AI vs ML Explained: Differences & How They Work

AI vs ML: Differences and how they work

When you hear about artificial intelligence (AI), you often come across another common term: machine learning (ML). These two are closely related, but they aren’t the same thing. AI is the broader concept of creating machines that can mimic human intelligence. ML is a subset of AI that focuses on teaching computers to learn from data and improve over time without being explicitly programmed.

Think of AI as the big picture, the universe of intelligent systems, and ML as one of the most powerful tools inside it. Let’s break down what each means, how they’re connected, and where they’re being used today.

What is artificial intelligence?

Artificial intelligence refers to the broad field of building machines that can simulate human decision-making and cognitive functions. It involves using computers and algorithms to analyze data, identify patterns, and solve problems in ways that resemble human intelligence.

Examples of AI include:

• A computer playing chess against a human
  
• A self-driving car navigating city traffic
  
• A virtual assistant like Siri or Alexa that can answer questions and perform tasks
  

At its core, AI is about enabling machines to reason, plan, and make decisions.

What is machine learning?

Machine learning is a specific branch of AI focused on using algorithms to analyze data, find patterns, and improve over time. Instead of following fixed instructions, ML models are trained on data to predict outcomes and make decisions.

For example, ML can be trained on thousands of emails to learn how to identify spam, or on years of stock data to forecast financial trends.

Supervised learning

Algorithms are trained on labeled data—meaning both inputs and correct outputs are provided. These models are commonly used in tasks like detecting spam or classifying images.

Unsupervised learning

Algorithms analyze unlabeled data to find hidden patterns or groupings. This is useful for tasks like customer segmentation or fraud detection, where patterns aren’t predefined.

Deep learning: where it fits in

Deep learning is a specialized type of machine learning that uses neural networks with many layers to process vast amounts of unstructured data. It powers some of the most advanced AI applications today—like voice recognition, image classification, and generative AI tools such as ChatGPT.

While all deep learning is machine learning, not all machine learning uses deep learning. The difference lies in scale and complexity.

How AI and ML are connected

All machine learning is AI, but not all AI is machine learning. AI is the overarching field, while ML is one of the most important and widely used methods within it.

As ML becomes more advanced, combining with other AI techniques, it strengthens the overall capabilities of AI, making systems more adaptable and human-like.

Differences of AI vs. ML

Here’s a quick side-by-side view of how AI and ML compare:

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Benefits of AI and ML

Both AI and ML bring distinct advantages, but together they can be even more powerful.

Benefits of AI

• Automates complex tasks
  
• Simulates human reasoning
• Powers systems like chatbots and virtual assistants

Benefits of ML

• Improves predictions over time
  
• Learns from historical and real-time data
• Reduces reliance on explicit programming

Benefits of using AI and ML together

While any use of ML is a form of AI, the real benefits come from using both AI and ML together. By combining tools, techniques, and actions, you can achieve your desired output much more effectively and efficiently. Here are some big benefits of using AI and ML together: 

Massive data set analysis

No human can process data the way machines can. By harnessing the power of AI and ML combined, you can process, classify, or group massive data sets. This enables you to discover valuable insights and help make informed decisions based on that data.

Problem-solving

One big problem with some types of data is the delay between analysis and action. By using AI and ML together, you not only get to analyze the data but can take specific steps based on the insights you’ve gained.  This ensures that your organization can respond and adapt quickly to nuances and fluctuations in the data. 

Enhanced job performance

When people discuss AI or ML, they often talk about AI taking over people’s jobs. The reality is that, given the current state of  AI, it’s much more likely to enhance people’s jobs rather than take them over. By utilizing ML to perform routine and repetitive tasks, employees can focus on more strategic or complex problems to support a business. 

Application across industries

One key benefit of AI and ML is that they aren’t confined to a single industry but have a variety of use cases across different fields that will continue to allow for wide adoption and use. 

Challenges and limitations

While AI and ML have enormous potential, they aren’t without challenges:

• Bias in training data: Algorithms can inherit human biases if the data is flawed.
  
• Data quality: Poor or incomplete data leads to poor results.
  
• Explainability: Complex models like deep learning can be “black boxes,” making it hard to understand why they produce certain outcomes.
• ‍Resource intensive: Training advanced models can require significant computing power and expertise.

Use cases and examples of AI and ML

One way to better understand AI and ML is to see how they’re being used today across different businesses and applications. 

Writing

ChatGPT’s release helped usher in a new wave of awareness and interest in AI. ChatGPT is often associated with the term generative AI, which at its core relies on ML models to function. These models are trained to recognize patterns and structures in the data they are provided. They are then capable of generating new information for the user in a similar format that aligns with the recognized input. 

Manufacturing

One benefit of utilizing AI and ML is increased efficiency. Because these tools can analyze more data than humans can, they can identify unique patterns and areas to improve processes. This is especially useful in complex processes like manufacturing. By integrating AI and ML into manufacturing, companies can better identify and predict bottlenecks, prepare for supply chain disruptions, and manage machine maintenance. 

Finance

Many financial organizations are excited about the application of AI and ML to help them identify and prevent fraud in the financial services industry. However, there are wider applications for AI in this field. For example, some financial services companies use ML models to analyze stock market data so they can  automate investment and trading decisions. Others use AI tools to more easily and securely authenticate users to accounts or enhanced services. 

Healthcare

Much of the attention surrounding the use of AI in healthcare is on improving patient outcomes and diagnosis. While AI is contributing to these areas and will continue to do so, some of the biggest healthcare advances using AI and ML are in pharmaceutical development. 

For example, some companies employ ML to analyze vast quantities of data related to drug development and clinical trials. This analysis allows companies to better understand new and beneficial applications for drugs currently on the market or in development. 

E-commerce

One field that greatly benefits from the near real-time power of AI is e-commerce. By applying ML and AI models to real-time customer data, organizations can better segment customers based on subtle patterns within the data. This enables them to provide personalized recommendations and efficiently manage supply chains to meet both current and future demands. 

Marketing

Using AI and ML to support marketing departments allows teams to better understand customer needs and forecast trends. It also allows teams to automate and streamline simple, everyday tasks like reporting, testing, and analyzing so marketers can focus on more strategic initiatives. 

Customer service

Many call centers try to find ways to replicate the success of their best agents across the organization. Some tools use AI and ML to train and guide employees through dynamic prompts and support scripts that adapt based on the unique needs of each caller. These tools rely on multiple layers of ML algorithms that are integrated to form a neural network. This allows customer service reps to have a valuable tool to help them problem-solve and support unique needs of individual customer. 

Key takeaway

Artificial intelligence and machine learning are closely linked but not interchangeable. AI is the broader goal of building systems that act intelligently, while ML is one of the most effective ways to achieve that goal through data-driven learning. Understanding both and how they work together helps organizations unlock deeper insights, improve decision-making, and stay competitive in a data-driven world.

Find out how Domo can help your business embrace AI and ML tools.

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