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In the Age of AutoML, we are all Data Scientists! | Artificial Intelligence (AI) has become increasingly essential to our daily routine lives.

Business Intelligence and Artificial Intelligence

Published on May 17, 2021 by Christelle Julias


As always, technological progress moves at the speed of sound. What was yesterday, is easily outdated today. In order to remain competitive, companies need to keep up with the next trends. One such trend that has revolutionized the business world is undoubtedly artificial intelligence. Some companies are still reluctant to adopt this cutting-edge technology. But by staying that way, they run the risk of getting stuck in stagnation.

As an analytics tool, companies use Business Intelligence, a combination of methods and domain expertise to define real-world problems. There is a little confusion between Artificial intelligence and Business Intelligence but instead of clarifying this any further, let us simply talk about how it can be synergized in the form of Decision Intelligence. What is it and how can it be leveraged?

If you are among those who are still not convinced by this powerful combination of analytics and automation, this article will try to give you some hints to help you rethink your opinion on the subject.

Without further ado, I invite you to read on.


To begin with, let's outline the various challenges that modern businesses face, the type of problems that need to be solved, and the need to adopt a superior data-driven tool; while at the same time explaining how AI could be the ultimate solution to each business use case where critical choices are required. That said, we will finally conclude on the need to implement Decision Intelligence as an investment that pays off in the short and long term.

The challenges of modern enterprises and how DI solves them

Modern enterprises are confronted with many issues comprising organizational, product outcomes, optimization of resources, and customer interactions.

Many are the sectors that can benefit from Decision Intelligence, just to cite:

  • telecommunications
  • industry and automation 
  • applied science and medicine 
  • mechanics
  • mechanization
  • robotics
  • IoT

What they have in common is that they need to process data and a combination of facts to get to a conclusion while these are changing according to different parameters. Those are familiar domains for BI. But what if we need to forecast what happens next?

SWOT analysis, pattern detection, CRM, and recommender systems

For ever-changing data, AI handles predicting the next trends well. Let's see how.

People's tastes and culture are easily influenced by what's new, and often their buying behavior is largely governed by what their neighbors and peers are doing. Thus, knowing these tendencies beforehand will help adjust the shot.

The main difference between predictive analytics and machine learning (AI) is that AI is scalable while predictive analytics is somewhat fixed. Why? In ML, the algorithms are fed with data and discover by themselves the set of rules that govern a situation. As such, it is able to adapt the model based on past errors. As a result, it is able to improve future performance, whereas, in predictive analytics, the model ingests historical data and provides projections based only on past events, but is not able to understand if new information appears.

Let's take the example of the advent of Tick Tock. Once launched, it has boosted the sales of other related commercial products: phones with HD cameras and videos were at the apex of their quest for quality. Also, flagship phones who simultaneously arrived on the market but did not take that into consideration were left behind.


In parallel, those who seized the opportunity to jump on the bandwagon succeeded to convey in a 15-second video the essentials of their brand without ruining themselves on ads.

Through Opinion - mining AI digs into the reviews and conjectures the different emotions expressed within comments be it satisfaction, anger or even, indifference. With data channels such as social media conveying new information quickly, contemporary firms assign their board decision-making through pattern recognition.

This does not mean that this is only mainstream. There are exceptions that can but confirm the rule.

Pattern recognition

The closest example at hand could also be bitcoin, whoever has perceived its explosive price would have bet on the best trading and avoid pitfalls. The others are biting their fingers off. 

But instead of appointing luck, we have to recognize that those who had AI were one step ahead. Also knowing both “WHEN”  and “WHERE” to invest is the main point that cuts the whole deal.

Finally, studying customer lifetime value is better tackled with DI than with BI or DI only. With DI you will be able to estimate how much money a customer will pay you during their relationship with you. This is especially valuable for the best marketing campaigns.

The benefits that companies gain from adopting DI i.e BI + AI

Companies gain many advantages from using DI. One of those is to target the future actions to take by deriving descriptive analytics from BI and generating prescriptive analytics thanks to AI.

Startups, Factories, and Health sector

This is especially true for startups and industries producing and exporting at scale, and Health organizations. What I allude to are waste management and inventory, and identification of illnesses. In fact, being able to handle these effectively will be a pain point to companies that do not adopt DI to mitigate the margins in comparison to those who are backed up with well-armed tools.

Other of those are pipeline leaks and failure detection in factories’ machinery. This is especially useful for hydraulic companies. Studies show that repairs are more expensive than prevention, which is why it is useful to plan for them before the troubles occur.

BI and AI synergize to give DI

In other words, BI provides the state of things and uncovers the statistical distribution of data, while AI can learn to discern a typical tendency in the way they are organized.

Using this logic, it can for instance be used for Health and Drug prescription.  Patients can hope for quicker recovery thanks to more and more accuracy in the treatment of diseases. BI will display the data structures in databases whereas AI will look for similar occurrences according to the correct identification of symptoms.


We have thus seen that BI is inevitable if we want to stay in the race, otherwise we can easily be overtaken by competitors. As AI and BI are not mutually exclusive, they can even be synergized to produce DI or decision-intelligence, an advanced method for targeting at the right aim. While BI is purely analytical since AI relies on data science and machine learning at its core, it is able to expand its knowledge and evolves as it ingests new data each day also the two become one extremely efficient data analytics tool.

It is a matter of life and death

Clearly, to be truly smart, companies need to implement some form of DI into their decision-making processes in order to remain AGILE, vigilant, and avoid reckless money losses. 

In other words, solid technology intelligence is a matter of life and death. As Artificial Intelligence and Business Intelligence are not mutually exclusive, given the number of applications relying on AI, it would be a bit controversial to exclude them from your next analytical tool. 

The forecasting engine of SmartPredict, an automated AI platform

Also, with the DI platform SmartPredict,  there is a special forecasting engine to support marketing teams in their decision-making. It includes different kinds of operations for digging information ranging from data profiling to chart generation. It also makes use of powerful machine learning models such as Prophet and GluonTS to enable you to quickly deduce accurate future estimates simply by feeding the model with historical data and updates. And you can do that without coding or advanced DI knowledge.