Difference Between Artificial Intelligence and Business Intelligence

Artificial Intelligence:
Artificial intelligence is the field of computer science associated with making machines that are programmed to be capable of thinking and solving problems like the human brain. These machines can perform human-like tasks and can also learn from past experiences like human beings. Artificial intelligence involves advanced algorithms and theories of computer science. It is used in robotics and gaming extensively. 

Business Intelligence:
Business intelligence is a set of technologies, procedures, and applications that help us to convert the raw data into meaningful information that can be used for decision making. It involves data analysis through statistical methods. It combines data mining, data warehousing techniques, and various tools to extract more data-driven information. It involves the processing of data and then using the data for decision-making. 

Below is the table of differences between Artificial Intelligence and Business Intelligence: 

S. No.	Factors	Artificial Intelligence	Business Intelligence
1.	Concept	Artificial intelligence involves humans like computer intelligence.	Business intelligence involves intelligent decision-making.
2.	Focus	It deals with the principles of statistical analysis.	It deals with machine learning and deep learning algorithms.
3.	Application	It is mainly used in robotics, image recognition, virtual gaming, fuzzy logic, etc.	It is used in data extraction and data warehousing techniques.
4.	Starts with	It begins with instructing systems to think and act like people, and it concludes with foresight into the future.	The process begins with collecting and analyzing data points from multiple data sources and concludes with visual dashboards and reports.
5.	Scope	Its scope is associated with events of the future.	Its scope is associated with what has happened in the past.
6.	Contributions	It contributes to the subjects like biology and computer science.	It contributes to OLAP, enterprise reporting and data analysis.
7.	Algorithm	It uses the BFS (Breadth First Search algorithm) and follows the FIFO principle.	It uses the linear aggression module for classifying data.
8.	Drawback	It has drawbacks such as a threat to privacy and safety.	It has drawbacks like improper technology and misuse of data.
9.	Intention	The main intention of Artificial intelligence is to develop machines that are capable of working like the human brain.	The main intention of business intelligence is analyzing data and predicting the future from the past data.
10.	Tools	It uses complex algorithms to make logic.	It uses spreadsheets, query software, and data mining tools for analysis.
11.	Research Areas	The following are some examples of Artificial Intelligence (AI) research areas: Expert systems Neural networks Natural language processing Fuzzy logic Robotics	The following are some examples of Business Intelligence research areas: Data mining in social networks Process analytics Bigdata Online Analytical Processing(OLAP)
12.	Algorithms	The following are some examples of Artificial Intelligence (AI) Algorithms: Breadth-first search algorithm Depth First Search Algorithm Uniform Cost Search Algorithm Travelling Salesman Problem Iterative Deepening Depth-first Search and others	The following are some examples of Business Intelligence Algorithms: K-Means Algorithm Naive Bayes Apriori Algorithm Decision Tree Algorithm Generalized Linear models and others
13.	Type of analysis	Prescriptive analytics relies heavily on Artificial Intelligence (AI).	Business Intelligence (BI) can help with descriptive analytics.
14.	Usefulness	It lets organizations estimate and predict client demand, competitive positioning, and economic trends and builds human-like intelligence in machines.	It examines historical data and lets companies to make better data-driven decisions to enhance operational efficiency, customer satisfaction, and staff happiness.

	Artificial Intelligence	Business Intelligence
Focus	Imitating human cognition and decision-making	Analyzing business data to inform decision-making
Data Input	Can handle unstructured and semi-structured data	Typically requires structured data in a data warehouse or data mart
Outputs	Predictive analytics, decision-making, automation	Dashboards, reports, data visualizations
Techniques	Machine learning, deep learning, natural language processing	Data mining, data warehousing, data modeling
Goal	Automate tasks, improve accuracy and efficiency, provide new insights	Optimize business processes, improve performance, identify trends and patterns