The Future of Work: How AI is Reshaping Jobs, Organisations, & Workforce Dynamics
Published 10 July, 2025.
Developed by Mark Cameron over several weeks working with ChatGPT, Perpexity, & STORM from Stanford University.
Introduction
Artificial Intelligence (AI) is revolutionising the workplace, automating tasks and augmenting decision-making. Studies suggest that nearly a quarter of all jobs will be affected by AI changes in the next five years. The World Economic Forum (WEF) projects that by 2027, AI and automation will create 69 million new jobs globally, displacing 83 million and resulting in a net loss of 14 million jobs, or about 2% of current employment. This transformation presents both opportunities and challenges. AI offers productivity gains and new roles, while raising concerns about job displacement and skills obsolescence. Organisations must proactively adapt to AI’s impact on work to maintain competitiveness and social responsibility.
The human dimension of this transformation is equally important. Technology reshapes work rather than eliminates it. A landmark MIT study found that 63% of jobs in 2018 did not exist in 1940, highlighting how innovation creates new opportunities while rendering some obsolete. AI follows this pattern, phasing out repetitive roles while emerging new occupations and industries.
Organisations must manage this transition to empower employees and preserve trust, inclusion, and engagement. This white paper examines AI’s impact on the future of work, focusing on human aspects of organisational change and job creation. We compare global trends with Australian insights, highlighting regional factors influencing AI adoption and workforce implications.
The paper begins by surveying current AI trends and statistics. It then presents an impact analysis of how AI reshapes job roles, organisational structures, and workforce dynamics across sectors. Finally, it explores strategic implications for organisations, recommending change management, reskilling, and promoting diversity, equity, and inclusion in an AI-driven world.
We conclude with findings and a call to action for leaders. The paper maintains an optimistic tone, emphasising AI’s potential to augment human potential and build resilient organisations. We hope senior executives will use these insights to navigate the evolving landscape of work confidently, knowing that the future can be shaped by today’s choices.
Current Landscape of AI and Work
AI adoption and workforce trends globally show rapid progress from research labs to practical business applications. Companies worldwide invest heavily in AI capabilities, including machine learning, data analytics, and generative AI tools. As of 2023, machines or algorithms perform about one-third of average work tasks, up from 2020. Despite the hype, automation is still in its early stages for many jobs, but the pace is accelerating. Advanced AI, particularly generative AI since 2022, is expected to substantially boost automation in the near future. McKinsey Global Institute research suggests that up to 30% of hours worked in mature economies like the US could be automated by 2030, accelerated by generative AI. The International Monetary Fund notes that about 60% of jobs in advanced economies have at least some component that could be automated with AI, compared to around 40% in emerging markets. High-income countries face greater disruption in the short term due to their more knowledge-intensive workforce and services that are now within AI’s reach.
Despite concerns, many employers see AI as a positive force for the job market. In WEF’s 2023 executive survey, 75% of companies plan to adopt AI by 2027, and 50% expect it to create net jobs, while only 25% anticipate a net decrease. This optimism stems from past automation waves that boosted productivity and created new roles. WEF estimates that 23% of current jobs will undergo significant changes by 2027, involving both job creation and displacement. While some roles will decline, such as clerical positions, demand for technology and business professionals will surge. The overall outlook is one of “creative destruction,” where AI disrupts jobs but also generates innovation, productivity gains, and new career paths. Companies aim to use AI to augment human work and create value, rather than simply cut costs, making reskilling a strategic imperative and part of their employee value proposition.
Australia’s AI Landscape in Context
Australia is an early adopter of digital technologies, including AI, but there are local differences. Pre-pandemic OECD estimates suggested that about 36% of Australian jobs are at risk of automation, compared to an OECD average of 46%. This lower exposure is due to Australia’s industrial structure, with a high share of service-oriented and knowledge roles, and strong workforce skills. Only about 11% of jobs are at high risk of automation, while another 25% will likely undergo significant job content changes. In practice, task-level automation is happening rather than wholesale job elimination. Many Australian firms are using AI to streamline routine processes, with 26% reporting increasing automation use during COVID-19 disruptions to ‘pandemic-proof’ operations. This trend has continued post-pandemic. A 2024 Deloitte report found that generative AI tool use in Australian workplaces jumped from 32% to 38% of employees in just 12 months, a remarkable 20% increase in a year. Australian employees are also growing more convinced of AI’s significance, with nearly two-thirds believing it will have an impact on business as profound as the internet’s by 2024.
When comparing Australia’s AI impact to global benchmarks, it’s crucial to consider Australia’s role as a technology user rather than a developer. Oxford Economics estimates that countries leading in AI creation, like the US, could see an additional 1.8–4.0% in annual GDP growth by 2032. However, Australia, as an adopter rather than inventor, might see a smaller boost (~0.2% increase in GDP by the mid-2030s) if it doesn’t build more domestic AI innovation capacity. This suggests Australian organisations must adapt and skill-build to capture benefits developed abroad and invest in local AI R&D to avoid lagging behind. On the labour market, Australia has experienced AI as an augmenting force, with employment in AI-exposed occupations growing from 2012–2022. AI-intensive sectors like tech, professional services, and healthcare have expanded jobs rather than cut them. Moreover, Australian workers have strong foundational skills for the digital era, with about 40% scoring in the top proficiency levels for problem-solving in technology-rich environments (versus ~30% OECD average). This indicates a well-prepared workforce for AI adaptation, provided continuous learning opportunities are available.
AI changes how jobs are done. For instance, an accounting analyst now spends less time gathering data and more time understanding AI-generated reports. Doctors use AI diagnostic tools to help, not replace, their clinical judgement. Recent surveys show that companies have only automated about 34% of applicable tasks, nearly the same as in 2020. Employers now expect about 42% of tasks could be automated by 2027, down from earlier forecasts. This reflects technical challenges and the fact that human judgement, creativity, and interpersonal skills are still important in most jobs. Some AI advances, like natural language processing and generative models, have shown the value of human traits like creativity, empathy, and complex problem-solving, which machines can’t replicate. Many forward-thinking organisations are redesigning jobs to let AI handle the mundane tasks while humans focus on higher-value activities.
Workforce disruption will be uneven, with certain roles and demographic groups facing greater challenges. Roles in routine administration and data processing are highly exposed to automation, and accounting, bookkeeping, and payroll clerks are among the top ten declining job categories in the next five years due to AI-based software. Factory assembly jobs, basic customer service, and data entry roles are also declining or being redefined. However, AI and machine learning specialists, data analysts, big data specialists, information security analysts, and other tech-centric jobs are projected to grow by 30–40% by 2027, adding millions of new jobs worldwide. Beyond tech roles, “softer” skills jobs like education and the care economy are also on the rise due to increased productivity and societal investment in human-centric services. The education sector globally is forecast to add over 3 million jobs by 2027, mirroring Australia’s need for more skilled educators and trainers to upskill the workforce. This suggests that AI’s impact is more about job evolution than elimination, as many jobs will still exist but in altered forms, requiring new skills or higher expertise.
The current landscape raises social implications. There’s growing concern that AI could exacerbate inequalities if not managed carefully. A recent UN ILO report found that jobs traditionally held by women may be more vulnerable to AI-driven task automation than those held by men. Specifically, about 9.6% of women’s jobs (especially in clerical, secretarial, and administrative support roles) could be significantly changed by AI, compared to 3.5% of men’s jobs, given AI’s strength in automating clerical tasks. This doesn’t mean AI will eliminate all these roles, but it indicates that without intervention, women, who are overrepresented in administrative occupations, might face disproportionate disruption. The ILO emphasises that most of these roles will be transformed rather than fully automated and calls on employers, governments, and worker organisations to leverage AI to enhance job quality and productivity instead of simply cutting jobs. Similar conversations are happening in Australia around diversity and inclusion, ensuring that AI systems are fair, reskilling programs are accessible to all employees, and the gains from AI translate into benefits like higher-value jobs and better work-life balance. In summary, the current landscape is one of cautious optimism, with AI’s capabilities advancing quickly and adoption broadening, yet society has a significant window of opportunity to guide these technologies toward positive outcomes. The next sections delve deeper into how AI is reshaping work in key sectors and what that means for organisations and their people.
Impact Analysis: How AI is Reshaping Work (By Sector)
AI is not impacting all industries uniformly – its effects vary by sector depending on the nature of work, the tasks involved, and the rate of technology adoption. Below, we examine several major sectors (professional services, manufacturing, healthcare, public service, education, and enterprise organisations) to understand how job roles, organisational structures, and workforce dynamics are being transformed in each. For each sector, I highlight global trends as well as any notable aspects of the Australian context.
Across the board, a common thread emerges: job roles are being redefined rather than simply removed. Human work is shifting toward areas where people excel (complex judgment, creative thinking, interpersonal interaction) while AI takes over routine, repetitive, or data-intensive tasks. This rebalancing has implications for organisational structure (e.g. new teams or units focused on AI, changes in management layers) and for workforce dynamics (e.g. changing skill requirements, new collaboration patterns between humans and AI systems). Let’s explore these changes sector by sector.
Professional Services (Consulting, Finance, Legal & More)
In professional services like consulting, accounting, legal, finance, marketing, and advisory roles, AI is a powerful force multiplier. These industries rely heavily on information and expertise, and AI tools automate time-consuming tasks, boosting productivity. For instance, AI can quickly review legal documents, sift through financial records for audits, or draft basic marketing copy. A McKinsey study found that current AI technology could automate 20-30% of the work done by lawyers and similar professionals. In accounting and finance, routine tasks like accounting entries, invoice processing, and payroll are being automated. The World Economic Forum predicts that accounting and payroll clerks will decline fastest due to software and AI handling these repetitive duties. Many large accounting firms now use AI-driven platforms for tasks like tax preparation and auditing, reducing manual effort. Australia’s big four accounting firms have rolled out AI-based audit tools that analyse entire datasets for anomalies, allowing human auditors to focus on interpreting results and advising clients.
AI is enhancing rather than eliminating most professional service jobs. By taking over low-value tasks, AI frees professionals to focus on higher-value activities like complex problem-solving, strategic planning, and client relationship management. For instance, while an AI may draft a standard contract, lawyers provide nuanced advice, negotiate terms, and make legal strategy judgments. In consulting, AI generates insights from large datasets, but consultants add value by recommending and persuading stakeholders. As a result, professional service firms are retooling their workforce, hiring data analysts, AI specialists, and tech-savvy talent, while reskilling existing staff to work effectively with AI outputs. Globally, roles like Data Analysts, Big Data Specialists, and AI/Machine Learning Specialists are in high demand, with WEF estimates predicting a 30-40% growth by 2027, creating millions of jobs. This trend is evident in Australia, where accounting firms and banks have created new positions for AI project managers, analytics translators, and even “AI ethicists” to oversee responsible algorithm use. For instance, Deloitte Australia’s AI Institute trains consultants in “AI-assisted advisory,” teaching them to use tools that scan client financial performance and generate instant diagnostic reports, which consultants then interpret for clients.
Organisational structures and work dynamics in professional services are changing. Traditional pyramidal team structures are being re-examined. AI can handle some of the work traditionally done by entry-level employees, so firms are experimenting with leaner teams or shifting junior roles to be more tech-focused. This could reduce the number of layers of hierarchy or repurpose those layers, for example, junior employees now manage AI tools and check their outputs. Some firms worry about the impact on apprenticeship models, but progressive firms are implementing training rotations to expose new hires to data science teams or AI centres of excellence. Multi-disciplinary teams are becoming the norm, with clients engaging with subject-matter experts, partners, data scientists, and AI engineers for technical modelling. Professional service organisations are evolving into more fluid, collaborative structures with embedded tech expertise.
From a human perspective, most professionals report that AI improves their jobs by reducing drudgery. A recent OECD survey of workers in finance and insurance found that employees using AI experienced improved job performance and greater job satisfaction because AI took over tedious tasks and allowed them to focus on more meaningful work. However, this requires continuous upskilling. Today’s consultants, lawyers, and accountants must be adept at using AI tools, such as understanding how AI models reach conclusions, verifying their accuracy, and communicating their insights to clients. Professional ethics are also in focus, with firms instituting guidelines for AI usage to ensure compliance and avoid bias. In Australia, with its strong professional services sector, these trends are expected to deepen. The sector will likely produce new jobs like “legal technologists” and “fintech advisors.” In summary, AI augments professional services by increasing efficiency and accuracy, leading to a higher productivity sector with more hybrid-skilled roles rather than a wholesale loss of employment. Organisations in this space are flattening structures, investing in tech talent, and focussing human capital on creativity, strategy, and client engagement, areas where humans still hold a comparative advantage over AI.
Manufacturing & Industry 4.0
Manufacturing has been at the forefront of automation for decades, with robotics and machinery leading the way. Now, AI is taking it to the next level, often called Industry 4.0. AI-driven systems improve assembly, quality control, and supply chain logistics in factories worldwide. Robots, guided by AI vision systems, perform intricate tasks at speeds and precision beyond human capabilities. Sensors and AI analytics predict equipment failures, reducing downtime. ‘Smart factories’ self-optimise via machine learning algorithms. This shift in roles means repetitive manual jobs decline, while skilled technical and supervisory roles grow. Jobs involving predictable physical work, like routine assembly and basic machine operation, are most susceptible to automation. This has led to a decline in roles like assembly line workers, welders, and machine operators in advanced economies. In contrast, roles that involve maintaining and overseeing machines or require flexibility and craftsmanship are still human. For instance, while robots now weld and paint car bodies, humans still handle complex assembly, custom installations, and quality checks, often with AI assistance.
Manufacturing employment has been declining in roles that can be automated globally, but many countries, including Australia, are reviving manufacturing by combining human craftsmanship with advanced automation. In high-cost economies, remaining or newly created manufacturing jobs tend to be higher-skilled, with titles like robotics technician, automation engineer, and industrial data analyst becoming common. Workers who manually operated machines are being retrained to program and monitor robotic systems. For instance, instead of inspecting items on a production line, workers may supervise an AI vision system that flags defects, verifying and addressing issues raised by the AI. This human-AI teaming enhances productivity. A McKinsey study found that blending automation with human expertise can significantly raise manufacturing productivity, but it requires redesigning workflows and reskilling workers at scale. While manufacturing is a smaller share of the Australian economy compared to Germany or China, there’s a strategic push towards advanced manufacturing in areas like medical devices, aerospace, and renewable energy technology. Australian manufacturers, often SMEs, have been slower in adopting AI due to cost and scale barriers, but initiatives, sometimes government-supported, are introducing AI-based robotics to these firms. For example, some Australian food processing plants have adopted AI-powered sorting machines and collaborative robots that work alongside humans to pack goods, improving throughput while reducing strenuous labour.
Factories are becoming more tech-centric, with new roles like automation coordinators and engineers on the shop floor collaborating with production supervisors. AI analytics influence decision-making, with AI autonomously adjusting production schedules based on real-time demand data, changing the role of planners from manual scheduling to supervising AI decisions and handling exceptions. AI algorithms optimise inventory and logistics, reducing the need for large teams of schedulers. However, humans still handle supplier relationships and contingency management during disruptions. IT and operations teams are integrating, merging operational technology (machines, production engineers) and information technology (computing, data) with AI and IoT. Companies often have a “Chief Digital Manufacturing Officer” overseeing both the factory floor and data strategy.
Manufacturing workers face a dual challenge: upskilling and right-skilling existing workers, and attracting new tech talent to factories. Assembly workers learn basic coding or machine interface operation, and some companies partner with vocational institutes to teach robotics maintenance or AI monitoring. TAFE programs now offer certificate courses in industrial automation in Australia. Firms hire data scientists and AI specialists to build and refine algorithms for efficiency gains. To bridge the cultural gap between traditional manufacturing staff and new tech hires, companies focus on change management, such as cross-training engineers and technicians. AI brings safety and ergonomic benefits, as robots can take over dangerous tasks, making manufacturing safer and improving job attractiveness and health outcomes. Australia’s manufacturing modernisation plans explicitly cite automation to improve worker safety and enable older workers to extend their careers by offloading physically taxing work to machines.
Manufacturing is shifting to an ‘augmented workforce’ model, with fewer people doing repetitive assembly and more ensuring smooth automated systems and making higher-level decisions. New jobs like ‘digital twin’ specialists and 3D printing technicians emerge. For countries like Australia, which can’t compete on cheap labour, AI-driven productivity boosts competitiveness by combining human ingenuity with smart machines. Organisations must invest in training and foster collaboration between engineers, IT professionals, and production workers. Industry 4.0 doesn’t mean factories without people; it means people working smarter, with AI handling predictability and precision, and humans providing oversight, creativity, and critical thinking for continuous improvement.
Healthcare & Medical Services
Healthcare, a sector where AI’s impact could be transformative, still prioritises human touch. AI in healthcare ranges from algorithms that accurately diagnose medical images like X-rays and MRIs, to predictive models for patient deterioration, and administrative bots that transcribe medical notes or schedule appointments. AI aims to improve care efficiency, crucial for ageing populations and staff shortages. Healthcare professionals, like radiologists, find AI valuable assistants. AI image-analysis software can detect tumours or fractures on scans as accurately as human experts, helping radiologists diagnose faster and focus on complex cases. Similarly, pathology AI systems can scan slides for abnormal cells much quicker than lab technicians alone. Clinicians increasingly trust AI for specific tasks, like preliminary flagging of suspicious regions in mammograms, which radiologists then review. This speeds up workflows and improves diagnostic accuracy.
AI significantly reduces administrative burdens on healthcare workers. Doctors and nurses spend much of their day on documentation, data entry, and coding insurance information. AI-powered voice recognition and natural language processing auto-draft clinical notes and fill out electronic health records from conversations, saving clinicians time. AI chatbots and virtual assistants handle routine patient inquiries, appointment bookings, and triage for minor symptoms, freeing up reception staff and nurses for more critical tasks. For instance, Australia’s public health services have trialled virtual assistants that answer common health questions, reducing pressure on nurse call lines. Pharmacy automation reduces pharmacists’ manual workload, allowing them to spend more time consulting patients about medication management.
Healthcare job roles are being augmented rather than eliminated. AI is shifting workers’ focus, enabling more proactive care. Nurses can rely on AI monitoring systems to alert them to potential patient deterioration, reducing repetitive monitoring and paperwork. Doctors use AI treatment recommendation systems as a reference, but still exercise judgment in final decisions considering patient preferences and context. AI facilitates telemedicine by triaging cases, with chatbots handling initial history-taking for telehealth consults. New roles like clinical data analysts, health informatics specialists, and AI system integrators appear in hospitals. These professionals implement and manage AI tools, ensure data quality, and train clinical staff in using new systems. The government and universities in Australia have launched programs in digital health and health informatics to cultivate such talent, anticipating hospitals’ need for these hybrid roles.
Healthcare providers are integrating AI through innovation teams or digital departments within hospitals and health networks. Many large hospitals now have a Chief Digital Health Officer who oversees AI deployments, data governance, and digital strategy. Multi-disciplinary committees evaluate AI tools for safety and effectiveness before adoption. Clinical workflows are being redesigned, with AI continuously monitoring vitals and nurses only alerted to intervene when necessary. This shift to exception-based work improves efficiency but requires trust in the technology. Healthcare change management is challenging due to provider caution about the potential risks. As a result, AI adoption in healthcare has been slower and more regulated than in finance or manufacturing. However, the pandemic accelerated adoption as hospitals under strain turned to AI for managing patient loads and predicting resource needs, increasing clinicians’ awareness of its potential benefits.
From a workforce perspective, there are both concerns and opportunities. Concerns include the fear that AI could deskill professionals, such as doctors relying too much on AI diagnostic suggestions or nurses losing clinical judgement skills due to algorithmic decisions. There’s also the fear of job displacement in certain ancillary roles, like medical coding and billing, which could be greatly reduced by AI. The HIMSS notes that roles like medical coders are likely to diminish as AI takes on those tasks. Additionally, the initial implementation of AI can add to workload if systems are not well integrated, as some doctors feel they have to double-check AI outputs thoroughly. On the opportunity side, AI can substantially alleviate workforce shortages by improving productivity. Many health systems, including Australia’s, face chronic staff shortages, especially in nursing, aged care, and rural healthcare. AI can help fewer staff care for more patients by automating ancillary work and providing decision support. It can also extend the reach of specialists, allowing them to virtually oversee radiology diagnostics in remote clinics with AI doing the initial image analysis. Patients can gain more access to self-service and preventive care through AI-powered apps, which might reduce the burden on clinicians. Overall, job growth in health is still robust, with the WEF projecting net job creation in health and education sectors as populations age and investment in these services increases. Healthcare in Australia is rapidly growing, and AI is seen as a tool to improve care quality and patient outcomes, not a threat to jobs. However, roles will evolve, with more ‘health coaches’ or ‘patient AI support specialists’ guiding patients in using AI-driven healthcare tools, ensuring accessibility for those less tech-savvy, especially the elderly.
In conclusion, AI in healthcare enhances the workforce by handling diagnostics, monitoring, and logistics, improving efficiency and potentially clinical outcomes. While the human aspects of healthcare remain crucial, doctors and nurses can reallocate time from paperwork to patient interaction and advanced care. To fully realise this benefit, healthcare organisations must invest in clinician training, establish clear protocols for human oversight, and address ethical issues like AI bias. Australia’s universal healthcare system approaches AI as a support for clinicians and a means to increase access to care, such as improving rural health delivery. Jobs created around healthcare AI, from data scientists to AI ethicists, will bridge the gap between cutting-edge technology and compassionate patient care.
Public Service & Government
Public sector organisations – government agencies at the national, state, and local levels – are also undergoing change as AI technologies are introduced to improve public services and internal efficiency. Governments handle vast amounts of information and repetitive processes (think of processing tax returns, license applications, benefit claims, etc.), which makes them ripe for automation. AI and automation in the public sector are often applied in areas like document processing, citizen services, and regulatory enforcement. For example, many governments have started using AI chatbots on their websites to handle common questions from citizens (e.g. “How do I renew my passport?”), providing 24/7 service and reducing wait times at call centres. RPA (robotic process automation) bots are employed to transfer data between systems, so a person’s online form submission can be processed without an officer retyping it into another system. In Australia, the Australian Taxation Office has trialled AI to help flag potential fraud in tax returns, and Centrelink (welfare services) uses automated decision systems for some claims – though not without controversy and the need for human oversight (as the “robodebt” saga demonstrated, algorithms must be carefully governed). The overall impact is that routine administrative roles in government are beginning to shrink, while demand is rising for more tech-savvy policy analysts and IT staff who can implement and monitor these systems.
A clear example of AI’s impact is at the entry level of government jobs. A recent analysis suggests that entry-level administrative positions and clerical roles in the public sector are among those most at risk of automation. These include jobs like data entry clerks, filing clerks, schedulers, and basic customer service representatives at government service centres – roles that involve processing forms or providing standard information. AI can handle many of these tasks with chatbots, digital forms, and automated approval workflows. As AI and digital self-service options expand, governments may need fewer people to do tasks like manually inputting data or answering routine inquiries. Some public agencies have already implemented “one-stop” digital portals that use AI to guide citizens through services, reducing the need for in-person visits. However, rather than leading to massive layoffs, this is likely to result in redeployment of staff to more complex roles that AI cannot do. Governments still need human judgment for nuanced decision-making, case-by-case exceptions, and dealing with complex or sensitive cases (for instance, a social worker assessing a family’s needs, or a public health officer planning interventions). What we’ll see is public service workers using AI tools to be more effective – for example, inspectors using AI to analyse which establishments are at highest risk and should be inspected first (as some local councils do for food safety inspections using predictive analytics).
New job roles are emerging in government to support AI initiatives. A survey of public sector trends finds growing suggestions for positions such as AI system administrators, data governance officers, and AI policy specialists within government agencies. These roles ensure that AI systems are maintained, that data used by AI is high-quality and handled ethically, and that there is alignment with laws and regulations. We are also seeing titles like Chief Data Officer or Chief Digital Officer in government departments, reflecting the importance of data and AI strategy in public administration. In Australia, the Digital Transformation Agency and various state digital innovation units are actively recruiting such specialists to drive AI projects in areas like transportation (e.g. traffic optimisation using AI), environmental monitoring, and even in law enforcement (with appropriate safeguards). Additionally, governments will likely hire more human-AI collaboration specialists – people who design workflows that effectively integrate AI tools into public service delivery, ensuring civil servants are properly supported and trained.
One significant dynamic is the relationship between AI adoption and the size of the public workforce. If AI can handle more work, governments might control staffing costs by not hiring as many new employees to replace retirees, rather than laying off current employees. In fact, a lot of public sector employment impact may come via attrition – as older workers retire, some of their positions (especially in clerical grades) might be eliminated if their functions are fully automated. For current employees, AI can be empowering. It can reduce overtime and burnout by taking over drudgery. For instance, during surges like a sudden spike in unemployment benefit applications, AI can help process claims faster, reducing backlogs and stress on staff. There is evidence that in the U.S., some federal agencies are using AI to avoid the need for mandatory overtime in crunch periods – presumably the same approach could apply in Australia’s Centrelink or Medicare services to handle peak loads. Moreover, AI might enable governments to offer more personalised services – for example, analysing data to identify citizens who need proactive outreach (like identifying at-risk individuals for certain health programs), tasks that would be hard to do manually at population scale.
Public organisations are undergoing significant structural and cultural changes due to AI. Traditional bureaucratic hierarchies and procedures are being challenged by data-driven, agile processes introduced by AI. Algorithmic recommendations can streamline decision-making, reducing the need for multiple layers of approval. This shifts human work from routine processing to exceptions and policy. Employees must trust and understand AI to some extent. Change management in public agencies involves training staff on AI tools and emphasising its role as a tool to assist, not punish or surveil them. Transparency is crucial, as public sector decisions face high scrutiny. Agencies must ensure that AI decisions can be explained and justified to maintain public trust. Australia’s public sector has established ethical AI guidelines to ensure accountability and bias mitigation in automated decision systems. Ethics and governance specialists, an emerging role in public service, oversee responsible AI use. They test AI systems for fairness before deployment, for instance, in job applicant screening or resource allocation.
Another aspect is the relationship between public employee unions and worker rights. In Australia, the main public sector union (CPSU) and the Australian Council of Trade Unions (ACTU) have emphasised that while they support AI adoption in government, it must not compromise worker rights, working conditions, or job security. They advocate for meaningful consultation on AI changes and possible agreements on redeployment and training for affected staff. Public sector leaders must engage employees in the AI transition, ensuring they feel involved and have pathways to upgrade their skills.
AI offers a chance for governments to become more efficient and citizen-centric. Routine tasks can be automated, allowing public servants to focus on complex social problems and personalised assistance. A Route Fifty article predicted that over the next five years, AI will reshape government employment by automating tasks, transforming skills, and creating opportunities for impactful public service. Governments need proactive leadership, investment in workforce development, and ethical, inclusive AI deployment. For instance, Australia’s government in early 2025 released a “Future of Work” report via a parliamentary inquiry, recommending campaigns to educate the public about AI’s opportunities and challenges and to encourage training and upskilling in industries most exposed to AI, including public service. This shows a policy-level understanding that the government must lead by example in preparing its workforce for AI. Public service jobs will become more tech-heavy in skills but also more rewarding, as bureaucrats shift from form-processing to problem-solving roles. New career paths like “digital service designer” or “civic data scientist” will attract talent interested in both technology and public good. If managed well, AI in government could mean faster, smarter services for citizens while public employees perform higher-value work with less drudgery.
Education & Training
Education plays a dual role in the future of work: it’s affected by AI and responsible for preparing the workforce to thrive with AI. AI is transforming teaching and learning in schools, colleges, and corporate training environments. Intelligent tutoring systems, personalised learning platforms, and automated grading tools are among the AI-driven changes. For educators, AI offers tools that automate tasks and enable personalised instruction. For instance, AI can handle grading multiple-choice or open-ended exams, freeing teachers from hours of marking. AI tutors provide practice exercises, instant feedback, and answer frequently asked questions, helping teachers manage classes with students at different paces.
The COVID-19 pandemic accelerated the adoption of AI in education. Many Australian schools and universities adopted AI-driven proctoring tools, chatbots, and learning analytics. Some of these tools remain in use in physical classrooms. The role of the teacher is evolving, not diminishing. AI can’t replace mentorship, motivation, and social-emotional support. Instead, teachers use AI data to inform their approach. For example, a learning analytics dashboard might show struggling students based on AI’s analysis of homework answers, enabling targeted intervention. In higher education, professors use AI to analyse large datasets to identify common themes or misconceptions, then address them in lectures.
New jobs in education are emerging due to AI. Instructional designers now need to know AI to design courses that use AI tools effectively. Larger institutions have roles like “learning experience platform manager” or “education data analyst” who deploy and maintain EdTech, including AI components. Even in primary/secondary schools, districts hire IT specialists or “digital curriculum leads” to integrate technology into teaching. Australia is focussing on teacher training in new technologies, including digital capability standards for teachers that include understanding of AI in education. As AI becomes more prevalent, schools are teaching students about AI basics like coding, computational thinking, and AI ethics to prepare them for the AI-literate future. This creates demand for professional development for teachers on AI and its implications. Universities like MIT, Stanford, and our own institutions offer MOOCs and workshops on AI for educators.
Education is a sector where job growth is predicted in the age of AI, unlike other industries. The WEF’s Future of Jobs analysis shows that education and training will see a significant job gain of +10% by 2027, creating millions of new teaching jobs globally. As technology changes jobs, people need more education and reskilling, leading to the rise of online courses, coding bootcamps, micro-credentials, and corporate learning programs. Training and adult education are booming, with roles like corporate trainers, curriculum developers, and career coaches in high demand. AI helps scale these efforts, but human educators, mentors, and program managers are still needed. In Australia, the government has invested in programs to reskill workers from shrinking industries into growing ones, partnering with TAFEs and trainers. This human-intensive process is augmented by tools like AI-based skills assessment platforms.
AI is revolutionising traditional education, prompting changes in organisational structure and pedagogy. Universities are adopting AI for research and administration, while academics consider flipped classrooms and blended learning models with AI tutors. This shifts professors’ workflows, replacing repetitive lectures with high-quality recorded or AI-interactive modules for content delivery, allowing for more discussion, problem-solving, and mentorship. Some universities have experimented with AI teaching assistants, like Georgia Tech’s Jill Watson, which answered student questions in an online course, though students remained unaware of its AI nature for weeks. Similar pilots in Australia have shown AI TAs can handle large volumes of student Q&A, but still require faculty oversight to ensure accuracy and fill in gaps.
Ethical and social considerations are crucial in education’s adoption of AI. Concerns include privacy (AI collecting student data), bias (AI potentially disadvantaging certain students), and over-reliance (students using AI tools like ChatGPT for assignments, raising questions about learning integrity). Educators and administrators must craft policies around AI use, such as clear guidelines for homework and ensuring AI-driven content is accessible to students with disabilities. Teachers and professors face a learning curve and resistance, with some worried AI could undermine their authority or jobs, though evidence suggests it’s more of an aid. The introduction of generative AI in late 2022/2023 initially caused alarm about cheating, but the narrative is shifting towards using AI as educational aids. Some Australian schools have now allowed generative AI with supervision, teaching students how to critically evaluate AI-generated content and use it to improve their work. This highlights the need for both teachers and students to learn new skills for an AI-rich environment, including critical thinking, data literacy, and collaboration with AI.
In terms of job creation, we may see more specialist roles like AI curriculum specialists, education technologists, and expansion of the EdTech industry, which will hire software developers, content experts, and learning scientists. Australia’s growing EdTech sector, with startups working on AI math tutors and literacy apps, creates jobs at the intersection of education and technology and provides new tools for schools.
In summary, AI in education aims to empower educators and learners. It personalises learning by providing tailored paths, which is challenging for teachers managing large classes. For educators, AI automates administrative tasks and offers data insights on student progress, allowing them to focus on inspiring, fostering critical thinking, and addressing emotional and social learning needs. Education organisations may see staff roles shift, with fewer teaching assistants grading and more in mentoring and developing digital content. As lifelong learning becomes common, many teachers will transition into training working adults. Ensuring educators are not left behind requires professional development in AI usage and continuous curriculum updates for relevant skills. Given education’s crucial role in adapting other sectors, investing in teachers and trainers is a critical strategic move for countries navigating the future of work. AI can assist, but human educators remain key to cultivating the next generation of talent alongside intelligent machines.
Enterprise Organisations (Cross-Industry Business Implications)
While we’ve discussed specific industries, there are broader trends affecting enterprise organisations across sectors like retail, telecommunications, banking, mining, and more. Any medium-to-large organisation now needs to integrate AI into its business strategy and operations. This impacts job roles and organisational structure in enterprises with common themes:
New corporate roles and team structures
Many companies have dedicated AI or data science teams to drive innovation. Roles like Chief AI Officer or Head of AI are increasingly seen in tech-forward companies and some Australian enterprises in banking and retail. These leaders coordinate AI strategy across departments. Companies are embedding data scientists and machine learning engineers into traditional departments, for example, an insurance firm might have data scientists working with claims managers to build AI models for risk assessment. Cross-functional teams are the norm for AI projects, uniting IT experts with business domain experts. This breaks down silos in large organisations.
Agile project-based structures
Instead of strictly segmented departments, enterprises spin up multidisciplinary teams to tackle specific AI initiatives. This requires flatter communication structures and more collaboration between previously separate functions (IT, operations, marketing, etc.).
Organisational decision-making and hierarchy
AI’s rapid data analysis and decision-making capabilities can impact management. Middle management roles, which involved gathering information and reporting, may decline if AI tools provide real-time dashboards and insights for decision-makers. Routine management decisions like approving expenses and scheduling staff can be automated, leading to leaner structures with managers focussing on high-level guidance. However, managerial roles evolve, with managers overseeing both people and AI systems. The concept of a ‘robot boss’ (e.g., AI scheduling software directing employee shifts) raises worker experience concerns, prompting companies to ensure AI supports managers rather than replacing human judgement in staff management.
Workforce dynamics and culture
Enterprises are increasingly recognising the need to foster a culture of data-driven decision-making and continuous learning to leverage AI effectively. Employees at all levels are encouraged to become more comfortable with data analytics and AI outputs. For instance, marketing teams use AI-based tools for customer segmentation and ad content generation, while sales teams employ AI predictions to identify leads. This democratisation of AI, where knowledge workers have access to AI-assisted tools, is a significant shift. Training is provided to upskill existing employees on these tools, such as training the finance department on an AI-powered forecasting system or teaching HR staff how to interpret AI-based resume screening results. The everyday use of AI assistants, like using Microsoft’s AI features in Office or drafting emails and reports, is becoming common, altering the tasks of many white-collar workers. People may spend less time researching and writing first drafts because AI can do those tasks, and more time reviewing, refining, and making decisions, effectively moving up the value chain of their work.
Most enterprises are still learning about AI. McKinsey research shows that almost all companies are investing in AI, but only about 1% feel they’ve fully adopted it at scale. This means organisations are experimenting with AI, perhaps with pilot projects or AI features in products, but few have fully transformed their core processes. The biggest barriers are often organisational, like a lack of skilled talent and leadership to drive change, rather than the technology itself. This means companies need to reskill their existing workforce and hire new talent to close the gap. This explains the surge in corporate training budgets focused on digital and analytical skills. Many large enterprises in Australia (like major banks and telecom companies) have announced multi-year workforce transformation programs to train thousands of employees in data science, agile ways of working, and AI tool usage, instead of just layoffs and new hiring. This helps fill skill gaps, fosters employee buy-in, and reduces fear. When workers see the company investing in their development to work with AI, they’re more likely to view it as a tool for their empowerment, not a threat.
AI-driven job creation in enterprises often involves new departments or business lines. For instance, the rise of e-commerce and AI-driven customer analytics in retail created roles in digital marketing, e-commerce platform management, and data analytics. In mining and agriculture, AI and drones led to new roles in remote operations centres. In finance, AI growth spurred fintech divisions, with roles like UX designers for automated financial advisor apps and compliance experts overseeing AI algorithms for trading. Many enterprises also create ethical AI committees or roles to evaluate risks of AI. This reflects the importance of trust and social responsibility in AI initiatives, often involving multidisciplinary knowledge.
Enterprise organisations must manage workforce transition as AI changes job content. AI automates tasks, but roles don’t disappear. For example, a customer support agent using an AI suggestion tool can handle more calls or chats, allowing companies to handle growth with the same number of staff or redeploy staff to handle more complex customer issues. While job numbers may not drop immediately, job nature shifts, and fewer new hires might be needed for certain entry-level functions. Enterprises must manage this through attrition and retraining rather than abrupt cuts that harm morale and employer brand. Many companies find value in redeploying people from declining roles into emerging ones, for example, turning back-office clerks into new sales or customer success roles that AI can’t do. Australian businesses, facing a tight labour market and skills shortages in tech, are motivated to retrain internal staff for tech-related roles, which is often faster and cheaper than hiring externally.
Global vs Australia context
Australian enterprises are rapidly adopting AI, especially in sectors like banking, insurance, and retail. While they often benefit from imported innovations, they sometimes lag in local R\&D. However, Australian companies are known for early adoption of business-value technologies, such as AI in fraud detection and customer service chatbots. Professional service firms have strong AI practices that promote knowledge sharing. The government’s support, including grants and frameworks for responsible experimentation, also helps enterprises. Culturally, Australian workplaces prioritise fairness and consultation, which can facilitate AI implementation through stakeholder engagement.
Enterprises are undergoing a digital transformation where AI is a key pillar. Jobs at all levels are being augmented, with AI-equipped tools for frontline employees, insights for analysts, dashboards for leaders, and new roles ensuring AI runs smoothly. The workforce is not being reduced; rather, it’s being realigned, with some jobs decreasing, many evolving, and some increasing. Studies like those from Deloitte and WEF indicate that AI will create more jobs than it eliminates in the long run, but these jobs will require different skills and often higher technical or cognitive capabilities. Enterprises must focus on reskilling, change management, and culture to fully leverage AI. The next section will explore these strategic implications in detail, providing recommendations for organisations to prepare their people and structures for sustainable success in the AI-augmented future of work.
Strategic Implications for Organisations
AI’s impact on work is profound but adaptable. Organisations and employees’ outcomes depend on their strategies. Leaders must be proactive and human-centric. Here are strategic recommendations for organisations to prepare for and harness AI changes. These recommendations cover workforce development, organisational change management, technology governance, and cultural considerations. They aim to ensure positive outcomes for businesses and people. We emphasise agility, inclusivity, and ethical integrity, reflecting global best practices and Australian values.
1. Invest in Continuous Learning and Workforce Reskilling
Lifelong learning is the new norm in the future of work. As AI and automation change job roles, organisations must ensure their workforce can keep learning new skills. This goes beyond occasional training; it means making learning a part of the company’s DNA. Many leading companies now prioritise reskilling and upskilling as a core strategic goal, not just an HR function. Examples include AT&T’s initiative to retrain its workforce in new tech skills and Telstra’s program to upskill thousands of employees in digital competencies. A culture of continuous learning might include dedicated learning hours, on-demand e-learning platforms with personalised course suggestions, tuition support for external courses, and clear career pathways that encourage employees to develop future-oriented skills like data analysis, machine learning, and design thinking.
A robust reskilling strategy should start with a skills gap assessment. Organisations can use AI tools to analyse their workforce’s skills against the skills needed for emerging roles. Some HR departments use AI to map this out and suggest personalised learning plans. Once gaps are identified, targeted training programs can be developed. For example, an Australian bank might find it needs more data analysts in operations. It could offer an internal “data analytics bootcamp” to employees in operations roles with the aptitude, moving them into higher-skilled positions instead of layoffs. This approach not only fills the role with someone with organisational knowledge but also signals to staff that there’s a future for them in the company. An HBS study found that employees are enthusiastic about reskilling when they see a clear benefit and leadership support, especially if it leads to advancement or new opportunities. Reskilling can also be a powerful tool for retention and engagement.
Digital literacy for all is crucial, especially for AI-driven systems in various roles, from HR to marketing to finance. Companies should provide basic AI literacy training, explaining AI capabilities, interpreting outputs, and effective tool usage. This demystifies AI and reduces resistance. Technical teams may need deeper training in data science, machine learning engineering, or AI project management. Partnerships with educational institutions can deliver tailored certificate programs. The Australian industry-academic landscape supports this, with collaborations like Swinburne University and Adobe on digital marketing training and the Queensland government partnering with Coursera for tech upskilling.
Reskilling efforts should be inclusive, considering diverse training needs and starting points. Organisations should tailor opportunities, ensuring support for older workers adapting to new tools or flexible learning options for part-time workers. Embracing diversity, equity, and inclusion (DEI) in skill development encourages underrepresented groups to enter tech roles through mentorship, scholarships, or diversity targets. Given the gender disparity in AI-heavy fields, companies must bridge this gap through proactive development and recruitment initiatives. This is socially responsible and leads to better innovations and bias mitigation in AI design.
Organisations should treat human capital development as a continuous investment, similar to software or equipment updates. This approach aligns with findings from the OECD and others that investing in skills leads to better employment outcomes despite technological advancements. Reskilling workers helps companies fill new roles internally, reduce layoffs, and create a more adaptable workforce. A concrete action plan could include establishing a reskilling task force, allocating training time or budget, recognising employees for new competencies, and leveraging AI in HR systems to track and encourage skill acquisition. The goal is a resilient workforce capable of transitioning roles as AI evolves, empowered by technology rather than threatened by it.
2. Embrace Change Management and Co-Design the AI Transition
Introducing AI into an organisation is a significant change management exercise that requires careful attention to the human side of change. Employees need to understand the reasons behind AI adoption, its impact on their jobs, and the support they’ll receive. Fear of job loss or ambiguity about roles can lead to resistance, even sabotage. Transparent and inclusive change processes can mitigate this. Early employee involvement is recommended, such as consulting on task prioritisation, running pilot programs, and incorporating feedback before a full rollout. Co-design ensures the AI benefits workers and fosters ownership.
Communication is crucial. Leaders should clearly articulate the vision, such as improving customer response time and freeing employees from repetitive queries to focus on complex problems. They should be honest about workforce implications, explaining job evolution and supporting it through training, reassignments, etc. If certain positions may be redundant, open dialogue and a plan, like retraining opportunities or transition packages, are better than rumours. Australian corporate culture values consultative management, so forums, Q&A sessions, and even employee union involvement in change planning can build trust and avoid conflict. For instance, involving the union from the start when automating a clerical process could build trust and avoid conflict.
Demonstrating quick wins and benefits to employees is crucial. Change management theory suggests that people are more likely to embrace change if they see positive outcomes early. When an AI solution is implemented, measure and share its impact on business and employee-centric terms. For instance, a hospital implementing an AI scheduling system might share that last-minute shift changes dropped by 30%, leading to more stable schedules. These wins should be publicly acknowledged to reinforce AI’s positive impact.
Change champions, or “AI ambassadors,” can be highly effective. These are early adopters or enthusiasts who mentor others. Peer learning is powerful, as colleagues often trust their peers’ experiences more than top-down mandates. Identifying and supporting a network of champions across departments can facilitate adoption. Many companies create cross-functional “AI adoption teams” that include IT personnel and user representatives to troubleshoot and iterate on implementation.
Leadership behaviour is equally important. Leaders should model a positive attitude towards AI, showing their willingness to learn and use new tools. They should also empathise with employees’ concerns. Managers should not simply use AI to demand more from workers (“the AI will increase your productivity targets now”). Instead, incorporate employee feedback on workload and set realistic expectations. Remember that productivity may dip initially during the learning curve, and management should account for this and not punish short-term drops that occur as people adapt.
Consider redesigning workflows and job roles collaboratively. Instead of removing tasks AI will do, proactively redefine roles to focus on remaining and new tasks. This may involve updating job descriptions, reassigning responsibilities, and altering team compositions. Involve employees in this redesign by asking what they could do more of if the AI handles certain tasks. This makes change feel like something done with them, not to them. Employees often have insights into their work that an outside process designer might miss.
For instance, when a bank introduces AI chatbots for first-line customer inquiries, engage customer service reps to determine how their role shifts. They can help define new service protocols to ensure seamless handoffs from bot to human agent. Shaping these protocols increases employee trust in the chatbot and reduces its perception as a competitor.
From a change management perspective, set up feedback loops post-implementation. AI systems need fine-tuning and encourage employees to report issues or suggest improvements. Rapidly incorporate feedback through iterative software updates or revised guidelines. This signals the organisation’s value for employees’ expertise and its focus on making the technology work for them, not just for cost savings. In Australia, many companies pride themselves on collaborative workplace relations; leveraging this to manage AI changes can be a competitive advantage, achieving smoother adoption with less disruption and more engagement.
3. Redesign Jobs and Organisational Structures to Harness Human-AI Collaboration
To fully realise AI’s benefits, organisations often need to reorganise work at both the job and structural levels. Simply adding AI to existing processes isn’t enough; the best results come from reimagining processes with a mix of human and AI strengths in mind. This may involve reallocating tasks, creating new roles, or reconfiguring teams or hierarchies. The key is to let humans and AI excel at their respective strengths. AI excels at data processing, pattern recognition, and routine decision rules, while humans excel at intuition, empathy, creativity, and handling novel situations. Jobs should be redesigned to emphasise the latter for humans, with AI handling the former.
One approach is “job crafting” in an AI era. Organisations can review each role and identify tasks that can be offloaded to AI or automation. These tasks can be centralised or managed by a smaller group overseeing the AI (for quality control), while the original role either takes on additional higher-value tasks or focuses more deeply on the remaining ones. For example, in a customer support centre, AI might handle password resets and simple FAQs, freeing human support agents to focus on more complex issues. This may involve training agents in deeper problem-solving skills or soft skills for customer engagement, since they won’t be spending time on routine issues.
Organisations are shifting towards agility and flexibility. Cross-functional teams are useful for deploying AI, so many organisations are flattening silos to enable continuous collaboration between IT, data, and business units. Frameworks like “product squads” or “chapters and guilds” (in agile organisations) are being adopted to allow expertise to flow where needed. AI projects often cut across departments, so companies may establish steering committees or matrix structures to manage these interdisciplinary efforts. Banks and telecoms in Australia have notably embraced agile organisational structures in recent years, which makes integrating AI easier because teams are already empowered to experiment and iterate.
Another structural change is embedding AI governance at a high level. Boards and executive teams should include oversight of AI initiatives as part of governance, similar to financial or risk oversight. This could mean setting up an AI ethics committee (either at board level or reporting into it) to guide strategic alignment of AI with company values and societal expectations. This is especially strategic in Australia given strong regulatory and community emphasis on issues like privacy and fairness. For example, with new regulations (like Australia’s Consumer Data Right and potential AI regulations being discussed), having leadership ensure compliance and ethical alignment from the outset is key.
We should also consider new hybrid roles in job design. Many roles will require a combination of domain expertise and AI savvy. Organisations should recognise these in job architectures. For example, roles like “AI-augmented analyst” could involve interpreting AI results and traditional analysis. Another example is maintenance technicians in manufacturing now needing IT skills to update robot software. Recognising and formalising these hybrid skill sets in job descriptions, career paths, and compensation is important for attracting and retaining the right talent. It shows employees that these advanced skill combinations are valued. Some companies are even creating dual career ladders where tech expertise is valued as much as managerial duties to keep top technical talent engaged.
Human-AI collaboration should be a design goal for processes. This means designing interfaces and workflows that make AI outputs easy for humans to interpret and act on. Organisations might need to bring in user experience (UX) design principles internally for employee-facing AI tools. How an AI flags a potential fraud case for a human investigator affects the human’s ability to trust and effectively use it. Investing in good design and change management for these interfaces reduces errors and increases adoption.
Finally, HR policies should support organisational agility. Rigid job classifications and strict departmental KPIs can hinder flexibility. Companies should allow talent movement to where new needs arise with less red tape and create performance metrics that reward collaboration and innovation. For instance, measuring a team’s overall outcome rather than individual task counts can make them more willing to integrate an AI that changes task distribution. Similarly, budgeting processes might need to allow funding for cross-department AI projects, not just within siloed budgets.
Redesigning the organisation for an AI-enabled world often aligns with existing trends like digitisation, agile management, and innovation. AI just intensifies these needs. Companies that reconfigure themselves to be nimble, interdisciplinary, and innovative will find it easier to deploy new technologies and pivot as needed. Australian firms complacent with old hierarchies may need to accelerate organisational reform to keep pace with more dynamic global competitors who leverage AI. This redesign is an iterative journey: as AI capabilities expand, organisations might revisit and tweak roles and structures regularly. The key is remaining flexible and responsive, making organisational structure a facilitator of human-AI synergy, rather than a blocker.
4. Foster an Ethical, Inclusive, and Trust-Centric AI Culture
Organisations adopting AI must prioritise ethics, transparency, and inclusion to maintain trust with employees and the public. Missteps like unintentional discrimination or employee data misuse can damage morale, brand reputation, and lead to legal consequences. Building a responsible AI culture is not altruistic but a risk management strategy and a long-term value proposition.
First, companies should establish clear ethical guidelines for AI, aligning with corporate values and external frameworks like the OECD AI Principles or Australia’s Ethical AI framework. These guidelines should cover issues like bias avoidance, accountability, data privacy, and security. All AI projects should be evaluated against these criteria. For instance, an AI hiring tool might require regular audits for bias and ensure human review of final hiring decisions to avoid algorithmic blind spots.
Inclusive design and development of AI involve including diverse voices in its creation and deployment, from the development team to user feedback. Organisations should proactively consider the potential impacts of AI on certain groups, such as women in clerical roles or older workers less tech-savvy. For internal systems, perform an ‘AI impact assessment’ on jobs to identify affected groups and potential solutions like reskilling or reassignments. This aligns with the ILO’s call for social dialogue around AI, ensuring workers have input and that AI enhances job quality for all. Transparency is crucial for trust. When introducing AI, be transparent about data usage and AI decisions. If an AI monitors productivity, clearly inform employees about the collected data, analysis, and management use. Secretive surveillance breeds mistrust, while sharing AI-driven insights openly can empower employees. For instance, providing an AI analysis of sales performance alongside improvement tips can be seen as a helpful coaching tool, not Big Brother, if done with respect and consent.
Some companies adopt algorithmic transparency with external stakeholders. For instance, banks publish plain-language explanations of their AI credit-scoring models and decision-making factors to build customer trust. In sectors like finance and healthcare, regulators may soon require such transparency. Being ahead of the curve in responsible disclosure can enhance reputation. In Australia, where consumer and citizen trust is emphasised, demonstrating an open and ethical approach to AI can differentiate a business positively.
Promoting diversity in AI teams is crucial. Ensuring that AI system builders and trainers come from varied backgrounds reduces bias and blind spots, leading to more robust solutions. Companies should strive for diversity in tech hiring and partner with educational institutions or scholarship programs to widen the funnel of underrepresented groups in AI fields.
Establishing an AI ethics board or task force internally is also important. It might include members from legal, HR, risk, and technical teams, as well as outside advisors or academics for perspective. They can review high-impact AI deployments, handle ethical dilemmas, and advise leadership. Even a mid-sized enterprise can have a smaller committee performing a similar function. The key is that there’s a checkpoint where questions of fairness, impact, and accountability are addressed before and during AI usage, not just after a scandal hits.
Another key factor is employee involvement. Create a culture where anyone can voice concerns about an AI system without fear. Encourage employees to speak up if they notice unfair or harmful behaviour. Ground-level employees may spot issues that executives or developers miss. Provide clear channels for feedback and assure employees that it will be taken seriously. This could involve existing ethics hotlines or internal social platforms.
Finally, building a trust-centric culture extends to external communication about AI. Avoid overhyping or making unrealistic promises; this leads to public distrust. Educate customers and partners about how you use AI to improve products or services and the safeguards you have. For example, a healthcare provider using AI for diagnostics can reassure patients that AI assists doctors, not replaces their judgment, and that any findings are double-checked by qualified clinicians. This messaging ensures stakeholders understand that human responsibility remains central.
In Australia, businesses operate in a context of strong consumer rights and a cautious stance on data privacy (with laws like the Australian Privacy Act and the OAIC). Embracing an ethical approach to AI is not just about avoiding negative outcomes; it’s also about aligning with societal values of fairness, equality, and safety. If done right, it can become part of the brand, showing that the company is innovative and principled. As AI becomes more pervasive, companies that maintain trust will have a competitive edge in customer loyalty and attracting talent (as employees increasingly seek to work for responsible, purpose-driven employers).
5. Develop a Clear AI Strategy and Strengthen Leadership for the AI Era
Preparing for the future of work with AI requires strategic alignment and strong leadership commitment. Organisations should treat AI adoption as a fundamental strategic priority, integrated with business goals and workforce planning. This involves creating a clear AI roadmap that identifies areas where AI can add value and aligns investments accordingly. The roadmap should consider timelines and include milestones for workforce transition.
Leadership should champion this roadmap. Successful AI-adopting companies often involve the CEO and top team in driving the agenda, not just IT. They ensure alignment between tech and HR initiatives, such as rolling out an AI tool with training and new KPIs. Leaders should ask questions like: “Do we have the talent to achieve our AI goals? If not, do we build or buy it? How will AI affect our organisational design in five years, and are we making adjustments now?” Forward-thinking leadership can shape the impact of technologies by guiding their deployment in synergy with human capital development.
With AI rapidly evolving, strategic planning must include scenario planning and flexibility. Predicting how AI will change work by 2035 is impossible, but companies can prepare by considering scenarios like automating 50% of current tasks or a new AI platform disrupting their industry’s service model. This may involve diversifying skill sets and staying informed about technological trends. In Australia, businesses can collaborate through industry councils or initiatives to stay informed and influence AI direction in their sector.
Leadership in the AI era also means stewarding cultural change. Leaders must embody values like learning, ethics, and inclusion. Engaging with AI tools, showing curiosity, and admitting challenges set a tone for learning and adaptation. Conversely, disengaged or cost-saving leadership can lead to cynicism or fear. Harvard Business School research shows that optimistic leadership that invests in people is crucial for successful technological change implementation. Consistently reinforcing this optimistic but realistic framing (“the sky is not falling, but we need to respond and adapt” as MIT’s Autor put it) is essential.
Governance should include metrics and accountability. Companies track financial returns on investment, so they should track returns on AI and automation. Metrics include productivity improvement, quality gains, innovation outcomes, and workforce metrics like job satisfaction, internal mobility rates, and skill acquisition. If the goal is to augment jobs, measuring the percentage of employees retrained into new roles vs. laid off after AI adoption could be a KPI for the leadership team. This holds leaders accountable for following through with the ‘people first’ rhetoric. It could even be tied to executive performance evaluations or bonuses, signalling seriousness. In Australia, where corporate social responsibility is increasingly expected by investors (through ESG scores and the like), demonstrating responsible technological change can be part of brand equity.
Collaborating with external stakeholders is a strategic move. This includes governments (for shaping policy and benefiting from grants), educational institutions (to ensure the education system produces the skills needed and partner on research or training), and even competitors (in non-competitive issues like developing industry standards for AI ethics or safety). The future of work is a societal issue, not just a company issue. In professional services sectors, alliances like the World Economic Forum’s reskilling initiative, where companies collectively pledge to reskill millions of workers, or in manufacturing, companies pooling resources to train workers in advanced manufacturing skills through joint apprenticeships, demonstrate this. Australian industry groups and the government are likely to support cooperative efforts to mitigate AI disruption, with potential tax incentives for reskilling or government-industry partnerships like the Digital Skills Organisation. Strategically, tapping into these can amplify individual organisation efforts and ensure a smoother ecosystem for the future of work (as suppliers, clients, and the broader talent pool adapt, not just your own firm in isolation).
In summary, preparing for AI’s impact is a leadership and strategy challenge as much as a technical one. It requires vision, planning, and a people-centric approach from the top. Organisations that navigate this well will be more innovative, productive, and attractive places to work, turning AI into a competitive advantage with a workforce that’s not fearful of the future but actively building it.
Conclusion
Artificial Intelligence is revolutionising the future of work, not by making humans redundant, but by transforming how work is done and the value humans bring. This white paper examines AI’s current and future impacts on organisations and their people, drawing on data-driven insights and trends across various sectors. Several key findings emerge:
AI as a Driver of Job Transformation
AI will automate routine tasks and job categories, but it will also create new roles and emphasise uniquely human skills. Around 23% of jobs globally are expected to change significantly by 2027 due to AI and related trends, indicating a shift in work rather than job losses. Technological change has always led to new jobs, and most jobs today didn’t exist in the early 20th century. Humans and machines will work together, each focusing on their strengths.
Sectoral Variations and Opportunities
AI’s impact varies across industries. In professional services, it boosts efficiency, shifts roles towards consultative and analytical tasks, and creates demand for data-savvy professionals. In manufacturing, AI-powered automation enhances productivity and safety, changes roles, and creates ‘smart factories’. Healthcare uses AI for diagnostics and reduces administrative burdens, enabling patient-centric care. Public sector organisations adopt AI to improve service delivery and reduce clerical workload, making government more responsive. Education utilises AI in teaching and equips learners with AI skills, growing as lifelong learning becomes essential. Australia’s high-skilled workforce, lower automation risk, emphasis on ethical AI use, and inclusive workforce policies are local nuances.
Human-Centric Change Management is Critical
The success of AI integration depends on prioritising people. Organisations that support their workforce through reskilling, open communication, and involvement in the change process are more likely to benefit from AI without alienating employees. Ignoring the human side can lead to resistance, low morale, and reputational damage. This report suggests strategies like continuous learning programs, co-designing AI implementations with employees, and transparent ethical guidelines to navigate the transition responsibly. AI should be viewed as a tool for humans, not against them, a message that must be communicated from the C-suite to the front lines. As Saadia Zahidi of the WEF said, investing in education, reskilling, and support structures is the clear way forward to ensure individuals remain at the heart of the future of work.
Leadership and Strategic Readiness
Preparing for AI’s impact is a leadership challenge. Organisations that thrive will have leaders who articulate a compelling vision of an AI-augmented future, align technology adoption with business strategy, and foster a culture of agility, trust, and innovation. This involves engaging with broader ecosystems, collaborating on standards, working with governments on policy and skill development, and addressing societal concerns like equity and job quality. Leaders must be humble and forward-thinking, acknowledging uncertainties and risks (like biases and displacement) openly, and adapting strategies as AI evolves. Many employers see AI as a net positive, with nearly half expecting it to create more jobs than it eliminates, but realising this outcome requires intentional action and effective change leadership.
In light of these findings, what should organisational leaders do now? I recommend several calls to action:
Start with a Workforce Audit and Dialogue: Assess your organisation’s exposure to AI-driven change. Which roles are most automatable? Which could be amplified with AI? Engage employees in this assessment; their insights are invaluable and involving them sets a cooperative tone. Use this to identify priority areas for training or redeployment. In Australia, where 36% of jobs have the potential to be substantially changed by automation, such audits can inform both company plans and feed into national discussions on workforce development.
Build an AI-Ready Talent Pipeline: Don’t wait for a skills crisis. Partner with educational institutions, invest in in-house academies, or join industry consortia to ensure a steady flow of AI literacy and expertise into your ranks. Consider offering scholarships or apprenticeships in AI and related fields, including to underrepresented groups (strengthening DEI and addressing talent shortages simultaneously). Ensure that current employees have pathways to transition into emerging roles – for example, create junior data analyst roles that operational staff can move into after training, rather than only hiring externally.
Pilot, Learn, Scale – and Iterate: Identify a few pilot projects where AI could add clear value and implement them in controlled settings. Use these to learn what works technically and culturally. Collect data on outcomes and feedback from employees and adjust accordingly. Once proven, scale up gradually. This iterative approach helps build internal capability and confidence. Celebrate the successes (e.g., “AI reduced processing time by 50% in pilot, employees report less stress on tedious tasks”), but also transparently discuss any issues and how they were resolved. This builds trust in the technology and in management’s handling of it.
Embed Ethics and Inclusion from Day One: Don’t make ethics an afterthought. Whether it’s assembling diverse development teams, conducting bias audits on AI systems, or setting up review boards, bake ethical considerations into every AI initiative. Train your workforce on ethical AI use (e.g., for those using AI in decision-making roles, ensure they understand its limits and the importance of human oversight). Publicly committing to ethical AI principles can also reassure stakeholders. Remember that embracing responsible AI is not just morally right but often legally and commercially prudent – avoiding pitfalls and fostering goodwill. As the saying goes, “trust is earned in drops and lost in buckets”: each responsible action builds trust, while one scandal can set it back immensely.
Plan for Transition, Not Just Implementation: Finally, craft transition plans for roles that will change. This could mean phasing automation to align with natural attrition, offering generous early retirement or severance coupled with outplacement support where necessary, and more ambitiously, guaranteeing that anyone whose job is displaced by AI will be given opportunities (and training) to apply for new roles within the organisation. Such commitments, where feasible, can greatly alleviate fear and demonstrate that the company values its people. They also reflect the social contract approach that Australian businesses often espouse – that employees are stakeholders to be treated fairly in change. Future-looking companies might even commit to metrics like no net job loss from AI without equivalent creation of new internal opportunities. Even if not always fully achievable, striving toward that ideal influences decision-making in a positive way.
In conclusion, the future of work in the age of AI is not a predetermined fate but a journey that organisations and societies can shape. As we stand in 2025, we are at a juncture much like past industrial revolutions – facing uncertainty, yes, but also immense opportunity. AI has the potential to elevate human work to more creative, strategic, and meaningful heights, if we guide its integration thoughtfully. For organisational leaders, the mandate is clear: embrace the technology and champion the people. Those who manage to do both will not only drive performance in the AI era but will also help foster a future of work that is more innovative, inclusive, and humane.
As a final note, we must acknowledge that the AI landscape is rapidly evolving. Predictions and trends cited in this paper (on jobs, skills, etc.) represent the best available insights as of now, but the actual trajectory could change.
New breakthroughs could accelerate impacts; conversely, societal interventions (education, policy) could mitigate negative effects. Therefore, organisations should treat their strategies as living plans, to be revisited regularly.
Building adaptability into the organisation – a workforce that can learn, a culture that can pivot, and a leadership team that scans the horizon – is the best insurance in a world where the only certainty is change. By doing so, companies in Australia and around the world can ensure that as AI drives the next chapter of work’s evolution, the outcome is one of shared prosperity and progress, with humans firmly at the helm, augmented by the intelligent tools we have created.
Sources
World Economic Forum (2023), Future of Jobs Report 2023 – Key findings on projected job churn and skills outlook.
MIT Task Force on the Work of the Future (2020), final report – Perspective on historical job creation and need for responsive policies
https://news.mit.edu/2020/work-of-future-final-report-1117
OECD (2021), Preparing for the Future of Work in Australia – Automation risk estimates and workforce skill levels.
McKinsey Global Institute and Deloitte Insights – Various studies on AI’s impact by sector and strategies for reskilling (e.g., growth of tech roles, AI adoption surveys).
Australian Parliamentary Library (2025), Potential Impact of AI on the Australian Workforce – Analysis of AI benefits, risks, and recommendations for upskilling.
Reuters (2025), ILO report on AI and gender impact – Statistics on differential impact on women’s jobs and emphasis on task transformation over job loss.
Route Fifty (2025), AI’s impact on public sector jobs – Discussion of roles likely to be automated and new roles emerging in government.
HIMSS (2023), AI in Healthcare Workforce – Outlines how AI can alleviate shortages and shift tasks in healthcare, with caution on job redesign.
Harvard Business Review (2023), “Reskilling in the Age of AI” – Emphasises making reskilling a strategic imperative for leaders.
https://hbr.org/2023/09/reskilling-in-the-age-of-ai
World Economic Forum (2023), Jobs of Tomorrow report – Highlights new job categories like AI Trainers, Explainers, Sustainers needed in coming years.
https://www.weforum.org/stories/2023/09/jobs-ai-will-create/