Five Ways AI Can Make You Rich

It is evident from history that as technology shifts, the odds of being prosperous and wealthy increase. As Artificial Intelligence (AI)-based innovations have revolutionized technology, now is the time to use your creativity, hard work, ambition, and a little luck to become a billionaire.

Five Ways AI Can Make You Rich

It is evident from history that as technology shifts, the odds of being prosperous and wealthy increase. Today is one of the times where Artificial Intelligence (AI)-based innovations have revolutionized technology. This is the time when your creativity, hard work, determination, along with a little bit of luck, will turn you into a billionaire. Tech giants and businesspersons have been investing billions in Artificial Intelligence (AI) ideas and startups across every field worldwide. In 2016, Google, Amazon, IBM, and Microsoft invested over $20 billion in AI. To understand how does IBM make money required the utilization of artificial intelligence and machine learning concepts in real businesses. European Union planned to invest $22 billion in AI after seeing China and the US's growth. Artificial intelligence is helping

  • Bankers to better access loan risks
  • Farmers to predict crop yield
  • Doctors to diagnose disease
  • Marketers target the right audience and get sales
  • Manufacturers to improve their product quality
  • Cybersecurity teams to give privacy protection.

There are many reasons to consider modern AI to drive and make the money because of its potential for technological advances in different industries. One of the significant boons of AI comes with introducing automation and robotics in almost every industry. IDC is a globally well-known research market that generated over $156.6 billion in revenues in 2020. The company generated over 12% more gains than in 2019; no doubt, the company earned more, but this number was not as high as expected.

The MD Anderson Cancer Center started a "moon shot" project in 2013 for diagnosing and recommending treatments for several types of cancers. The system uses IBM's cognitive computing technology and costs up to $62 million. Similarly, many firms adopt this AI and machine learning business applications that promises to grow more than 42% annually and cross the $733 billion market size by 2027. If it's an evident future, why not open it and plant the seeds of future wealth because of AI?

Common Types of AI in Business Improvement

Companies need to focus on artificial intelligence in business intelligence rather than in technologies. Artificial intelligence offers solutions to real world business problems to support three basic needs of any business, including:

  • Automation in business
  • Obtaining insights via data analysis
  • Customer and employee engagement

Process Automation is the most common and cheap AI for automating digital and physical tasks. Robotic process automation technologies (RPA) come with advanced automatic systems in the form of code. It acts like humans inputting and sharing information across multiple IT systems. What makes it unique is discussed below:

  • Transfer data from a call center or e-mail system into the record.
  • Handle customer conversations and reach multiple systems to replace lost credit cards with updated information.
  • Use natural language processing (NLP) to lead legal documents and extract provisions.

Plus, artificial intelligence implementation is affordable and more comfortable to implement on a running system. RPA is also the least smart as the researchers don't have to train its program using NLP but add intelligence and learning slowly.

A second most common type of AI involved in business improvement is Cognitive Insights. It uses algorithms to detect patterns in vast data and interpret them to get meaningful insights. The given machine learning technology is expert at:

  • Predicting the customer’s product of choice and ignorance.
  • Detecting credit frauds and insurance claim frauds in real-time.
  • Warranty analysis to check the quality of automobiles and other products.

These machine learning insights differ from traditional analytics; the senses are more detailed, and models improve with time and training on a large dataset. Companies using machine learning are trained to recognize images and speech and share new data for valuable insights.

The third most important AI branch in business is Cognitive Engagement that engages buyers and sellers via natural language processing. Further, intelligent companies uses intelligent agents and chatbots to respond to the customers’ queries on time and alert humans where they’re unable to answer. The specific features of this category are given below.

  • Internal site linking for answering employee’s queries related to HR policy, benefits, and IT.
  • Health treatment recommendation systems for providers to create personal healthcare plans.
  • Increase personalization, engagements, and sales via a product recommendation system rich in language and image presentation.

Here are 5 unique ways AI can make you rich with its business related applications.

1. Stock Investment and Trading Bots

Traders and long-term investors have adopted AI in the evaluation of the trading market. They train AI models on past knowledge of market fluctuations to get insights about the market state with the least errors. Automated algorithms will make a real assessment of stock values based on already applied calculations. A new trader in the stock trading market can use AI to guide him in the market's behavior and fluctuations. The algorithm also suggests to the digital marketer the machine use to pick which stock is better over other.

How does it work?

It works by learning the dividends and return rates of stock. The system generates and presents an exaggerated list of options to the user to approve based on his interests. If the user is not around, the AI system has partial or full control of the stock when the trading season started. AI does most of the trading work for long-term investors by generating large data volumes based on cryptocurrency price fluctuations. One can develop these trading bots by themselves and integrate them into their existing system. If the algorithm feels a sharp down in the gross stock value, it responds to it immediately and sells the user's stock to avoid significant loss. In contrast, if the system notices a considerable rise, it alerts the user to invest more in rising values. Although a 100% success rate is not predictable, it helps the user to earn millions.


The trading market is a tricky category of earning money as at the day's ends, whether the investor comes home with unexpected money in pockets or even lost his savings. There are equal chances that an investor generates high revenues or at the risk of losing all compensation. AI, thus, involves handling the risks of losing money. A starter can generate up to 20% more revenues on the invested balance depending on the dividend's stability. Further investment and computing by AI may lead to an additional boost up to 15% or above. But it all depends on the assessment of AI and the performance of the stock.

The French multi-award-winning startup specializes in AI development -crypto bots for managing to buy and selling systems. Company holders can integrate these trading bots into their system without any risk of Ponzi schemes. These AI engines share predictive signals from historical market movements to generate consistent long-term returns.

2. Platform Development

Machine learning needs a reliable platform and a robust backend to optimize performance by enhancing modern AI computations. Professionals also hire part-time developers on a pay-per-hour basis or work on open-source projects. Some developers also pay money in exchange for using the concrete platform of a company and do experiments on it.

How does it work?

Many programmers join hands in a meeting to create new software and hardware. They divide work among each other based on the skill and demand of generated revenues. It occurs mostly in open-source programming, where developers first create a platform and then ping the organizations. Then they offer a platform to the public at affordable rates or free of cost. For instance, Google Collab, Google Cloud Platform, and Google App Engine are available for public use with free trials. The innovation of this technology helps young coders with basic AI knowledge to create new codes. Python community works similarly and pays the open-source coders for their programs.


Highly developed companies pay a handsome amount to their coders as compared to open-source communities. Developers of Google, Oracle, Heroku, and AWS are getting high income to fulfill their needs. For as estimation, their compensation ranges from $2222-$6511 and may exceed $8893. On the other hand, a stable but secondary source of income is open-source development. Script generators and library developers for TensorFlow and Keras have planned to manage moderate-income amounts ranging from $1300 to $3500. However, platform development is a stable source of income due to continuous returns on a generated code.

3. Teaching Online

The online teaching and learning passion have been increased since the last few decades, especially in last year due to COVID-19. Furthermore, many technology users are ready to pay for learning data science and AI via online courses because of their popularity. Teaching, comparatively, requires less effort but background knowledge to be shared and delivered. A talented person can make a video series based on pay-per-view or start a paid online course about any demanding AI skill of the day. But the content you prepare must be outstanding comes with an extraordinary presentation that compels learners to join it at first sight.

Moreover, if the instructor makes quiz weekly to test what his students learned in the last week will be a plus point. Artificial intelligence and machine learning are hot programming disciplines, and the instructor can attract more audience by providing them necessary codes which the users use to learn.

How does it work?

The instructor will provide practice codes, tools, GitHub links, and links to different development platforms at extra wages. So, it will help them to generate more money in a given time without any extra effort. Use the right platform to guide the community with comprehensive knowledge and fantastic presentation. The instructor must host the course at the forum with frequent visitors; for instance, Udemy and Udacity are well-known for delivering online courses. Developers and programmers explain and outline the content comprehensively at low initial costs to attract more audience than usual. It helps in getting a stable source of income and making the talent popular.


Teaching is a part-time job that one can also do in his new mood and make it a second income source. However, the compensation amount depends on the content's quality and the trick of gaining the audience's interest. The programmers can make it their only job as it's a burning skill and the digital world's future. No one can stop their course from spreading among a massive audience and ultimate bonus compensation after gaining popularity. The only thing needed is perfect knowledge and perfect skill of delivering it.

4. Contracting and Freelancing Programs

The easiest way to make money with AI is to sell the skill or work with an organization that knows the potential and worth of that expertise. According to Indeed's report, a machine learning engineer gets an average annual salary of $146,085 by 2018, increasing more 344% than in 2015. However, these statistics are about the United States, and every country has different demand for this skill. After the pandemic, people have become more aware of online selling and earning technology in this digital age. The only thing that an expert seller needs are a laptop and a stable internet connection to connect with technology-thirsty people worldwide.

How does it work?

Not only in the field of AI and machine learning, but freelancing also has its way in every corporate department. Sellers make virtual relations with their clients via online freelancing platforms, serve their buyers, and get money in return. Event planning, report completion, budget generation, and management are top activities of interest. Many businesses outsource young coders to do their programming tasks in AI cases because of their up-to-date knowledge of the subject matter. It's more comfortable than hiring an individual for a quick charge and paying him monthly.

It is also easier for freelancers to sell their services without going out of their homes. There is a chance for a freelancer expert in the corporate business to get a handsome amount of money in less time. Various online platforms, including Upwork, Freelancer, and Fiverr allow the young generation to cash their skills via enormous freelancing artificial intelligence opportunities.


In terms of compensation, freelancing in AI and ML programs range from less to moderate as it depends on the complexity of the task. Further, it also depends on the time consumption by that job and other freelancers' availability doing the same position. Uniqueness in the offered job enhances its demand in the market as there are few writers available for writing AI-based content than for general niche content creators. Data optimization, Artificial intelligence development, and augmentation are highly demanded skills and can pay handsome compensation to their expert. In general, people do freelancing in their spare time as a second source of income, as for a newbie, it's not a stable source of earning money.

5. Startups

AI startups are offering enterprise cognitive technology solutions, cognitive algorithms, and in-depth successful industry solutions. These startups need access to unique datasets and in-depth domain knowledge to attract and retain rising AI talent. It is different from a standing app in the mark, but it must solve real-world problems in real-time. AI startup winners focus on industry and enterprise solutions where talent deals with high valued use cases. Therefore, at present, there are a handful of winners in AI startups and getting tantalizing cheques from the giants.

How does it work?

Modern-day startups are taking their inspiration from the ambitious Silicon Valley AI platforms. But because of the high-paying requirement and investment risk, it is not the cup of tea of many professionals. Young passionate industry professionals are interested in replacing the machine learning field with automated novel models to technology. Elon Musk says:

“Starting a business is like chewing glass”

Technology implementation requires expertise in addition to resources and funding. Another significant factor to consider is the passion and human resources of individuals to work as a team. Without teamwork, organizations failed to become a brand and tangled in a group of deviating people. The last but least requirement of a startup is failure management that's compulsory to cope with immense loss. According to a study, many startups fail because of their starters' inability to retain it after a failure. As an intelligent entrepreneur, one should have the patience to wait for its AI model to reach a marketable place.


It's not a compulsory shot for a business startup to rise and flourish as there are equal chances of its failure. But when the business cracked the code of success, no one can stop its sky-touching compensations. How much a business grows and earns depends on the AI type dealing with and its market interests. If the startup is concerned with a novel community-serving product, there are higher chances of its fast and ever-green growth, eliminating the need for a second income source. Most common AI incorporated businesses include Amazon, Uber, Practo, and Cvent become popular and world-known businesses and don’t need funding because of their raised revenues. However, small-sized AI-businesses may generate lee revenues, but the amount will be enough to sustain a wealthy livelihood and a healthy business environment. Small-sized AI-startups have to benefit from fewer risks and more room to grow after earning a legal firm's name.

Income optimization and revenue generation becomes sustainable via AI, machine learning, robotics, and automation. Further improvement and personal skill assessment and its comparison with the competitors can make it better and ultimately the primary source to generate wealth.

Wrapping Note

All the above-discussed methods are real methods of artificial intelligence for humans to make money online, and they are also exclusive to some extent. Adopt those methods which are still less popular among the population to gain rapid growth and income. Moreover, with the growing potential of AI and machine learning, one can rely on these two pillars for generating high revenues with expert skills. Personal skill assessment, market demand, and competitor analysis can increase revenues and make it the only source of earning maximum compensation.

Cheers :-)