The Changing Nature of Work in the Age of AI
As AI systems—especially generative AI, advanced machine-learning models and automation tools—become more capable and more widely adopted, the nature of work is shifting in subtle and not-so-subtle ways. Work is no longer simply “do the tasks” but increasingly involves managing, collaborating with, or augmenting intelligent systems.
Research by the Organisation for Economic Co‑operation and Development (OECD) finds that for many workers today the most visible effect of AI is not job loss, but changes in the tasks they perform and the working environment. OECD
Meanwhile, firms integrating AI report productivity gains, new workflow patterns, and evolving roles: AI may handle routine and repetitive elements, while human workers focus on higher-level, less automatable activities.
This shift is reflected in recent analyses. For example the PricewaterhouseCoopers (PwC) 2025 Global AI Jobs Barometer found that nearly all industries are increasing their use of AI and that AI-exposed jobs are seeing faster skills change. PwC+1
In short: work is being redefined, not simply eliminated.
Which Jobs and Tasks Are Most Affected?
One of the core questions is: which jobs are most exposed to AI, and which are relatively safe (for now)? Studies show that exposure depends heavily on the nature of the tasks within a job: repetitive, predictable, rule-based tasks are more vulnerable; while non-routine, human-centric, context-rich tasks are more resilient.
The IMF has estimated that about 40 % of jobs globally could be affected by AI, and in advanced economies this could rise to around 60 % because of the prevalence of cognitive-task oriented roles. Personnel Today+1
The same OECD chapter notes that job quality and tasks may shift: jobs may not vanish but may become “squeezed” into simpler tasks, changing the nature of the work. OECD
Research in the UK has introduced a “Generative AI Susceptibility Index” showing that nearly all UK jobs have some exposure to generative AI, although only a minority are “heavily affected” so far. arXiv
To illustrate:
- Jobs such as data entry clerks, telemarketers, routine accounting or administrative assistants are in the higher-risk category because much of their work is rule-based and repetitive.
- On the other hand, roles that demand emotional intelligence, complex decision-making, novel problem-solving, craftsmanship, leadership, caregiving and human interaction are relatively more insulated—though not completely immune.
It’s important, though, not to think purely of “jobs lost” vs “jobs safe” but rather “tasks within jobs shifting”, and “jobs transforming”.
Jobs Being Transformed, Not Just Displaced
A key insight from recent research is that AI doesn’t necessarily mean pure job elimination; much of the impact involves transformation of roles and tasks. The PwC study emphasises that AI is making workers “more valuable, not less” — even in jobs that are exposed to automation. PwC+1
For example:
- A customer-service representative may find that routine inquiries are handled by chatbots or AI assistants, while their role shifts to handling more complex, escalated cases, relationship-building and judgement calls.
- An accountant may see software automate certain bookkeeping and reconciliation tasks, while the human professional shifts focus to interpretation, advising, strategy, exception-handling.
- A manufacturing technician may see sensors and AI monitor equipment, but their role may evolve to supervising, maintaining, analysing data, and ensuring human-machine collaboration.
What this means is that human skills of oversight, creativity, critical thinking, communication, adaptation, emotional intelligence and ethical judgment become increasingly important.
The Opportunity Side: New Jobs, New Skills
While much of the public discourse focuses on risk and displacement, there is considerable opportunity in the AI transformation of work. Many new jobs are emerging, new fields are being created, and the skill-premium for certain capabilities is rising.
The PwC 2025 Barometer shows that workers with AI-related skills – for example prompt engineering, working with AI tools, data analytics – command a wage premium. According to one dataset, workers with such skills had on average 56 % higher wages than similar peers without those skills. PwC+1
Additionally, industries that adopt AI more effectively tend to have three times higher growth in revenue per employee than less AI-enabled sectors. PwC
Emerging job categories include: AI trainers and teachers, data scientists and analysts, human-machine teaming managers, AI ethics and policy specialists, automation engineers, intelligent process designers. Smart Forum+1
Thus for workers, the message is: there is strong upside in preparing for the evolving labour market, emphasising adaptability and continuous learning.
The Risks and Challenges: Disruption, Quality, Inclusiveness
Despite the opportunities, the transition is uneven and carries risks—especially for certain groups, job categories and geographies.
Task and job displacement. Because many roles comprise a mix of automatable and less automatable tasks, some workers may find themselves doing more of the “left-over” tasks that are less desirable: repetitive, low-autonomy or lower-paid. The OECD report warns of downward pressure on wages and job quality in such cases. OECD
Job quality and worker well-being. Changes are not always beneficial: some workers report more stress, tighter monitoring, algorithmic management and faster work-paces when AI is introduced. The same OECD study highlighted that while managerial or highly skilled workers tended to report improvements, others found less positive outcomes. OECD
Skill-divides and inequality. Those who can access training, upskilling and transition support are better positioned to benefit; those who remain in exposed roles with limited options may face greater risk. Also, developing economies and lower-income workers may face longer transitions. The IMF and other analysts warn of increasing inequality if policy responses are weak. Personnel Today+1
Geographic and sectoral variation. Adoption of AI is faster in capital-intensive, technology-ready sectors; slower in labor-intensive, less-digitised industries. Thus job-market effects may vary widely across regions, sectors and countries.
Uncertainty and pace. Because AI adoption is accelerating, the pace of change may outstrip the ability of institutions (education, re-training, regulation) to keep up. Goldman Sachs
Navigating the Transition: What Workers Can Do
For individuals seeking to prepare for the future of work in an AI-enabled world, several strategies stand out.
Focus on skills that complement AI.
Skills such as critical thinking, creativity, problem-solving, adaptability, relational and interpersonal skills, ethics, judgement, leadership and collaboration with AI systems are increasingly valued. Academic work suggests that the “complementary effect” of AI (i.e., AI augmenting human skills) may be up to 50 % larger than the “substitution effect” (AI replacing humans) in many contexts. arXiv
Continuous learning and flexibility.
Because roles and tasks will shift, being comfortable learning new tools, learning how to work with AI, and being willing to pivot will help. Workers may need to retrain, acquire digital literacy, specialise in new tools and stay agile.
Adopt a human-plus-machine mindset.
Rather than seeing AI as competition, consider how to partner with AI—how to use AI to do what it does well (data analysis, routine tasks) and free yourself for what humans do best (values, context, meaning, connection).
Build domain expertise + tech literacy.
A combination of domain knowledge (industry, function, context) and tech familiarity (AI tools, data, digital workflows) is powerful. Workers who can bridge human domain insight and machine capabilities will be well-placed.
Ensure your role remains relevant by emphasising value-added tasks.
If your role is composed largely of repetitive or low-autonomy tasks, consider repositioning toward higher-autonomy activities: decision-making, oversight, creative work, mentoring, client-facing roles.
Advocate for meaningful work and fairness.
Given concerns about job-quality and inclusion, workers should engage in conversations around how AI is implemented in their organisation, how decisions are made (e.g., about monitoring, decision-algorithms), and how fairness, transparency and human oversight are maintained.
What Employers and Policy-Makers Should Consider
The transformation of work through AI is a collective challenge. Organisations and policy-makers play a major role in shaping whether the transition is inclusive, productive and sustainable.
For organisations:
- Design AI adoption to augment human roles, not simply replace them.
- Invest in retraining, upskilling and transition support for workers whose roles are most impacted.
- Monitor not only productivity gains but also worker well-being, job quality, fairness of AI-driven decisions (e.g., algorithmic management).
- Foster cultures of human-machine collaboration and redesign workflows to maximise human strengths.
For policy-makers:
- Develop frameworks for lifelong learning, resumeskilling and social safety nets to support transitional periods.
- Encourage inclusive access to digital and AI literacy, especially in less-digitised regions and among vulnerable worker groups.
- Monitor labour-market dynamics and ensure regulatory frameworks that address algorithmic fairness, worker rights, job-quality and inclusiveness.
- Support sectors and workers in transition (e.g., regions heavily reliant on roles at high exposure) and promote pathways into growing roles.
Looking Ahead: What the Future Might Hold
It is impossible to predict exactly how the job-market will look a decade from now, but several plausible trends emerge.
Task-based shifts, not always job loss.
More often than wholesale elimination of jobs, we may see jobs morphing: parts of roles automated, parts shifting to higher-value human activity. The transformation may play out over years rather than overnight.
For example, the Goldman Sachs research estimates that full adoption of generative AI could raise labour productivity in developed markets by around 15 %. They estimate that job losses in the United States might amount to 2.5 % of employment if efficiency gains are broadly distributed. Goldman Sachs
New roles, new industries.
Just as previous industrial revolutions created roles that nobody could foresee, AI will generate new occupations, new industries and new forms of work. The PwC Barometer suggests that AI-exposed roles often see wage and opportunity growth rather than decline. PwC
Geographic and sectoral divergence.
Some regions, industries and firms will benefit more quickly from the AI transition; others may face greater disruption. Developing economies, where labour costs are lower and digitisation is slower, may face a different trajectory compared to highly-digitised developed economies.
Hybrid human-machine teams.
Future organisations may increasingly deploy human-AI teams: humans focus on strategy, ethics, relationships and judgement; AI handles data-intensive, high-volume, predictable tasks. Workers who can position themselves in such teams will gain advantage.
Faster pace of change.
One distinctive element of the AI wave compared to past technologies is the pace. Because generative AI and large-scale models are evolving quickly, the rate of task change may accelerate. This faster pace makes adaptability and continuous learning more crucial than ever.
Focus on value and meaning.
As routine tasks become more automated, human work may increasingly emphasise what machines cannot easily replicate: empathy, connection, creativity, mentoring, leadership, ethical judgement, meaning-making. For many workers, this may shift the nature of fulfilment and career satisfaction