The Impact of AI and LLMs on the Future of Jobs

Artificial intelligence (AI) has grown tremendously in recent years, which has created excitement and raised concerns about the future of employment. Large language models (LLMs) are the latest example of that. These powerful subsets of AI are trained on massive amounts of text data to understand and generate human-like language.

According to a report by LinkedIn, 55% of its global members may experience some degree of change in their jobs due to the rise of AI.

Knowing how AI and LLMs will disrupt the job market is critical for businesses and employees to adapt to the change and remain competitive in the rapidly growing technological environment.

This article explores the impact of AI on jobs and how automation in the workforce will disrupt employment.

Large Language Models: Catalysts for Job Market Disruption

According to Goldman Sachs, generative AI, and LLMs can potentially disrupt 300 million jobs shortly. They have also predicted that 50% of the workforce is at risk of losing jobs due to the integration of AI in business workflows.

LLMs are increasingly automating tasks previously considered the sole domain of human workers.  For instance, LLMs, trained on vast repositories of prior interactions, can now answer product inquiries, generating accurate and informative responses.

This reduces the workload of human staff and allows for faster, 24/7 customer service. Moreover, LLMs constantly evolve, going well past customer services and being utilized in various applications, such as content development, translation, legal research, software development, etc.

Large Language Models and Generative AI: Automation

LLMs and generative AI are becoming increasingly prevalent, which could lead to partial automation and the potential displacement of some workers while creating opportunities for others.

1. Reshaping Routine Tasks

AI and LLMs excel at handling repetitive tasks with defined rules, such as data entry, appointment scheduling, and generating basic reports.

This automation allows human workers to focus on more complex tasks but raises concerns about job displacement. As AI and LLMs become more capable of automating routine tasks, the demand for human input decreases, consequently triggering job displacement. However, jobs that require a high degree of human oversight and input will be the least affected.

2. Industries at Automation Risk

Sectors with a high volume of routine tasks, like manufacturing and administration, are most susceptible to AI and LLM automation. Due to their ability to streamline operations like data entry and production line scheduling, LLMs are a risk to jobs in these sectors.


According to the Goldman Sachs report, AI automation will transform the workforce with efficiency and productivity while also putting millions of routine and manual jobs at high risk.

3. Potential Loss of Low-Skilled Jobs

The impact of AI on the low-skilled workforce is expected to grow in the future. ​The skill-biased nature of AI-driven automation has made it more difficult for those with less technical knowledge to grow in their employment. This is because automation widens the gap between high and low-skilled workers.

Low-skilled workers can only retain their jobs through high-quality education, training, and reskilling programs. They may also face difficulty transitioning to newer, higher-paying, and high-skilled jobs that use AI technologies.

This becomes more evident as the latest McKinsey report predicts that low-wage workers are 14 times more likely to need a job switch. Without upskilling or transitioning to new, AI-compatible roles, they risk being left behind in a rapidly evolving job market.

4. Role of AI and LLMs in Streamlining Processes

A significant shift occurs within the business landscape due to the growing adoption of AI and LLMs. A recent report from Workato reveals a compelling statistic: operations teams automated a remarkable 28% of their processes in 2023.

AI and LLMs are game-changers, reducing operating costs, streamlining tasks through automation, and improving service quality.

The Future of Work in the Age of AI

While AI is inevitable, with enough resources and sufficient training, employees can use AI and LLMs to increase productivity in their daily routine tasks.

For instance, the National Bureau of Economic Research (NBER) states that customer support agents using a generative AI (GPT) tool increased their productivity by around 14%. This shows the potential of collaboration between humans and machines.

While AI undoubtedly changes the job market, its integration should be seen as an opportunity rather than a threat.  The true potential lies in the collaboration between human intuition, creativity, and empathy combined with AI’s analytical prowess.

Reskilling For LLMs and Generative AI

While GPT could generate texts and images, its successors, like GPT-4o, seamlessly process and generate content across text, audio, images, and video formats.

This shows that the new multi-modal LLMs and AI technologies are evolving rapidly. Reskilling becomes essential for both modern organizations and workers to survive due to the impact of artificial intelligence on the future of jobs. Some of the important skills include:

Prompts Engineering: LLMs rely on prompts to guide their outputs. Learning how to create clear and concise prompts will be a key factor in achieving their true potential.
Data fluency: The ability to work with and understand data is essential. This covers collecting, analyzing, and interpreting data, influencing your interaction with LLMs.
AI literacy: Foundational knowledge about AI, including its capabilities and limitations, will be essential for effective collaboration and communication with these powerful tools.
Critical thinking and evaluation: While LLMs can be impressive, assessing their outputs is important. Assessing, updating, and analyzing the LLM’s work is essential.

Ethical Implications of AI in the Workplace

The presence of AI in the workplace has its pros and cons, which must all be carefully considered. The former, of course, increases productivity and reduces costs. However, if adopted detrimentally can also have adverse effects.

Here are some ethical considerations that need to be part of the larger narrative:

Algorithmic Bias and Fairness: AI algorithms have the potential to reinforce biases found in the data they are trained on, which could result in unfair recruitment decisions.

Employee Privacy: AI relies on vast amounts of employee data, raising concerns about potentially misusing this information, which could lead to unemployment.

Inequality: The increased use of AI in workflows presents challenges like inequality or inaccessibility. Initiatives like upskilling and reskilling programs can help reduce AI’s negative impacts on employees across organizations.

Workplace paradigms are shifting as a result of AI and LLMs integration. This will greatly impact the future of work and careers.

For further resources and insights on AI and data science, explore

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