in Blog

May 26, 2023

Generative AI removes limitations of traditional rule-based automation


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

The worldwide intelligence process automation market is projected to reach $30 billion by 2024, up from $20 in 2021. [1] Traditionally, organizations and developers relied on rule-based systems to automate repetitive tasks, thereby increasing productivity and efficiency. Unfortunately, traditional rule-based systems are barred with numerous inefficiencies, particularly when faced with real-world situations that involve unstructured data and need some level of problem-solving.

Generative AI systems powered by powerful deep learning models can overcome most of the challenges rule-based systems face due to their ability to adapt, learn, and innovate based on training and input data.

This article will take a closer look at how generative AI models can overcome the limitations of rule-based systems, including the characteristics of both technologies and the advantages generative AI offers over traditional rule-based systems.


What is rule-based AI?

Rule-based AI systems are systems that apply a set of predefined, human-made rules to make decisions or provide solutions for specific problems. These rules are formulated by domain experts who code them into a set of rules that AI systems can easily interpret.

A typical rule-based AI system requires a set of facts and another set of rules for governing the manipulation of source data. This unique mode of operation makes rule-based systems especially suitable for applications involving complex, incomplete, or uncertain information, such as fraud detection, financial analysis, and medical diagnosis. [2]

When applied correctly, rule-based systems can offer numerous benefits, including access to a transparent and interpretable framework for decision-making. They are also easier to update and maintain compared to more complex models like Generative AI models.

However, rule-based systems face several drawbacks, including their inability to handle situations outside their predefined rules. Any inaccuracies in their rules and source data can also affect their accuracy significantly.

Benefits of Rule-Based AI Systems

Rule-based systems offer numerous benefits when it comes to solving problems and making important decisions in their field of focus. Some of the most notable benefits of rule-based systems include:

Benefits of Rule-Based AI Systems

Adaptability and explainability

Rule-based systems are typically designed based on a set of explicit rules developed by domain experts and coded into the system. In most cases, these rules are written logically like ‘if-then’ statements, making them very easy to understand and explain. This unique quality makes rule-based systems especially suitable for application in fields like finance and healthcare, where decisions have far-reaching consequences and, thus, have to be explained.

Potential for high performance and accuracy

Rule-based systems get their ‘knowledge’ directly from domain experts. This makes them exceedingly accurate and reliable in their fields of operations. Most of these systems are also designed to optimize specific performance metrics like speed and accuracy, making them even more suitable for specific applications.


Rule-based AI systems can be designed to suit the needs of a specific organization, field, or other purpose. For instance, they can be designed to reflect the policies and practices of a specific organization or optimize performance when it comes to solving specific problems.

Limitations of rule-based systems

One of the biggest limitations of the rule-based approach is its lack of flexibility in handling situations that fall beyond its predefined rules. Essentially, if a situation does not fall within the system’s predefined rules, the system may provide inaccurate results. This issue also arises in cases where the predefined rules are incomplete or incorrect.

Rule-based systems also face major scalability issues. As you scale the model, the number of rules increases proportionately, which, in turn, increases the system’s complexity. With time, the system becomes more difficult to maintain and update. Scaling rule-based systems can also lead to increased computational requirements, which, if not met, might lead to longer computation times.

Some other limitations of rule-based systems include the following:

Issues solving problems with many constraints

Rule-based systems are best suited for simple applications. Say, for instance, your problem involves a lot of rules and exceptions that must be followed in order to get accurate results. In that case, you might have a problem writing viable code that covers all rules and exceptions. And, even if you manage to write them, any error or contradiction in the rules might render the model inaccurate and ineffective.

Limited intelligence

The intelligence of rule-based systems is limited by what’s explicitly programmed into them. Therefore, their ability to achieve intelligence is limited to what developers know at the time, and any further intelligence would need to be programmed into them beforehand.

What is generative AI?

Generative AI is a subset of machine learning that uses a set of algorithms to generate new, seemingly realistic content from training data. These models are trained on a massive amount of training data in an unsupervised way such that they ‘learn’ to identify patterns and create new data. Generative AI models can create anything from text and images to videos and music. Some models, like GPT-3.5, can even generate code. [3]

Read more about What is generative AI, and will it replace human creativity?

Benefits of generative AI

As a field of study, generative AI can help developers create computers that can perform complicated tasks that are far beyond the capabilities of regular algorithms. Some of the most notable benefits of generative AI include:

benefits of generative AI

High-quality output

Generative AI models generate data through self-learning and from multiple sources. For instance, models like GPT-3 generate text from tens, hundreds, or even thousands of different sources, which enables it to learn and use complex grammar rules without necessarily being programmed to do so.

Reduced project-related risk

Generative AI models enable teams to create designs that can be customized to fit any project. For instance, developers can create multiple versions of a particular design and test them against each other to see which works best for that specific scenario.

A better understanding of abstract theories

Generative AI models can understand abstract theories better in both the real world and in simulations. This fact is based on the notion that generative AI models can learn from their training data and use the knowledge to create relevant, high-quality new data.

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How does generative AI remove the limitations of rule-based automation?

Generative AI offers added flexibility and adaptability

Unlike traditional rule-based automation models, which rely on a set of predefined rules to draw conclusions, Generative AI models can adjust their response based on the uniqueness of the scenario, thus making them more flexible. Generative models are also more adaptable to different scenarios due to the abundance and versatility of their training data.

Generative AI can handle complex and unstructured data

The mere fact that rule-based systems require clearly defined explicit rules for every possible situation means that they often struggle with complex or unstructured data.

Conversely, generative AI models are trained on vast amounts of unstructured data, meaning they focus on learning the relationships within the given datasets to generate new data rather than relying on predefined rules. This characteristic enables them to handle far more complicated tasks, even those involving unstructured data.

Enhanced creativity and novelty

The rule-based approach is quite restrictive when it comes to generating new data and ideas since the model typically relies on predefined rules that may not cater to some use cases, especially those involving creating new data. Generative AI, on the other hand, can generate creative and novel outputs by learning from diverse datasets. It can also generate unique data and ideas that the developers may not have explicitly programmed or anticipated. [4]

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Final thoughts

Generative AI has set a precedence for a wide range of new AI applications that could not have been possible with traditional rule-based automation. By learning from training data rather than following predefined rules, Generative AI models are more adaptable and flexible and suit a bigger range of use cases than their rule-based counterparts.

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[1] World Intelligent Process Automation Market. URL:, Accessed May 24, 2023
[2] Rule-based System For Process Automation. URL: Accessed May 24, 2023
[3] What is generative AI? Everything you need to know. URL: Accessed May 24, 2023
[4] Generative AI: Imagining a Future of AI-Dominated Creativity. URL: Accessed May 24, 2023


Generative AI