Is Your Job At Risk?

Automation and artificial intelligence (AI) are quickly changing how businesses operate across the globe.

These technologies can perform many tasks at superhuman levels of speed and precision, driving 10x - 100x or even higher gains in efficiency and productivity.

Almost every industry is exploring how automation or AI can improve their workflow, from manufacturing and logistics to healthcare and finance.

At the same time, there’s widespread discussion about how this revolution will affect workers and society.

In fact, the World Economic Forum projects that automation and AI could create 97 million new jobs globally by the end of 2025, even as they displace about 85 million existing jobs.

This enormous shift brings both exciting opportunities and significant challenges, making it crucial to understand what these technologies are and how they’re changing the world.

Automation vs. Artificial Intelligence

It’s easy to use terms like automation and AI interchangeably, but they are not the same thing.

Automation generally refers to using machines or software to perform tasks with little or no human input – especially repetitive, routine processes.

In other words, automation is any technology that reduces human labour in a task, which could be as simple as a thermostat, or as complex as an assembly-line robot or even a translation program.

The key is that automated systems follow predefined rules or instructions set by humans. Once set up, they carry out their tasks reliably and tirelessly, but they don’t learn or improvise beyond their programming.

Artificial Intelligence, on the other hand, involves machines that simulate human intelligence and can learn, reason, and make decisions in a more autonomous way.

AI systems are designed not just to follow fixed instructions, but to adapt their behaviour based on data and experience. For example, Google’s DeepMind AlphaFold continuously improves by learning from vast datasets of known protein structures.

It refines its predictions through iterative deep learning, enabling it to solve complex biological problems with increasing accuracy. This AI’s adaptive learning capability outperforms human-designed models in scientific research.

So an automated system will do exactly what it’s told. An AI system will figure out how to achieve a goal or improve at a task by analysing patterns, much like a human would (although more narrowly than actual human intelligence).

Real-World Examples of Automation and AI

One example of advanced automation is the rise of ”dark factories”.

A dark factory (also called lights-out manufacturing) is a fully automated production facility where robots and machines handle all tasks without any on-site human workers.

Because no people are present, these factories don’t even need lighting – literally operating in the dark.

Without coffee breaks or shift changes, such automated lines can work 24/7, greatly increasing output.

Companies invest in these dark factories to increase efficiency and cut costs as machines don’t tire or demand wages, and to improve safety by having robots handle dangerous tasks.

AI-Powered Decision-Making Systems

Beyond physical robots, AI is being used to make complex decisions in ways that were once possible only for experienced human analysts.

Retail giant Walmart has deployed an AI-driven inventory management system that analyses vast amounts of sales data, customer trends, and supply chain variables.

The AI can autonomously decide when to restock products and in what quantities, optimising inventory levels across hundreds of stores in real time.

This kind of system learns from historical sales patterns (even filtering out anomalies like one-off panic-buying events) to predict future demand accurately.

As a result, Walmart’s AI makes faster and more precise stocking decisions than humans alone could, reducing out-of-stock items and waste from over-ordering.

AI-powered decision-making is also transforming other sectors.

In finance, for example, banks and lenders use AI algorithms to automatically assess loan applications and credit risks.

These systems can weigh thousands of data points – an applicant’s credit history, income, market trends – and instantly determine approval or flag risks, tasks that used to take loan officers days.

One AI lending platform even automates the initial loan qualification process based on machine-learning models, making data-driven decisions on who is eligible for credit.

Similarly, in agriculture, companies like John Deere employ AI for “precision farming,” where an AI system analyses sensor and satellite data to decide the best way to plant, irrigate, and harvest crops for maximum yield.

These AI decision systems illustrate how artificial intelligence can handle complex analytical jobs from retail to finance to farming, balancing many factors and learning from results to support or even replace human decision-makers in certain domains.

Impact on Human Jobs

As machines get better at tasks, some human roles are indeed being reduced or replaced. At the same time, entirely new jobs and opportunities are being created.

The net effect on the workforce is complex.

You’ve already read the recent analysis by the World Economic Forum that by the end of 2025, about 85 million jobs may be lost to automation, while about 97 million new jobs may arise due to the new division of labour between humans, machines, and algorithms.

This means that many traditional jobs will shrink, but new categories of work are emerging to take their place.

To understand this shift, it helps to look at which jobs are most affected.

Jobs at Risk

1. Roles that involve repetitive, routine tasks are most vulnerable to automation.
For example, factory assembly-line workers can be replaced by robotic arms, and warehouse pickers by automated guided vehicles or warehouse robots. In offices, clerical positions like data entry and bookkeeping are increasingly handled by software.

2. Service roles such as cashiers, bank tellers, and postal clerks are rapidly declining.
Self-checkout kiosks, ATMs, and online services are increasingly handling those transactions.

3. Customer service is another area seeing automation.
AI chatbots can now address many common inquiries that human call centre agents used to handle.

4. Workers performing predictable physical jobs or straightforward information processing.
They are directly competing with machines that can do the same work faster or cheaper.

New Opportunities

On the flip side, automation and AI are also creating new roles and increasing demand in other areas.

1. There is a surge in need for technical specialists who can develop, manage, and improve these advanced systems.
For example, AI and machine learning specialists, data scientists, robotics engineers, and software developers are among the fastest-growing jobs today.

2. Beyond tech development roles, entirely new job categories are appearing.
The role of data analysts has expanded significantly in the past decade as they now work with AI-driven tools, big data, and machine learning, shifting from basic reporting to predictive analytics, automation, and strategic decision-making. New specialisations are also emerging, such as AI trainers and machine learning analysts.

3. As machines take over repetitive work, human jobs are shifting.
They move toward tasks that require creativity, strategic thinking, interpersonal skills, and other strengths that AI cannot easily replicate.

Jobs in healthcare (which require empathy and complex human judgment) and education, for example, are expected to grow even as automation spreads.

4. There are also maintenance and oversight roles created by automation.
For example, when a factory automates, it often needs more skilled technicians and engineers to maintain the robots and software.

While robots displaced some unskilled assembly-line workers, they also created new jobs for machinists, advanced welders, and other technicians who keep the automated systems running.

And so the workforce is shifting. Fewer people will be doing simple routine work, and more people will be in jobs building technology, interpreting data, or in roles that require uniquely human qualities.

Here’s a nice chart by McKinsey on the growth and decline of different professions due to automation and AI:

Adapting to the Change

With these shifts in job markets, a big question is how workers and economies can adapt.

History has shown that technological revolutions (from the Industrial Revolution to the computer age) eventually create new prosperity, but they can cause painful transitions in the process.

This wave is no different.

Many workers will need to reskill or upskill – meaning learn new skills or even switch careers – to stay employed in the age of automation.

In fact, studies suggest that a large share of today’s workforce will need such transitions.

According to a McKinsey analysis, by 2030 up to 375 million workers globally may need to change occupations and learn new skills due to automation, which is about 14% of all workers.

Both businesses and governments have a role to play in easing this transition.

The World Economic Forum notes that the most competitive businesses will be those that choose to upskill their staff rather than simply lay them off.

Educational institutions and online learning platforms are also expanding courses in coding, data analysis, robotics, and other in-demand skills to help workers pivot.

For older or less-skilled workers, support will be needed to ensure they aren’t left behind. This might include government-funded training, apprenticeship programs in tech, or incentives for industries to hire and train displaced workers.

Economies as a whole may also adapt by evolving new policies.

Some economists have suggested ideas like a universal basic income (UBI) – a baseline income provided to all citizens – as a safety net in a future where stable jobs might be fewer.

The rationale is that UBI could cushion workers during retraining periods or between gigs, and give people more flexibility to learn new skills or pursue entrepreneurial ventures.

While UBI is still experimental, it’s gaining attention as one option to handle widespread job displacement. However, implementing UBI on a large scale could be costly, raising concerns about its long-term viability and the potential need for increased taxation. ​

Governments might also consider tax incentives for companies that create human jobs, or taxes on heavy use of robots(sometimes dubbed “robot taxes”) to fund social programs.

Whether through education, economic policy, or social support, the goal is to ensure that as automation and AI increase productivity, the benefits are shared and workers are empowered to transition into new, fulfilling roles rather than being left without work.

Future Developments in Automation and AI

Looking ahead, automation and AI are on track to become even more powerful and pervasive.

One major area of development is generative AI, which are AI systems that can create original content and designs with just a line of prompt.

Recent breakthroughs like large language models (for example, OpenAI’s ChatGPT) have shown that AI can perform tasks once thought uniquely human, such as writing essays, coding software, or creating images.

These generative AI advances are already changing how we produce content and make decisions, potentially transforming entire professions in media, marketing, customer service, and beyond.

In addition, Artificial General Intelligence (AGI) — AI that matches or surpasses human cognitive abilities across diverse tasks — may be closer than previously anticipated.

Dario Amodei, co-founder of AI company Anthropic (competitor to OpenAI), predicts that AGI could emerge as soon as in the next 2 – 4 years.

Similarly, Elon Musk predicts that AI will surpass human intelligence within a year or two, reaching a level smarter than all humans combined by 2029 or 2030.

Corporate Initiatives Towards AGI

Major corporations are actively pursuing AGI development.

Alibaba's CEO, Eddie Wu, has identified AGI as the company's top priority, aiming to create AI that can reason and perform complex cognitive tasks like a human.

This initiative underscores the potential for AGI to significantly impact industries and economies.

AGI Progress

Recent technological advancements also point toward the feasibility of AGI.

Anthropic's Claude 3.7, the first hybrid AI model, combines instinctive output with in-depth reasoning to solve complex problems. This demonstrates AI's evolving ability to integrate various cognitive functions, which is a critical step toward achieving AGI.

The rapid progress in AI capabilities, coupled with significant investments from leading technology companies, indicates that AGI could become a reality in the near future, necessitating proactive discussions on ethical considerations, societal impacts, and regulatory frameworks.

Physical Automation

The development of autonomous vehicles and drones are already changing how we live. These are essentially robots on wheels and wings.

Companies like Baidu and AutoX have launched autonomous taxi services in cities such as Beijing, Shenzhen, and Wuhan. These robotaxis operate without human drivers, offering residents the future of urban mobility – right now.

Beyond passenger transport, autonomous vehicles are also utilized for delivery services. In Shenzhen and Wuhan, self-driving delivery cars distribute goods efficiently in urban environments.

To support the integration of autonomous vehicles, Beijing has implemented regulations aimed at increasing the presence of driverless cars on its streets. These policies are designed to encourage technological innovation and facilitate the safe deployment of autonomous driving technology.

Check out the self-driving taxis that are currently in operation for a couple of years now:

Check out goods delivery by drone:

Here’s a drone that can deliver construction equipment weighing hundreds of pounds:

You can already see how these developments are already transforming logistics and transportation industries.

Likewise, AI is expected to continue making inroads in fields like medicine (a future where AI assists doctors in diagnosing illnesses or performing surgery), education (personalized AI tutors for students), and smart cities (AI managing traffic flow, energy usage, etc.).

We will also likely see stronger collaboration between humans and machines. These “cobots” (collaborative robots) work directly with human workers on production lines or in laboratories, combining the strength and precision of machines with the judgment and adaptability of people.

AI’s ultimate impact on humanity is very clear. We’re already experiencing the beginnings of a major shift in our experiences as humans, but our jobs will definitely be impacted in some way, for better or for worse.

My question to you is simple:

Are you prepared for the transition?

Cheers!

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