Work should evolve to the benefit of society under AI changes

Holocaust survivor Lola Mandelkier Sztrum, 101, in a nursing home in Buenos Aires. In the world of care giving, while AI might excel at processing medical data or monitoring vital signs, the human eleShow more

The rapid development of AI has created unprecedented fears and anxieties about the future of work and employment. While I am not in the business of predicting the future, as an academic studying these transitions, I believe historical patterns can offer valuable insights into our current situation.

Many people are understandably concerned about the changing job market, but historically, technological advancements have consistently led to increased productivity, greater wealth creation and the emergence of new jobs and industries.

Consider the city of Blackpool in the UK, which rose to prominence as a significant tourist destination in the early 20th century. This transformation was a direct spill-over effect from the Industrial Revolution’s wealth creation in Manchester and Liverpool, where newly affluent workers sought leisure activities, spawning an entirely new industry in Blackpool.

This example offers a compelling parallel to the current situation. Just as the Industrial Revolution created unforeseen opportunities, it could be argued that AI’s impact on the economy won’t merely eliminate jobs but will create new ones through generated wealth. This leads to two key considerations.

First, where will these new jobs emerge, both in terms of industries and geographical locations? And second, will humans have the ability to adapt and retrain to remain relevant in the job market?

The varying susceptibility of different jobs to automation presents a fascinating paradox in the AI-driven future. Some roles, particularly those involving routine, data-driven tasks, are prime candidates for automation.

Customer support, for instance, has already seen significant AI integration through chatbots that can handle increasingly complex queries. Similarly, data entry, basic accounting functions and routine administrative tasks are being rapidly transformed by automation.

However, other professions present far more formidable challenges to automation, particularly those requiring complex human interaction and emotional intelligence.

Care for the aged stands as a compelling example of this complexity. With ageing populations in developed nations, the demand for caregivers continues to rise, yet these roles resist simple automation due to their multifaceted nature.

A caregiver’s job encompasses medical knowledge (medication management and health monitoring), emotional intelligence (providing companionship and emotional support), and physical capabilities (assistance with daily activities and personal care). While AI might assist with certain aspects, such as medication scheduling or health monitoring, it cannot replicate the human touch that makes caregiving so essential.

This automation resistance extends to many other professions that require complex human interaction – from mental health counsellors to teachers, social workers to physical therapists. These roles share common elements that current AI technology struggles to replicate: contextual decision-making, emotional intelligence and the ability to respond to unpredictable human needs.

The demographic dimensions of this challenge add another layer of complexity. Demographers project that Western Europe, Japan and North America will face increasingly ageing populations, creating a surge in demand for health care and care services for the aged. Meanwhile, Sub-Saharan Africa remains the world’s demographic outlier, with a growing young population.

This global demographic disparity creates intriguing possibilities for workforce migration and specialisation. Could there be a future where younger workers from Africa help fill the caregiving gap in ageing societies? How might this influence global economic patterns and immigration policies?

While considering these demographic shifts, new opportunities emerge in unexpected places. In developed nations with ageing populations, new industries could emerge that are centred around active ageing, preventive health care and social engagement for seniors.

These sectors could combine human care with technological assistance, creating new job categories that blend traditional caregiving skills with technical expertise. The challenge, therefore, isn’t simply about identifying which jobs will or won’t be automated. Instead, it is about understanding how human skills and AI capabilities can complement each other to address pressing societal needs.

While AI might excel at processing medical data or monitoring vital signs, the human elements of care work – empathy, cultural understanding and physical assistance – will remain crucial. Looking at historical precedents, workforce adaptation presents a complex and sobering challenge.

While technological advances have consistently generated new employment opportunities, transition periods have often left entire communities behind – from skilled craftsmen during the Industrial Revolution to manufacturing workers in the automation age. Today’s AI revolution moves at an unprecedented pace, making adaptation even more crucial.

Consider the evolving stories of bank tellers and travel agents. When ATMs and online booking platforms emerged, many predicted these professions would vanish. Instead, successful workers transformed their roles by focusing on higher-value services, demonstrating how adaptation can work.

However, not everyone managed this transition successfully, highlighting the need for comprehensive support systems. The key to navigating future transitions lies in a three-pronged approach: individual initiative, institutional support and policy frameworks.

Today’s landscape offers unique opportunities through online learning platforms, flexible degree programmes and corporate retraining initiatives. However, these tools must be paired with robust social support systems and policies that protect workers during transition periods.

Success will require not just personal resilience and motivation, but a fundamental shift in how careers are viewed – moving from a linear progression to a model of continuous learning and adaptation. Without such comprehensive measures, there is a risk of repeating historical patterns where technological progress creates winners and losers rather than advancing society as a whole.

The question isn’t just whether individuals can adapt, but whether society can build the necessary frameworks to support this unprecedented scale of workforce transformation. Our response to this challenge will largely determine whether AI’s impact on employment becomes a story of displacement or one of collective progress.