BI has been around for a while. But, AI in the market has started to rule almost every sector. Will it take over or will they complement each other? Well, for that you need to understand the difference between AI vs BI and what they actually mean.
We are living in a technological era. Over the past two decades, technology has turned the world upside down. We are doing things today that nobody would have imagined a few years back and the credit goes to Artificial intelligence. But, how is it related to Business Intelligence? Does Business Intelligence need Artificial Intelligence? Let’s find out about AI vs BI.
AI vs BI are separate yet complementary concepts. “Intelligence” in BI refers to the more intelligent business decisions that may be made as a result of data analysis and visualization, whereas “intelligence” in AI refers to computer intelligence associated with making machines that are programmed to be capable of thinking and solving problems like the human brain. In data driven businesses, each has a role to play.
If you want to know in detail about AI vs BI, read ahead, you’ll find head to head comparison between artificial intelligence and Business Intelligence.
AI vs BI- Comparison
This table provides you with the differences between AI vs BI
|S.No.||FEATURES||Artificial Intelligence (AI)||Business Intelligence (BI)|
|1.||Idea||Artificial intelligence (AI) is based on the idea to replicate human intelligence in robots.||Business Intelligence is based on the idea of analyzing corporate performance through research and data collection.|
|2.||Aim||To design a system capable of thinking for itself just like humans do||To provide information that can enable efficient and effective business to comprehend the past and foresee the future.|
|4.||Tools||complex algorithms and Chatbots||Research, Data analysis tools, spreadsheets, and query software|
|5.||Application & Scope||Extensively used in robotics, virtual reality, augmented reality and has a scope for future||Extensively used in business operations, data analysis and betterment for the corporate world|
|6.||Research Areas||AI research areas include Systems of expertise and neural networks Logic fuzzy, robotics, and natural language processing||Research areas for BI include Social network data mining, process analytics and user queries, marketing|
|7.||Algorithm||Breadth-first search algorithm, Depth First Search Algorithm, Uniform Cost Search Algorithm, Iterative Deepening Depth-first Search,||Decision Tree Algorithm, Generalized Linear models, Apriori Algorithm, One class Support vector machine, Orthogonal Partitioning clustering|
|8.||Downside||Threat to Privacy, Threat to Human dignity, Threat to safety||improper technology and misuse of data.|
|9.||Analysis||Prescriptive analytics||descriptive analytics.|
|10.||Contribution||Contributes to biology and computer science mainly||Contributes to online analytical processing, enterprise reporting, and data analysis|
|11.||Integration With Example||AI-powered chatbot. As the chatbot gains knowledge from the queries of your users, it will be able to give clients that use live chat more individualized responses. On a larger scale, artificial intelligence (AI) could assist a fashion brand in analysing social media data to assist in predicting which hues, materials, and accessories are gaining or losing appeal in order to inform production choices for the upcoming buying season.||In data driven enterprises, data generated by your business, such as site visits, transactions, brand interaction, campaign analytics, and KPIs can gain insight into how business operations and marketing efforts can be optimized by gathering and using this information.|
Now that you have an idea about differences between AI vs BI, let’s have a detailed explanation so that there is no scope for confusion.
1. AI vs BI- Idea
The idea or concept for both AI vs BI is quite different. AI is based upon the idea to replicate human intelligence in robots, whereas BI is strictly based on analyzing corporate performance by research and data collection. The AI machines can perform human-like tasks and can also learn from past experiences like human beings. BI involves processing of data and then using the data for decision-making.
2. AI vs BI- Aim
AI started with the intention of designing a system capable of thinking for itself just like humans do, whereas BI’s aim is to provide data that will help effective businesses understand the past and anticipate the future for better marketing and profit.
3. AI vs BI- Resources
The debate about Artificial intelligence vs Business intelligence is going on for a long time basically come to a conclusion that AI is operational with machines. On the other hand, BI is mainly human based.
4. AI vs BI- Tools
Different tools are applied for both AI vs BI. Do you use Microsoft Excel to make spreadsheets? If so, you may not even be aware that you are employing business intelligence. With the use of these spreadsheets, business leaders can easily visualise and evaluate data. Chatbots and complex algorithms are the tools used for AI.
5. AI vs BI- Application & Scope
One could wonder that application and scope of both AI vs BI could be different, yet both complement each other in this case. While BI tools make it easier to analyze data, AI programs are made to let computers make decisions based on the data they receive. Chatbots are one of the best illustrations of how an AI computer may make decisions that resemble those of a human.
6. AI vs BI- Research Areas
Research is an essential element if you want something to work out. Without research, you cannot understand the logistics. AI research areas include Systems of expertise and neural networks Logic fuzzy, robotics, and natural language processing and BI research areas involve Social network data mining, process analytics and user queries, and marketing. This is another difference between Artificial intelligence vs Business intelligence.
7. AI vs BI- Algorithm
AI vs BI Algorithm takes user-generated queries and lets the algorithm pull from its source the best-suited information or data. Breadth-first search algorithm, Depth First Search Algorithm, Uniform Cost Search Algorithm, and Iterative Deepening Depth-first Search are examples of Artificial intelligence algorithms. On the other hand, Decision Tree Algorithm, Generalized Linear models, Apriori Algorithm, One class Support vector machine, and Orthogonal Partitioning clustering are Business algorithm’s examples.
8. AI vs BI- Drawbacks
No doubt, both Artificial intelligence and Business intelligence have a long way, but they have their own downsides. Threat to Privacy, Threat to Human dignity, Threat to safety could be the issues we’ll face with AI, whereas improper technology, misuse of data, and data breach are BI’s drawbacks.
9. AI vs BI- Contribution
In the field of contribution, Artificial intelligence mainly contributes in biology, computer science, Virtual reality and Augmented reality whereas Business intelligence contributes in online analytical processing, enterprise reporting, user behaviour and data analysis
10. AI vs BI- Integration With Example
Today, a lot of businesses have chatbot software on their websites. Without any assistance from a human, these AI-infused systems can respond to customer inquiries. Chatbots give businesses the opportunity to provide superior customer service when implemented properly. Artificial intelligence could help a fashion brand analyze social media data to help predict which colors, materials, and accessories are gaining or losing popularity in order to advise production decisions for the following buying season.
How AI And BI Can Work Together?
Even though AI and BI alone have a lot to offer on their own, when combined, they can produce even greater outcomes. BI with AI capabilities can go deeper into unsolved issues and uncover vital information in data that has not previously been studied. It is unfathomable how quickly BI and AI are developing on their own in their respective fields. Earlier, stand-alone BI apps could analyze historical data; but, when paired with AI, it can forecast future events by looking at the patterns in the past. Additionally, BI-based data management and analysis provide a wealth of useful resources for AI applications; by incorporating and leveraging such insights, firms may create predictive machine learning models and get quicker and more precise outcomes.
Here comes an end to our post about Artificial Intelligence vs Business intelligence. Although, it is not about choosing between BI and AI, rather, it is about how these two technologies may work in unison to improve corporate performance. Well, that’s all from our side. What are your views on AI vs BI? Let us know in the comments section below. Keep following Deasilex.