Rule-based AI vs machine learning: what’s the difference?

September 13, 2021
Hot topics 🔥
AI & Data Science
Mario Grunitz
Rule-based AI vs machine learning: what’s the difference?

In order to obtain conclusions from data using artificial intelligence (AI), we commonly utilise one of two contrasting approaches: rules-based and machine learning. In some cases, however, it is possible to use both approaches together to get maximum value. It is crucial to understand both the strengths and weaknesses of both technologies in order to identify the right solution for your business. 

What is rule-based AI?

A system designed to achieve artificial intelligence (AI) via a model solely based on predetermined rules is known as a rule-based AI system. The makeup of this simple system comprises a set of human-coded rules that result in pre-defined outcomes. These AI system models are defined by ‘if-then’ coding statements (i.e. if X performs Y, then Z is the result). Two important elements of rule-based AI models are “a set of rules” and “a set of facts” and by using these, developers can create a basic artificial intelligence model. These systems can be viewed as a more advanced form of robotic process automation (RPA).

To learn more about robotic process automation (RPA), visit our blog page.

Rule-based AI models are deterministic by their very nature, meaning they operate on the simple yet effective ‘cause and effect’ methodology. This model is immutably structured and unscalable, therefore it can only perform the tasks and functions it has been programmed for and nothing else. Due to this, rule-based AI models only require very basic data and information in order to operate successfully.  

What is machine learning AI?

A system designed to achieve AI utilising the power of machine learning (ML) is known as a machine learning model. Machine learning systems are designed to define its own set of rules based on data outputs it has access to, without human intervention. By only taking the outputs from data or experts, ML systems thus utilise a probabilistic approach: one that takes into account variations and probabilities to create informed results. This means they constantly evolve, develop, and adapt when new information is added. 

Predominantly used as an alternative method to address some of the challenges of rule-based systems, such as practical training of large datasets, ML systems are used on larger projects that require scalability. As a direct counterpart to rules-based AI systems, ML systems are mutable and nimble, allowing them to transform data or value through mutable coding languages. The more data you feed the system, the more accurate it becomes in identifying patterns.

Choosing the right approach

Based on the prerequisites and functionalities of rules-based AI and ML systems, which approach is best? As in all cases, it depends on your project requirements.

Rule-based systems

Rules-based systems are perfectly suited to projects and applications that require small amounts of data and simple, straightforward rules. Think of this as a fit-for-purpose system: one that is highly effective within its predetermined parameters, and pretty useless outside of them. Thus, rules-based systems are great for repetitive processes that require little-to-no human decision making, such as fault analysis, email routing using triggers, and basic searching, among many others.

Rules-based systems are great if you need information quickly, as the limited parameters of the systems allow for speedy results. Additionally, rules-based systems are seen as incredibly pedantic and thorough which is why they are often used in processes where errors cannot be tolerated, such as medical diagnosis and finance processing.

Machine learning systems

On the other hand, ML systems are commonly used when large volumes of relevant data records are available for making more accurate predictions. For any processes that have multiple factors, situations, numerous potential outcomes, ML systems are your best bet. 

ML systems are used when you are required to predict an outcome but do not necessarily know how to do so. For example, ML systems are great for sales lead qualifications and customer support automated responses — any situations that have multi-variables. For this reason, ML systems are best suited to rapidly changing environments like e-commerce recommendations and general forecasting. YouTube and Netflix auto-suggestions are a great example of this, as the algorithms learn from your activity and are trained to assess your preferences and suggest content based on your preferences.

If you are looking for more technical detail on how to best choose which development route is best for you then we suggest reading this thorough article by Capital One.

Best of both

There are some systems available that combine rules-based AI systems with ML systems in a ‘best of both worlds’ situation. By combining the two approaches, businesses are able to make up the shortfall of each system, providing them with a thorough system that is both 100% accurate and robust. 

This comes in the form of rule-based machine learning (RBML) systems that are able to identify and adapt their own set of  rules. The defining characteristic of these systems are their ability to identify and utilise a set of relational rules that collectively represent the knowledge captured by the system. It does this by utilising its learning algorithm and relying on a knowledge base, meaning it does not require human coding or intervention for defining ‘if-then’ statements. 


Both rules-based systems and ML systems have their associated strengths and weaknesses. But these can only be determined by your project requirements. If you are in need of precision within a relatively small framework parameter then rules-based systems are for you. If, on the other hand, you have access to large amounts of data and need to predict something, then ML systems are your best bet. If, however, you have business needs that require both precision and large data processing used to inform predictions and forecasting, then a hybrid approach may very well be what you are after.

Mario Grunitz

Mario is a WeAreBrain Co-founder. With more than 15 years of experience in the tech space, he has worked all over Europe and held countless leadership positions in corporate, startup and agency spheres.

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