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Have you ever wondered why the sight of black cobra doesn’t scare an infant or a two-year-old baby, but it does scare a child of age 10 or an adult? The answer lies in the adrenalin hormone, secreted by a human body.

We human beings are trained for certain stimuli but if we encounter something new, we may not be able to respond well to the situation. In technology, AI is meant to think like humans by giving it the required amount of training. This training can be given by exposing the AI to a certain amount of data.

To learn business intelligence in detail and understand the role of BI technology in AI, it is very important to understand the exactness of BI and AI and the difference between the two.

Business Intelligence Role in AI

Underlining difference between Business Intelligence and Artificial intelligence

The base function of AI is to think and act like humans whereas the base function of BI is data analysis. The role of AI lies in decision making, whereas the role of BI lies in preparing the reports. The key role of BI is to collect the information, which the organizations can use to make optimized decisions. AI on the other hand uses the organization’s own information along with the information of the industry as a whole, to help the organizations to make the best possible moves.

For example, a simple tool that can convert handwritten text into typed text first uses the general information provided to it. This tool works using AI. Initially, this AI is exposed to a certain amount of handwritten text and it converts it into the typed text. Once the output is there, it requires some manual corrections to train the AI. Next time with a similar input, the output comes out in a better way. For the purpose of decision making, BI provides firsthand information to the AI to work, and later AI is trained to perform in a better way.

To understand the role of BI in AI, let us look at some examples of AI solutions

DOMO is a business management software company. This company has created a cloud-based dashboard that receives data from different apps using approximately 400 software connectors. This data is used for business intelligence.

This way, companies using DOMO can extract the data from different apps such as Salesforce, Shopify, etc. to gain insight into the customer’s sales or product inventory. The extracted data can also be used to spot the trends in the performance of the product. Later, a new feature known as Mr. Robot was added to the dashboard of Domo. The purpose of Mr. Robot is to provide insights to make decisions. This way BI is used to fuel AI.

Complementary nature of BI and AI

 There are certain patterns in the data that humans and BI cannot detect easily, the much-detailed algorithms with a consistent focus on certain parameters can help in the detection of such patterns are helping the decision-makers in a much holistic way.

Considering that the AI is superior to BI in all ways will not be correct. If all the possible constraints are not introduced to the AI, then relying on AI could result in business loss. Similarly, BI can also be extremely useful for decision-makers if the problem is unique, because of the generic nature of AI, which means it is trained to solve general problems.

Despite all the plus points, there always remains a threat to privacy breaches and data security with AI. However, with BI two major issues are organization and its people and data with technology. By considering all the positives and negatives of two systems, it is quite clear that BI in AI helps the organizations to review the data of the past and helps with new features. This helps in saving a lot of time in which organizations spend on data analysis and decision making.

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Imarticus Learning is India’s leading professional education institute that offers training in Financial Services, Data Analytics & Technology. We’ve successfully transformed careers of over 35,000+ individuals globally through our Certification, Prodegree, and Post Graduate programs offered in association with leading and renowned global organisations in the Financial Services, Data Analytics & Technology domain.
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