Definition
An expert system is a computer program that leverages artificial intelligence (AI) technologies to replicate the judgment and behavior of a human or an organization that has experience and a good knowledge of a particular field [1].
An expert system is just like a database of human expertise or knowledge. The data in the knowledge base is added by humans that are experts in a particular domain. The goal of expert systems is to complement, not replace, human experts. They try to behave like a human expert on a particular subject area. A non-expert user leverage on expert system to get information.
For example, an expert system is like a scenario where we have Madam Ngozi, FIRS's most knowledgeable tax expert with 30 years of experience, available 24/7 to answer taxpayers' questions. When business owners have tax queries, Madam Ngozi asks clarifying questions ("What type of business do you operate?" "What was your annual revenue?" "Do you have employees?"), then applies her extensive knowledge of tax laws, VAT regulations, and corporate tax codes to provide accurate advice.
An expert system captures this expertise in
software—programmed with tax rules like "IF annual revenue exceeds ₦25
million THEN register for VAT" or "IF company has remote workers THEN
withholding tax applies differently." This allows small business owners in
Aba or Kano to get expert tax guidance without traveling to Lagos or paying
expensive consultants, and ensures consistent, accurate tax advice based on
codified expertise from senior FIRS officials.
Origin
In 1965, Edward
Feigenbaum and Joshua Lederberg of Stanford University in California developed
the first expert system known as U.S. Dendral. It was designed to analyze
chemical compounds. Expert systems now have commercial applications in diverse fields
such as medical diagnosis, petroleum engineering, and financial investing [3].
Context and
Usage
In today’s
AI-powered world, expert systems can be applied in domains such as the
following:
- Finance Domain: It helps financial sector to detect potential frauds and suspicious conduct, as well as guiding bankers on whether or not to offer business loans.
- Knowledge Domain: People who are non-experts use them to get information on particular domains. For example, tax advice.
- Manufacturing & Designing Domain: It has a wide range of applications in designing and manufacturing tangible objects, including producing and designing camera lenses and automobiles [4].
Why it Matters
AI has accelerated
the development of smart technologies, helping systems to make more informed
decisions in dynamic environments. The global AI market is projected to reach
$1,811.75 billion by 2030, a significant increase from its $279.22 billion
market size in 2024. Expert systems are one of the most effective applications
of AI. Replicating human reasoning, this hardware and software make AI useful
for organizations. They precisely evaluate data and draw intelligent
conclusions like a human expert but at a much greater scale and speed [5].
Related AI
System Types
- Emotion AI: AI systems that detect, interpret, and respond to human emotions from various inputs.
- Expert System: AI program that mimics human expert decision-making in specific domains.
- Generative AI (GenAI): AI systems that create new content including text, images, audio, or video.
- Multimodal AI: Systems capable of processing and integrating multiple data types simultaneously.
In Practice
A real-life case
study of expert systems can be seen in the case of MYCIN. MYCIN is a bacteria
identification system that uses backward chaining to identify infections and make
recommendation of drugs based on patient weight.
References
- Lutkevich, B. (2024). Expert System.
- Great Learning Editorial Team. (2025). What are Expert Systems in Artificial Intelligence?
- Zwass, V. (n.d). Expert System
- Herovired. (2025). Expert System in Artificial Intelligence.
- Coursera Staff. (2025). Expert Systems in AI: Your Partner to Transcend Problems.