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Difference Between Artificial Intelligence and Business Intelligence

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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.FactorsArtificial IntelligenceBusiness Intelligence
1.ConceptArtificial intelligence involves humans like computer intelligence.Business intelligence involves intelligent decision-making.
2.FocusIt deals with the principles of statistical analysis.It deals with machine learning and deep learning algorithms.
3.ApplicationIt 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.ScopeIts scope is associated with events of the future.Its scope is associated with what has happened in the past.
6.ContributionsIt contributes to the subjects like biology and computer science.It contributes to OLAP, enterprise reporting and data analysis.
7.AlgorithmIt uses the BFS (Breadth First Search algorithm) and follows the FIFO principle.It uses the linear aggression module for classifying data.
8.DrawbackIt has drawbacks such as a threat to privacy and safety.It has drawbacks like improper technology and misuse of data.
9.IntentionThe 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.ToolsIt 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) 

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 analysisPrescriptive analytics relies heavily on Artificial Intelligence (AI).Business Intelligence (BI) can help with descriptive analytics.
14.UsefulnessIt 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
Last Updated : 03 May, 2023
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