perform. These earlier waves of automation produced economic and social consequences, including polarisation of income and skill levels across some sectors. We can learn from these changes to better manage future transitions.

Research by Jobs and Skills Australia (JSA) [2] finds that most occupations will have some tasks that could be automated using AI and new technologies. Knowledge work is identified as most exposed to this change, reflecting AI's current capacity to augment or automate tasks performed by skilled professionals more readily than to fully automate physical, interpersonal, or highly variable work. The direction and timing of these changes are uncertain.

Early indications show that the impacts of exposure to AI automation are yet to be felt broadly across industries. Research by the Office of the Chief Economist [3], which tested whether employment in more AI-exposed occupations has grown more slowly since ChatGPT was released in November 2022, found no evidence to date of broad labour market upheaval driven by AI in Australia. However, it did find that occupations more exposed to potential automation by generative AI have grown more slowly than other occupations.

The Australian Federal Government's most recent Intergenerational Report [4] notes that AI could reshape the labour market, but its effect on employment is highly uncertain. AI can augment an expanding range of tasks previously done by workers. But it can also raise productivity, create new tasks, and increase demand for labour elsewhere in the economy. This is reflected in work undertaken by the International Monetary Fund in 2024 [5], which came to the same conclusion, and work undertaken by the Organisation for Economic Co-operation and Development (OECD) in 2024 [6], which compared the regional effects of earlier automation technologies with those of generative AI and found generative AI could affect a much wider group of workers and places than past technologies, because it performs cognitive, non-routine tasks rather than routine ones.

Spatial analysis of exposure to AI automation

Work happens in places, and different places have different economies and mixes of jobs and skills. In our analysis, we identify the spatial dimension of JSA’s estimates of exposure to AI at the local government level across Australia. We mapped exposure to automation across Australia to explore not only which jobs are at risk of automation, but also where they are located and where the people employed in these occupations live. It is important to note that AI exposure to automation measures how much this occupation's tasks may change, not the probability that the job will disappear.

The analysis combines 2021 Census employment data by occupation, place of work, and usual residence with Jobs and Skills Australia's automation exposure scores by occupation. This lets us capture exposure to automation in:

  • the city centres, activity centres and industrial lands where people are employed
  • the communities where people live.
See the map above

We aggregated these occupation-level scores across each local government area’s total workforce for every LGA in Australia for both employment measures to understand total exposure to automation from AI (by LGA).

The results show a clear spatial difference in exposure to AI-driven job automation. Major urban and city centres carry the highest exposure to automation. This includes local government areas in capital cities, which together account for most of Australia’s population and workforce. These same locations are also likely to benefit from productivity gains from AI adoption.

These areas have a higher concentration of knowledge-economy and information-based jobs, most clearly seen in Inner-city Sydney and Melbourne. By contrast, areas built around work requiring a physical presence are far less exposed, since these tasks remain largely beyond what current AI systems can perform.

This pattern marks a departure from previous waves of economic disruption. The automation of manufacturing and the long decline of agricultural work hit regional and outer-suburban Australia hardest, the places furthest from our city centres. This new disruption looks set to concentrate instead in the professional heart of our cities, among the workers and industries that have historically been shielded from technological displacement.

The location and types of jobs shape the spatial structure of cities and regions

Settlements are being reshaped through planned major transport investments, such as high-speed rail, which carry far-reaching implications for the location of jobs, housing, and the workforce. AI adds another layer of change, one that is harder to plan for because its effects may spread across many occupations and places rather than concentrate in a single sector or corridor.

Given this, we need to start investing now in understanding AI's spatial implications for jobs, housing, and the workforce, so our cities can adapt and respond to the challenges and opportunities this creates.

We need to consider the importance of place in a world where connections matter more. Places connect people, and jobs and services will continue to agglomerate in population centres. What these jobs will look like in the future is up for debate; however, our planning must allow for places that are flexible and can respond to an increasingly changing world.

Learning from the past to ensure fast-paced AI delivers productivity and improved living standards

Australia has been through dramatic economic transitions in the past. The rapid rise of business services over the past four decades amply illustrates this; today these services account for almost one-fifth of Australia’s GDP, more than the contributions of mining, manufacturing and agriculture put together. This economic shift both reflected and drove a major boost in productivity, enabling Australia to post an enviable record among rich countries for unbroken growth, though the COVID pandemic ultimately put the brakes on.

We can now see that the productivity dividend arising from the shift to business services was not shared equally across the community. Many once-secure, well-paid manufacturing jobs, for example, became precarious or disappeared altogether, and the fortunes of their host suburbs declined as a result. Housing prices ballooned in those parts of our cities which offered propitious conditions for advanced business services, such as the inner cities. In some ways, we emerged from this structural change as a more fractured and divided society.

Australia needs to learn from the past four decades of structural change as we embark on the AI transition, a shift of potentially similar magnitude but likely compressed into a much shorter time frame.

In principle, productivity gains should translate into higher living standards. In this sense, AI is an opportunity of immense proportions. Nonetheless, recent history shows that fair sharing of this lift in national income can’t be taken for granted; good public policy must engineer it. In large part, the tools of this engineering lie in macroeconomic policy, including the tax and transfer system. But urban management also has a critical role to play. Productivity gains are capitalised into land values; fairer tapping of this uplift to help pay for better infrastructure to support suburban communities is essential.

Implications of AI exposure for the future of cities and regions

The geography of AI exposure suggests the next wave of economic change will concentrate in Australia's major urban employment centres rather than traditional industrial regions. As AI reshapes knowledge-intensive occupations, CBDs, activity centres and innovation precincts may experience significant workforce and economic transformation. Given these places' significant economic role, this will also have broader metropolitan and economic implications and opportunities.

Understanding how these changes affect places, not just occupations, is important when planning for the future of our cities and regions.

We need to consider how exposure to AI-driven job automation could reshape places across our cities and regions, including:

  • Changing demand for employment floorspace and locations. Including flexible and adaptable commercial, mixed-use and innovation precincts that can respond to evolving industries and ways of working.
  • Growing importance of places that support human connection, creativity and innovation. As AI becomes more embedded in work and daily life, demand for vibrant centres, public spaces, and cultural destinations that offer opportunities for face-to-face interaction, shared experiences, and collaboration will grow, including experience-based retail.
  • Investment in education, training and skills development at all career and life stages. As AI reshapes work, access to learning, reskilling, and upskilling will be key to adaptability, driven by stronger links with industry and community to meet needs.
  • Planning for places most likely to capture productivity gains from AI adoption. The analysis suggests that higher-density capital city regions will experience the greatest disruption from AI exposure. These places will need affordable housing and transport connectivity to attract and retain a talented, skilled workforce and improve living standards for all.
  • Infrastructure investment is needed to support future jobs, inclusive growth and competitiveness. Including access to digital connectivity, proximity to data centres and enabling infrastructure. Consideration should also be given to how the benefits from these are shared across workers, businesses, communities and regions.
  • The social and political consequences of new waves of technology-driven disruption. Including growing inequalities in income and shifts in political and economic power.

Understanding the spatial implications of AI-driven job automation will help ensure Australia's cities and regions can adapt and prepare for both the opportunities and disruptions that future technological change may bring. This will enhance long-term strategic planning at the metropolitan and local level and support actions that strive for more productive, inclusive and sustainable places, communities and economies.

References

[1] Jobs and Skills Australia (2025). Our Gen AI Transition Implications for Work and Skills. Final Overarching Report. Accessed at: Our Gen AI Transition - Implications for Work and Skills | Jobs and Skills Australia

[2] Jobs and Skills Australia (2025). Our Gen AI Transition Implications for Work and Skills. Accessed at: https://www.jobsandskills.gov.au/studies/generative-artificial-intelligence-capacity-study/occupation-data-on-ai-exposure

[3] Office of the Chief Economist, Department of Employment and Workplace Relations (2026). AI and employment in Australia. Accessed at: https://www.dewr.gov.au/workplace-relations/resources/ai-and-employment-australia

[4] The Treasury, Australian Government (2026). 2026 Intergenerational Report. Accessed at: https://treasury.gov.au/publication/2026-intergenerational-report

[5] International Monetary Fund (2024). Gen-AI: Artificial Intelligence and the Future of Work. IMF Staff Discussion Note SDN2024/001. Accessed at: https://www.imf.org/en/publications/staff-discussion-notes/issues/2024/01/14/gen-ai-artificial-intelligence-and-the-future-of-work-542379

[6] OECD (2024). Job Creation and Local Economic Development 2024: The Geography of Generative AI, OECD Publishing, Paris. Accessed at: https://doi.org/10.1787/83325127-en

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Share:
Author(s):

Alison Holloway, Jayden Perry

Experts in this area:
Sectors:
Related Services:
Types:
  • Local government
  • State government
  • Federal Government
  • Business
SGS Economics and Planning Alison Holloway Square Colour 2
For further information contact:

Alison Holloway

Principal & Partner

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SGS Economics and Planning Jayden Perry Colour
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