artificial intelligence and machine learning difference and benefits

We live in a technology world where Artificial intelligence and machine learning are the foundational pillars for running smooth business operations. Businesses use these 2 technologies (AI & ML) to create intelligent solutions that mimic human behavior. 

AI/ML technologies are there to transform how businesses conduct their operations and improve people’s lives in various ways. 

In this blog, we will dive deep to make you understand the benefits of AI and ML and how they differ. 

What is Artificial Intelligence? 

Artificial Intelligence
Artificial Intelligence

Terms like AI and ML are used interchangeably but are entirely different. Artificial intelligence means adding capabilities to machines so that machines can perform tasks that, at present, humans are doing. 

AI refers to simulating human intelligence in machines such as speech recognition, visual recognition, decision-making, language translation, etc. 

It is about making computers intelligent to solve complex problems. 

AI is everywhere from the moment you start interacting with voice assistants such as Siri and Alexa to the AI-powered chatbots (ChatGPT, Bing Chat, Bard) you are using to smoothen your business processes by entering a prompt and the chatbot provides an instant response within few seconds. 

So, AI isn’t the future; it’s now. AI tools are everywhere around us, blowing our minds ranging from shopping online, ordering food, and surfing the internet to enjoying music and games, and so much more than that. 

What is Machine learning? 

Machine Learning
Machine Learning

Machine learning is the subset of AI wherein machines learn from data, identifying patterns and trends in data to make predictions. Here, machine learning uses historical data as the input set to develop new output.   

With the help of machine learning, computers learn from large amounts of existing data.  

For Instance – ChatGPT uses ML capabilities and is trained on several data sets, including articles, books, social media, websites, etc. 

ML makes use of historical data to detect the next set of patterns.   

For instance – If you are creating an ML model for detecting the image of a dog, then it will give results for dog images. However, it becomes unresponsive if you are trying to input a new set of data, such as a cat image. 

The most popular example of Machine learning is product recommendations. If you are thinking of how Amazon and other retailers come up with product recommendations that you will likely purchase. 

Because of ML, Amazon analyses customer purchasing history, preferences, and behavior and is likely to suggest similar categories of products that the prospect is interested in. 

How are AI and ML Connected?  

How AI and ML Connected
How AI and ML Connected

Al and ML are closely related to each other, but they are not the same thing. AI’s objective is to give computers the capabilities that humans can do.   

While machine learning deals with developing models from which computers can learn from data, analyze data and make predictions about the world.  

According to Microsoft, AI is when a computer uses AI technology to think like humans and perform complex tasks. At the same time, ML relates to how a computer system develops its intelligence. 

AI and ML have a strong relationship with each other:  

  • AI and ML focus on building intelligent programs that perform complex tasks and help businesses take over human tasks and finish them quickly.  
  • AI is an umbrella term for ML, and ML is the subset of AI/ a category of AI. ML is used for various AI apps and even makes AI better.  
  • Both AI and ML can learn and change over time. They aren’t stagnant. AI follows a specific set of rules to handle a certain number of tasks. On the contrary, machine learning learns independently, analyses the data, and predicts a similar range of patterns in the future. 

Benefits of AI 

Here are some of the benefits of AI-  

1. Reduces Human Errors 

Human errors start as humans make mistakes from time to time. In comparison, AI reduces human errors as decisions are already taken from previously gathered information. Hence, errors are disappearing because machines are trained to provide accurate results.  

For Instance – Robots can perform complex surgical procedures with 100% accuracy. Under this, AI provides surgeons with a new set of eyes where AI is trained on large volumes of data and acts as a learning tool for surgeons. 

2. Provides 24*7 Availability  

CNBC Report
CNBC Report

Various research studies indicate that humans produce up to 3-4 hrs./day. They need breaks to refresh themselves and balance their personal and professional lives.  

But AI can perform multiple tasks quickly and does not need any breaks.   

For Instance – AI chatbots provide round-the-clock support to customers and are there to answer complex queries to their problems. 

3. Helps with Repetitive Jobs 

AI helps businesses perform repetitive jobs that require little or no creativity. Using AI, businesses automate mundane tasks that free up human staff and let them perform other important tasks which require their participation.   

For Instance – AI can perform repetitive tasks such as automated data entry, invoice processing, software testing, visual quality inspection, providing customer support, etc. Thus, it allows humans to focus on higher-level tasks.  

Benefits of ML

Here are some of the benefits of ML-  

1. Helps in Forecasting Demand  

Companies are under a burden to analyze current trends and estimate demand in the future, but integrating machine learning models in their AI systems can help businesses to analyze customer behaviors, predict market trends, etc. 

For Instance – ML is a lifesaver for e-commerce websites such as Amazon.  

Amazon uses ML to analyze customers’ browsing behavior and purchase history and then come up with offering the right set of products and lucrative deals to customers. Thus, Amazon can increase its sales and boost its revenue. 

2. Operational Efficiency Growth  

Machine learning possesses the capability of automating repetitive tasks. Using ML, chatbots work round the clock, and they always stay energized as they can analyze massive volumes of data without burning out. 

3. Detects Fraud  

Detecting fraud plays a pivotal role for businesses, especially banks.  

Businesses can use machine-learning algorithms to identify potential fraud like insurance fraud, credit card fraud, etc. ML sends alerts to the bank if someone tries to cheat the banking system or if the person is not authentic. 

For Instance – Mastercard uses AI and ML technology to track various processes such as location, time, transaction size, purchase data, etc. The system evaluates account behavior to determine whether the transaction is fraudulent. 

Difference Between Artificial Intelligence and Machine learning 

Basis of comparison  Artificial Intelligence Machine learning 
Meaning AI refers to the technology that brings intelligent capabilities to machines to perform tasks more efficiently. In addition, AI cannot learn from its mistakes.  ML, the subset of AI, allows a machine to learn from past data.    
Flexibility AI does not rely on datasets. It’s more flexible than ML  ML models are particularly used for doing predictive analytics  
Type of data AI deals with structured, semi-structured, and unstructured data  Machine learning involves dealing with structured and semi-structured data  
Applications There are various applications of AI, including Siri, providing customer support through chatbots, machine translation such as Google translate, etc.  Image recognition, speech recognition, self-driving cars, virtual try on etc., are some applications of ML  
Decision making AI strives to create a system that mimics human behavior  ML is dependent on AI. It creates algorithms that redefine the AI model.  

Wrapping Up  

Now, you get an understanding about Artificial intelligence and machine learning. To summarize, AI is there to solve complex tasks requiring human intelligence.  

On the contrary, Machine learning focuses on performing tasks by learning from data to make future predictions and then helping businesses to make informed decisions.  

It simply points to one thing – Machine learning is the subset of AI, but not all AI is machine learning.  

Do you need help in designing intelligent solutions?  

At BigOhTech, we have a certified team of 20+ AI/ML experts having expertise in the AI/ML domain. Our AI/ML experts aim to develop intelligent solutions that align with your business goals. 


Q1. What is the main difference between AI and Machine Learning?  

AI deals with developing intelligent programs which mimic human behavior and can perform human-related tasks. At the same time, machine learning involves creating algorithms to learn from past data and forecast future predictions. 

Q2. How does Machine Learning contribute to Artificial Intelligence?  

Machine learning acts as the pathway to AI. ML is the subset of AI. Machine learning is related to how machines develop their intelligence. 

Q3. What is the future of AI and Machine Learning?  

The future of AI looks promising. In the coming few years, AI will be used in a wide array of industries, such as –  

1. India is the second most populated country after China. There is a lack of healthcare facilities and lack availability of doctors, so AI is there to detect human diseases based on symptoms even if you don’t go to doctors.  

2. AI is going to transform the education system. There is no need for skilled laborers involved in manufacturing industries, as everything is automated through robots.   

3. Research studies predicted that AI would be used in cybersecurity, which will identify the origin of cyber-attacks. 

On the contrary, Machine learning too helps various industries to make informed decisions in the future-  

1. Computer vision is a significant ML advancement that allows computers to identify objects in videos and images. As advancements are going on in machine learning, the error rate will likely be reduced from 26% to 3%.   

2. ML uses a recommender system to understand the needs and preferences of the target market and then come up with tailor-made suggestions. This is how Netflix knows which episodes are your favorite. 

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