Intelligent machines have caused a massive revolution across industries. Yet we probably have not witnessed the best of what AI inventions can do. In general, most technological inventions came up to make life/work easier for humans. In the workplace, they have been used to perform some repetitive tasks initially performed by humans yet much faster and more productively to enhance overall operational performance and allow employees to focus on more valuable work. Technology has enhanced decision-making, problem-solving, and critical thinking while improving our knowledge and learning abilities.
Artificial Intelligence (AI) is impacting industries and humans in very many ways. The main goal of AI is to simulate human intelligence into machines to create expert machines capable of thinking, working, and making decisions like humans. AI has been the driver of technologies like IoT, big data, robotics, and many more that have emerged in the past decade. On the flip side, many believe that the emergence and implementation of AI and its sub-branches in organizations will replace certain roles and lead to job losses. While this might be true to some extent, 34% of human resource leaders are looking at staff training and reskilling as a way around it. In other words, it is time for employees to consider undertaking an artificial intelligence, big data, or machine learning course and others to align their skills to job market demands.
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What is machine learning?
Machine learning is a subset of AI in which systems learn from historical data, discover patterns, and make future predictions without explicitly being programmed to do so. Machine learning uses statistical models to analyze historical data, discover patterns, and draw inferences. Advanced machine learning is useful for applications such as natural language processing, facial recognition, and computer vision which are today being adopted widely to enhance performance and productivity in the workplace.
There are three types of machine learning:
- Supervised learning: A machine learning model trains from labeled data to predict outcomes accurately. Supervised machine learning is useful in classification and regression.
- Unsupervised learning. A machine learning model trains from unlabeled data to identify patterns on its own. It is useful in exploratory data analysis.
- Reinforced learning. A machine learning model that trains on trial and error based on a system that either rewards or penalizes its decisions in a particular situation.
Will machine learning replace jobs in the future?
Based on a projection by Mckinsey, intelligent agents and robots may replace up to 30% of the world’s jobs by 2030. This represents around 400 million jobs. Machines will be trained to work more smartly and productively than humans, resulting in some roles being redundant. How true are these claims?
That said, here are 8 jobs/skills that machine learning may replace in the future.
- Switchboard operators and receptionists
Companies can no longer keep up with the demands that come with handling customer queries 24/7. Owing to this, it is projected that virtual personal assistants and chatbots will replace up to 69% of managers’ workloads by 2024. These chatbots are designed with the capabilities of routing customer calls to the right department, chatting with customers, answering general queries, and ensuring 24/7 availability of customer support services. Other organizations have installed touchscreen systems that customers engage with instead of receptionists. These systems can also track and produce customer calls and engagement reports to help in decision-making.
- Loan officers and credit analysts
Machine learning techniques are today being implemented to predict credit ratings. Credit ratings are useful information for risk assessment. Machine learning techniques like support vector machines, random forests, and artificial neural networks alongside big data analytics have been employed widely to forecast ratings for businesses and individuals. These techniques have proved more accurate and reliable for assessing credit and default ratings compared to their human counterparts. As such, loan officers and credit analysts may have to reskill to remain relevant in the industry.
- Market research analysts and specialists
AI and machine learning focus on statistics, analysis, and reporting which the marketing industry relies on to make strategic decisions. For this reason, this industry is certainly one that AI disruption will impact significantly. AI-driven marketing has changed how market research and analysis is conducted by introducing machine learning algorithms that can be applied to conduct market research cheaper, faster, less labor-intense yet with quick results and better ROI compared to traditional marketing analysis. GrowthBot, for instance, is a marketing chatbot that is used to conduct online market research with a simple command.
Because AI-driven marketing does not require advanced marketing knowledge, market research assistant, research analyst, and other market research specialist skills will be impacted significantly by machine learning in the future.
- Parking lot attendants
Machine learning techniques like computer vision have been employed successfully to manage parking lots. This is done by installing sensors or cameras that detect and recognize spaces occupied by vehicles. Availability of such information in real-time is crucial not only for effective parking lot management but also for users to plan themselves and save the time it usually takes to find an empty parking space.
However, with this kind of technology in place, the traditional role of parking lot attendance has slowly become obsolete.
- Bus and taxi drivers
With the invention of autonomous vehicles and the safety that they promise to deliver to our roads, it seems that the bus and taxi driver roles may soon be out of the market. It is projected that with full implementation and availability of autonomous vehicles to the public, at least two million jobs will be lost in the United States alone.
- Editing and proofreading
Thanks to AI and Machine learning, more people rely on proofreading software like Grammarly to check their work than they do humans. Grammarly automatically detects grammatical and sentence construction errors and can also gauge the correctness, tone, and conciseness of a written piece using natural language processing techniques. Taking it a step further, the most recent GPT-3 (Third generation pre-trained transformer) AI writing software uses natural language generation to transform data into written English sentences. While it is believed that writing software cannot replace human originality, empathy, and creativity, they sure do have an impact on editing jobs.
While some say that telemarketing is soon becoming extinct, others strongly believe in robots replacing telemarketers. The advantage? Robots are programmed to remember every tiny detail about a product/service on sale just so that a potential customer does not miss any of it. Still, others believe, and rightly so, that telemarketing is an evolving role. Telemarketing today integrates CRM, social media, content marketing, PPC campaigns, email marketing, and automation. Thus to remain relevant, telemarketers need to embrace these tools, channels, and resources while at the same time building strong customer relationships. Telemarketing’s focus has shifted from quantity to quality.
- Cyber Defence analysts
Cybersecurity attacks are growing in sophistication and number with each passing day. To protect the information, the industry demands more effective security threat mitigation strategies. Eyes are on security automation that is considered more efficient, cost-effective, and intelligent compared to their human counterparts. Security automation has seen a 12% adoption within one year and cyber defense analysts have a reason to worry about automation taking up their roles. Alternatively, they could opt for upgrading their skills as there is always a human aspect of their role that automation cannot replace.
Several studies on the impact of advanced technology have found that technology adoption may not wipe out full occupations but replace or enhance certain skills. Automation has been found to drive productivity, efficiency, and performance in the workplace, leaving employees to focus on innovation and valuable tasks.