Rule-based AI vs Machine Learning: Which One is Best for Your Enterprise?

Updated on Apr 18th, 2024


With the emergence of artificial intelligence, many new concepts came to the market. Be it computer vision applications or conversational bots for customer services, AI transformed various aspects of the global sector. Likely, rule-based systems and machine learning solutions are other critical AI aspects that are gaining huge momentum in the digital era.

We all know that machines do not inherit self-understanding capabilities. They need to be taught to make them smart and automated. That’s where the rule-based vs. machine learning concept comes to play! Both the rule-based systems and the machine learning solutions analyze the unstructured data and then offer insights. However, the confusion between rule-based vs. machine learning can be seen in every other organization. 

So, how do you choose the best of the two? Well, that’s what our blog is all about! Today, we will draw a comparison between rule-based vs. machine learning so that you could choose the best for your enterprise! But, first things first.


What is a Rule-Based System?

Before the inception of AI and machine learning, global businesses used to work with rule-based systems for creating smart machines. To put it simply, a rule-based system can be defined as a robust AI model that works on certain rules specified by the developer. The system considers various situations and then offers the best outcomes based on rules specified by the user.

You can use the If and Then statements to build efficient rule-based systems depending upon your needs. Also, the possibility of mistakes is eliminated in the rule-based systems are they only work as per the rules specified by you. However, many businesses also take it as a drawback of these AI applications.

What is Machine Learning?

Another most popular and trending AI application is machine learning. If you consider a rule-based system to be intelligence constrained, then a machine learning solution could be considered an adaptive learner. A machine learning solution analyzes the user inputs and then provides the best output. 

As the machine learning solution continuously learns from the user inputs, the outputs are also enhanced with each use-case. That means you could have the most accurate prediction after some time with a machine learning solution. That’s what becomes the base of recommendation systems used in Amazon, Facebook, or Netflix. Plus, not to forget, machine learning solutions learn on their own, which means you do not have to specify any rules or algorithms for making them automated and smart!

Read More: What is Machine Learning and how it helps in Business Enhancement?

Rule-Based vs. Machine Learning – Key Differences

Now that you know both the terms, let us quickly draw differences that make rule-based systems and machine learning solutions separate.

1.Learning Models

The foremost factor that you need to consider during a rule-based vs. machine learning battle is the learning model. If you want a deterministic approach for your custom AI solution, choosing a ruled-based system is the best choice. On the other hand, a machine learning solution always offers a probabilistic approach. But, what does that mean?

Well, it simply means that a ruled-based system will analyze the inputs and rules to predict whether a given output can be achieved or not. On the contrary, a machine learning solution will learn from the user inputs to predict the best possible outcome for a certain condition. For instance, if a person took a loan from the bank, with a rule-based approach, we can predict whether the user will pay a loan or not. With an ML solution, we can know how much loan will the customer repay and at what time!


Another vital factor to be considered during the rule-based vs. machine learning battle is using the enterprise AI solution. You need to be very clear where you would use your AI system as it will directly impact the choice of your data manipulation technique. 

Here are some of the applications of a rule-based vs. a machine learning solution.

Uses of Rule-Based Systems:

  • Predicting the Correct Diagnosis to Doctors
  • To Play an Online Game
  • Software Testing
  • Manufacturing Process Automation

Uses of Machine Learning Solution:

  • Recommendation System
  • Speech Recognition
  • Self-Driving Cars
  • Virtual Assistant
  • Traffic Prediction
  • Fraud Detection

Hence, with these points, one could seamlessly differentiate between ruled-based and machine learning and choose the best for their business.

3.Internal Working

It is often seen that rule-based systems are considered less flexible than machine learning systems. However, their internal work works quite differently. In the battle of rule-based vs. machine learning, the rule-based system wins due to their simplified working. We all know that rules are specified in a rule-based approach, and that becomes the working base. One can seamlessly predict the outcome or even update the rules if he/she has access to it.

However, in machine learning solutions, the models work anonymously, and hence, the user could not get any idea of the outcome. Moreover, the output also differs for each use-case. For instance, in Netflix or Spotify, the suggestions would be different for different users based on their past experiences and browsing patterns. That’s why the working of machine learning is a bit complicated and unpredictable!  

4.Type of Data

You might know that in a rule-based system, the data requirement is generally low as it solely works on the parameters provided by you. However, that’s not the case with machine learning solutions!

The machine learning solution works with a huge dataset and then offers the outcomes based on the learnings. For instance, if your data type is restricted to some values (like the name of the city/state), then rule-based systems are best. Whereas, in the case of recommendations, machine learning would work best!  


5.Project Needs

Another crucial factor that you need to consider during rule-based vs. machine learning is your project needs. You need to clearly understand your project and business needs before coming to any solid conclusion. Suppose, if your project is all about a food delivery app, the rule-based system will work best. 

However, if it is fleet management software, then for route optimization and predictive analysis, machine learning models would work best! For that, the best way is to consult with an expert. An AI expert would help you choose the most promising and reliable data model to bring success to your solution.


Besides features and trends, if the businesses are most concerned about something is the cost. Your budget greatly impacts the technology that you use in your custom software development.

For instance, if you have a normal budget, then going for a simple rule-based system would not be bad. But, if your requirements are high, then opting for an advanced and unsupervised machine learning solution is the best decision.


The amount of scalability you need in your enterprise AI solution also impacts the rule-based vs. machine learning battle. It is no doubt that both data manipulation techniques are best for the business. However, one thing that could not be overlooked is their flexibility.

Despite offering smart features, rule-based systems are often less flexible than machine learning solutions. That’s because, in a rule-based approach, the system only works as per the initial rules specified by the user. Whereas the machine learning models continuously learn from the user inputs and upgrades themselves with each use case.


Finally, we have the latest market trends that impact the rule-based vs. machine learning battle. Every other company today wants to have the best and latest digital solution for their business and customers. That’s where machine learning solutions win over rule-based systems!

No doubt that rule-based systems are best for some AI use cases, but the world today has become much more advanced than ever. In such a scenario, we have many machine learning models that could win over rule-based systems without compromising the functionality and cost. 

Differentiation Factors

Rule-Based Systems

Machine Learning Solutions

Learning Models Deterministic Probabilistic
Usage Can be used in simple and straightforward applications Can be used in predictive analysis
Internal Working The internal working is simple and rule-specific Internal working solely depends on user-inputs
Data Type A low volume of data is required for output Requires a huge volume of data to give output
Project Needs For Static fields, a rule-based system could be used For dynamic and predictive fields, machine learning solutions are used
Cost A rule-based system is less costly Machine learning solutions are somewhat expensive than the rule-based systems
Flexibility Less Flexible More Flexible
Trend Not much in trend A trending AI application

Besides the above-discussed factors, there are many others that you may need to consider before choosing a reliable AI application model. Hence, the best way would be to consult with an AI expert that could best analyze your business needs.

An expert AI development firm will have a complete idea of the latest and most reliable AI trends and models that would fit best for the success of your project.  

That’s where Matellio comes in!

With over 12+ years of experience, Matellio today has become a leading choice of businesses when it comes to enterprise AI solutions. Whether you are a startup, or even a Fortune 500 company, with our flexible pricing models, and trending UI/UX services, all your business needs could be satisfied. Moreover, with our flexible hiring services, we ensure you the best AI software development in the most cost-effective manner. 

Hence, reach us today, and become a part of the successful companies of the world. Leverage our free expert consultation services to transform your business idea into a reality today!

Till then, Happy Reading! 

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