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Artificial Intelligence and Business Intelligence: Differences and Comparisons

Differentiation Between AI & Business Intelligence

by digitalraghul

In this article, we are going to talk about  Artificial Intelligence and

Business intelligence Differences and Comparisons. Nowadays AI is becoming more Valuable and It has more scope for the future. Let’s get into the article.

Business intelligence is a technology that collects, stores, accesses, and analyses data to assist business users in making better decisions. Artificial intelligence, on the other hand, is a way to create a computer, a robot that is controlled by a computer, or software that thinks intelligently like humans. Artificial intelligence is founded on the study of how humans reason, learn, make decisions, and work to solve problems. The results of this study are then used as the foundation for creating intelligent software and systems.

Artificial Intelligence and Business Intelligence in Direct Contrast

Artificial Intelligence:

  • Philosophy – The goal of artificial intelligence (AI) is to replicate human intelligence in machines.
  • Goals –  to develop expert systems and incorporate machine intelligence
  • Areas that Contribute – A blend of science and technology, artificial intelligence is based on computer science, mathematics, psychology, and biology.
  • Application – Various industries, including gaming, natural language processing, expert system vision systems, speech and handwriting recognition, and intelligent robots, use artificial intelligence.
  • Research Areas –Robotics, fuzzy logic, neural networks, and expert systems are among the research topics for artificial intelligence.
  • Issues – Three problems with artificial intelligence exist. Threat to safety and Human Dignity.


Business Intelligence

  • Philosophy – It facilitates the analysis of corporate performance through data-driven insight, allowing one to comprehend the past and foresee the future.
  • Goals – At all company level decision-making levels, it should supply information that can facilitate efficient and successful business decisions
  • Areas that Contribute -It combines business analysis tools including OLAP (online analytical processing), ad hoc analytics, and enterprise reporting.
  • Application – Spreadsheets, reporting and querying tools, digital dashboards, data mining, data warehouses, and business activity monitoring all make use of it.
  • Research Areas – Business intelligence research includes OLAP, Big Data, Process Analytics, and Data Mining in Social Networks.
  • Issues – Issues with business intelligence are divided into two categories. They are data and organizational technology.


Comparative Table of Business Intelligence and Artificial Intelligence

Following is the comparison table between Artificial Intelligence and Business Intelligence.

Basis of Comparison Artificial Intelligence Business Intelligence
Philosophy The goal of artificial intelligence (AI) is to replicate human intelligence in robots. It helps in analyzing business performance through data-driven insight i.e understanding the past and predicting the future
Goals to develop expert systems and incorporate machine intelligence At all levels of the organization, it should deliver data that enables efficient and successful business decisions.
Areas that contribute Artificial Intelligence is a combination of science and technology based on computer science, maths, Biology, and Psychology It combines business analysis tools including OLAP, corporate reporting, and ad-hoc analytics (online analytical processing)
Applications Artificial Intelligence is used in various fields such as Gaming, Natural language processing, Expert systems, Vision systems, Speech recognition, Handwriting recognition, and Intelligent Robots. It is used in Spreadsheets, querying and reporting software, Digital dashboards, Data mining, Data warehouse, and Business activity monitoring.
Research Areas Expert systems, neural networks, natural language processing, fuzzy logic, and robotics are all fields of artificial intelligence research. Business intelligence research areas include big data, process analytics, social network data mining, and OLAP.
Issues Three problems with artificial intelligence exist. They are threats to safety, human dignity, and to privacy. problems related to business intelligence are divided into two categories. They are data, technology, people, and organizations.


Artificial intelligence versus business intelligence algorithms


The Business Intelligence and Artificial Intelligence algorithms are as explained below:

Artificial Intelligence Algorithms

Business Intelligence Algorithms

Algorithm for breadth-first searches

Beginning with the root node, it first investigates the neighbor nodes before moving on to the neighbors at the next level. It offers the quickest route to the answer and can be used with FIFO.

Decision Tree Algorithm

The predictive information is extracted in the form of rules that are intelligible by humans, and these rules may be if-then-else rules.

Depth First Search Algorithm

Utilizing the LIFO (Last in First Out) data structure, this procedure is carried out. Similar to breadth-first search, it builds nodes, but the only difference is in the order. It saves the nodes from root to leaf throughout each iteration and is unable to check for duplicate nodes.

Naive Bayes

Using the Bayes algorithm, which derives probability forecasts from the underlying evidence as shown in the data, it provides predictions.

Uniform Cost Search Algorithm

By raising the cost of the path to a node, this algorithm sorts data. The least expensive node is always expanded. If each transition costs the same amount, this search is the same as the breadth-first search. It investigates the route in the ascending order of expense.

Generalized Linear models,

 uses linear regression for continuous targets and logistic regression to classify binary targets. Both confidence constraints for predictions and bounds for prediction probabilities are supported.

Iterative Deepening Depth-first Search

When it reaches level 1, it restarts the depth-first search and moves on to level 2, where it repeats the process until it finds the answer.

Minimum Description Length

It is a model selection principle based on information theory. It makes the supposition that the best method to explain data is to offer it in the simplest, most concise manner possible.

Pure Heuristic Search

Nodes are expanded according to their Heuristic values. It generates two lists: an open list for newly produced but unexpanded nodes and a closed list for nodes that have previously been expanded. This discards the longer pathways while saving the shorter ones.

K-Means Algorithm

The program divides the data into a predetermined number of clusters using a distance-based approach. There is a centroid in each cluster.

Travelling Salesman Problem

The basic goal of this algorithm is to locate a cheap tour that departs from a city and stops in each city along the way exactly once, then returns to the original starting location.

Apriori Algorithm

By identifying co-occurring items within a set, it does market-based analysis. This algorithm looks for rules that have more support and confidence than minimal support and minimum confidence, respectively.

Hill-climbing search

is an iterative method that begins with a randomly chosen solution to a problem and seeks to improve upon it by gradually modifying each component of the solution. An incremental modification is regarded as a new solution if it results in a superior one. Until no more advancements are made, this process is repeated.

Support Vector Machine

Different iterations of SVM handle various kinds of data sets using various kernel functions. Both linear and non-linear Gaussian kernels can be used. With SVM classification, the target classes are separated by as much of a margin as possible. The goal of SVM regression is to identify a continuous function that has the most data points within an epsilon-wide tube surrounding it.

Other algorithms include Bidirectional search, Local beam search, A* Search, and Simulated annealing. BI uses/supports Orthogonal Partitioning Clustering, Non-negative Matrix Factorization, One Class Support Vector Machine, and Maximum Entropy.



Artificial intelligence is the focus of a new endeavor to create a computational model of intelligence. The fundamental assumption is that a digital computer may be set up to carry out symbolic operations and represent human intelligence in terms of symbol structures. Groups within an organization can employ meaningful insights from business data by using business intelligence to meet requirements. A Business-focused analysis is provided by business intelligence solutions at a size, complexity, and speed that is not feasible with simple operational systems reporting or spreadsheet analysis, adding significant value.

If You Wanna discover more about artificial Intelligence in an efficient way, kindly check this out!!!


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