The future of work: how AI is changing the landscape of work

Machine Learning

The future of work: how AI is changing the landscape of work
Image by editor

If you only think about the past five years, how have your conversations with family and friends changed? Some people may never talk about technology at all, but I can’t help but think that technology is all around us.

The recent release of ChatGPT and now GoogleBard has taken the world by storm with its amazing features. Look at these tools and start thinking about how they can improve your work, company processes, personal life, and more.

Artificial intelligence is automating tasks that were once only performed by humans. The relevance of automating certain tasks to make human life easier will only continue to grow. Some call us lazy, others think it’s the smarter option.

Here are some examples of how AI is changing the landscape of work.

  • automation: Task automation. While this has led to the loss of a large amount of jobs, it has also created new opportunities for others.
  • New job opportunities: AI systems require human workers, such as data scientists and machine learning engineers, to work with them.
  • Changing skill requirements: As AI applications become more integrated, current and new workers need a deeper understanding of how systems work, engineers, analysis phases, and more.
  • Work-life balance: With the rise of AI tools, more people are working remotely, part-time, and freelance as tasks are automated.

Automation is the number one reason the landscape of work is changing today. As more and more tasks are done by artificial intelligence and fewer by real humans, you can understand why companies start laying off employees. Employee costs include salaries, pensions, health insurance, and maternity/parental leave. More and more companies see their employees as a loss, and the automation of artificial intelligence tools is the biggest breakthrough.

Below are some sectors that have already implemented automated tasks.

customer service

Just a few weeks ago, the world was hit by ChatGPT and Google Bard. More and more industries are adopting AI-powered chatbots to provide customer service. With the rise of large-scale language models, these chatbots can be expected to handle customer queries, answer questions, solve problems, and even increase sales.

For example, chatbots are also used in the financial industry for tasks such as new insurance applicant sign-up, know your customer (KYC), and anti-money laundering (AML) policies and processes. The implementation of automated tools for sensitive tasks such as these proves the success of AI and how it continues.

data entry

Previous data entry tasks were manual, very tedious and repetitive. There were several deficiencies in this department as the tasks were highly repetitive and tedious and workers were prone to error.

AI can now automate data entry tasks by extracting data from raw files and documents and populating it into databases.


Well, we all know about self-driving cars. With companies like Tesla, Waymo, and Uber, more and more devices are hitting the market. These cars use AI computer vision to safely drive passengers from A to B, navigate roads, and avoid obstacles.


As mentioned earlier, chatbots are used in the financial industry to automate processes and tasks such as KYC. AI is also being used by these financial institutions to analyze data and make better predictions now and in the future.

The financial industry has vast amounts of data at its disposal. The more historical data you have, the better your analysis results. Unfortunately, this changes the need for humans to work alongside AI systems, rather than AI systems working for humans.

health care

In an industry where many are shocked to see the integration of AI tools, we can expect much more. Professionals in the healthcare industry use AI to diagnose diseases, recommend treatments through data analysis, and even use robotics to perform surgery.

As AI continues to grow, the options for the vast majority of people are to be laid off or work with AI systems. This is why we naturally see more data experts, more courses on learning how to code, Bootcamps, and more.

You will naturally see more of these roles:

data science

Data science combines statistics, data analysis, machine learning, and artificial intelligence. Data scientists are therefore responsible for collating, preparing, cleaning, and manipulating data to identify patterns in the data and perform advanced data analysis.

machine learning engineer

Machine learning (ML) engineers are programmers adept at researching, building, and designing software to automate predictive models. Their role is to build artificial intelligence (AI) systems that consume and learn from large amounts of data to generate and develop algorithms that can make predictions about the future.

AI trainer

AI tools are best when you have all the necessary knowledge. AI trainers need to help teach AI systems how to perform tasks. It is also responsible for collecting, labeling, and inputting data into AI algorithms to ensure that it learns from labeled data and produces accurate output.

It’s hard to see what the future holds, especially when artificial intelligence is mixed in. Unfortunately, AI will start to put more people out of work, and more people will work with AI systems.

This will change people’s desire to acquire new skills to ensure job security. More people will learn programming languages, understand AI, and understand how to use his AI in sales, marketing, etc.

In addition to the pandemic causing major changes in how people work, AI has added to it. More people will continue to work while working from home or traveling around the world, as AI systems can automate many tasks.

With this information, I think it’s important to know how AI will change the landscape of work, regardless of your current job role, and how it can help you meet the skills that are in demand today. increase.

Nisha Aria Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing career advice and tutorials on data science, as well as theory-based knowledge on data science. She also wants to explore different ways artificial intelligence can extend human lifespan. She is an avid learner looking to expand her technical knowledge and writing skills while helping guide others.

Source link

Leave a Reply

Your email address will not be published. Required fields are marked *