Brian Chan
Brian Chan
Last updated 14 November 2025
Before the cashier machine, shopkeepers did math in their heads to give customers change. Before the typewriter, people wrote letters by hand to communicate across distances. History is a long story of technological innovation transforming not just how we work, but the very jobs we do. The current conversation around artificial intelligence, often painted in dramatic strokes of utopia or dystopia, is simply the next chapter in this familiar narrative.
This pattern of disruption and creation is not new. The mechanical loom automated weaving, displacing artisans but creating jobs for machine operators. Gutenberg's printing press made scribes obsolete but gave rise to printers, publishers, and typesetters. More recently, the automobile replaced the horse-drawn carriage industry but built a new economy around manufacturing, mechanics, and road construction. From ATMs and online banking transforming the role of bank tellers to personal computers and e-commerce reshaping office work and retail, technology has consistently shifted jobs rather than eliminating them outright. AI is the next powerful force in this progression, and understanding this history is key to navigating the changes it brings.
The conversation around artificial intelligence is dominated by a central question: is it a job destroyer or a job creator? The reality is that it's both. AI is a force of transformation, automating certain tasks, augmenting human capabilities, and giving rise to entirely new professions.
There is no denying that AI and automation are leading to job displacement in certain sectors. Roles that involve repetitive, routine, and predictable tasks are the most vulnerable. Research suggests that occupations like computer programming, accounting, administrative assistance, and customer service are at high risk of being impacted as generative AI becomes more adept at handling knowledge-based tasks. Some roles in data entry and on the assembly line have already seen significant change. During the COVID-19 pandemic, for instance, many companies turned to automation to handle tasks previously done by human workers.
However, job displacement does not necessarily mean permanent unemployment. Historical precedents show that while some jobs become obsolete, new ones emerge. For example, the internet is estimated to have created more jobs than it destroyed, with some studies suggesting a ratio of 2.4 created for every one eliminated.
The same technological forces that displace jobs also create new opportunities. As AI technologies become more integrated into our industries, a host of new job categories are emerging, often centered around the development, implementation, and management of these complex systems. There is already a surging demand for professionals who can build, train, and fine-tune the algorithms that power AI, such as machine learning engineers and data scientists. Similarly, the rise of AI ethicists and governance specialists reflects the growing need to navigate the complex ethical and societal implications of AI and ensure fairness and transparency in its deployment.
In addition, specialists who can design effective ways for humans to communicate with and guide AI systems—AI interaction designers and prompt engineers—are becoming increasingly valuable. There is also a need for AI trainers and content curators, people who teach AI models and ensure the quality of their outputs. Furthermore, AI is expected to augment jobs in sectors like healthcare and education, where the human touch remains indispensable. By handling administrative burdens, AI allows professionals to focus on the more complex, creative, and empathetic aspects of their work.
For businesses, the drive to adopt AI is often framed as a race for competitive advantage and, ultimately, higher profits. The potential is enormous, with Bloomberg Intelligence analysts projecting the generative AI market could become a $1.3 trillion industry by 2032. Morgan Stanley forecasts that revenue from generative AI could jump from $45 billion in 2024 to about $1.1 trillion in 2028, with the sector starting to generate profits as early as 2025.
However, the path from investment to profit is not a straight line, and the reality on the ground appears far more complex. A widely cited 2025 report from MIT, "The GenAI Divide: State of AI in Business," presents a sobering counter-narrative. The study reveals that despite tens of billions in enterprise investment, a staggering 95% of integrated AI pilots fail to produce any measurable impact on a company's profit and loss. This highlights a significant "GenAI Divide" where a mere 5% of companies are extracting millions in value, while the vast majority are stuck in stalled pilot programs. The reasons for this high failure rate are multifaceted, including a tendency to force AI into existing processes, a lack of alignment with business workflows, and a failure to bridge the "learning gap" where AI systems don't improve over time. Many gains are seen at the individual productivity level with tools like ChatGPT but fail to translate into broader, enterprise-wide financial returns.
On the other hand, a different perspective suggests the picture is not so bleak. A study from the University of Pennsylvania's Wharton School found that 74% of businesses that actively measure the return on investment (ROI) from their generative AI initiatives are already seeing a positive return. This more optimistic view stems partly from a broader definition of ROI that includes not just direct profit but also gains in productivity and employee retention. Furthermore, clear winners have emerged in the AI economy. So-called "shovel sellers" like Nvidia, which produces the essential chips for AI, have seen profits soar. Likewise, major software and platform companies such as Microsoft, Adobe, and Google are successfully monetizing AI through their integrated services.
This conflicting data suggests that profit is not an automatic outcome of AI adoption. It underscores a critical distinction between simply investing in the technology and strategically integrating it into core business objectives. The firms that succeed are those that move beyond the hype, redesign workflows, and focus on specific, high-value applications, particularly in back-office automation where ROI is often highest.
Perhaps the most profound impact of AI will be in augmenting human capabilities, creating a new partnership between people and machines. AI can act as a collaborative tool, handling tedious work and freeing up humans to focus on strategy, creativity, and complex problem-solving. A Stanford University report found that knowledge workers using AI assistants were more productive and produced higher-quality work.
To navigate this transition successfully, a commitment to upskilling and adaptability is essential. Both individuals and organizations must invest in developing the skills for an AI-augmented workplace. This includes not only technical AI skills but also uniquely human capabilities that machines cannot replicate: critical thinking, emotional intelligence, and creativity.
The future of work is not a predetermined outcome to be passively awaited; it is a design exercise. Through proactive policy, responsible governance, and a commitment to lifelong learning, we can shape the deployment of AI to foster shared prosperity and empower human potential. The conversation must shift from a simplistic narrative of job loss versus job creation to a more nuanced focus on managing the transition, equipping our workforce for the future, and designing a world where humans and AI collaborate to solve our most pressing challenges.
The message is clear: AI may not take your job, but a person who knows how to use AI effectively might. Rather than viewing AI as a threat, see it as a powerful tool that can augment your abilities and make you more valuable. Here is how you can prepare to not just survive, but thrive in the age of AI:
By embracing AI as a collaborative partner and focusing on continuous personal and professional development, you can position yourself not just to adapt to the future of work, but to help shape it.