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)
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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
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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. |