An introduction to Artificial Intelligence for Business

Artificial Intelligence for Business

Artificial Intelligence (AI) has come a long way since Alan Turing first explored the concept in the early 1950’s. What started as a mathematician’s intuition about computing machinery has evolved into a key asset for modern businesses.

What is Artificial Intelligence?

The Britannica encyclopedia defines Artificial Intelligence as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings”. It also does it much more efficiently and quickly than Human cognition. 

Three technological trends can explain the spectacular rise of Artificial Intelligence for Business in the last decade:

  • The generalization of high-speed Internet and the development of sensor technologies allowed an exponential increase in data capture.
  • Increasing computing power and storage capabilities make it quicker and cheaper to store and process all the collected data.
  • The advancement of algorithmic techniques made it possible to analyze these volumes of data.

In a nutshell, we collect more data, have more room to store it and the right technologies to analyze it. Among these technologies, let’s focus on Machine Learning, as it is the most used in business today.

Nowadays, Artificial Intelligence for Business is mainly using Machine learning techniques to create value. (Very) basically, it combines a data set and an algorithm. The algorithm will analyze the data and propose an interpretation by identifying patterns and explanations. When doing so, it mimics the Human brain: think about the last time you were analyzing an Excel spreadsheet to solve a business issue!

Four types of Machine Learning

Supervised learning 

It is the simplest to apprehend. The algorithm trains on data properly labeled by a human being, with an outcome identified as the target – basically, a column to predict. It will then find correlations and patterns within the data to explain the target outcome. The process repeats until the results are considered accurate enough. They are then applied to new data of the same format to anticipate the target outcome.

Churn prediction is a good example of supervised learning. By analyzing data where past churners and non-churners are duly labeled, algorithms can find the reasons why customers churned. They can find common patterns and identify potential future churners.

Unsupervised learning

Here, the algorithm will explore unlabeled data with no target outcome and regroup it according to similar patterns. For instance, B2C Customer segmentation uses Unsupervised learning algorithms on CRM data to identify groups of customers.

Semi-supervised learning

Here, the algorithm trains first on a small set of labeled data. It then analyzes unlabeled data to improve the first results and gets better predictions. Image analysis often relies on semi-supervised learning. The algorithm will learn from a small set of labeled pictures, say dogs, and then will analyze large sets of unlabeled images, improving its prediction accuracy during the process.

Reinforcement learning 

These algorithms learn how to perform a task by analyzing data and getting rewards for the outcomes they propose. The higher the reward, the better the outcome is. For instance, the Netflix algorithm suggests shows to the users and gets rewards according to the time the viewer spends in front of it. The longer it is, the more the algorithm will suggest similar shows.

The next steps of AI for business

The explosion of data and storage capabilities available fueled the rise of Artificial Intelligence for business. However, most of the business experts don’t use Big Data directly. What they do use on a daily basis are Small Data sets to get feedback and guide their decisions.

Now Enterprise AI needs to go further and be available at every step of the company. Decision Intelligence Platforms designed for Small Data will play a major role in this evolution. Modern algorithms such as MyDataModels’ proprietary algorithm can perform Supervised learning on 300-row, 15-column datasets. When integrated to Decision Intelligence platforms, they can provide fast actionable insights to Business experts to guide their decision.

Since Business experts need to make fast, transparent and explainable decisions, Artificial Intelligence algorithms and the insights they provide must be transparent and explainable too. Our Decision Intelligence Platform, powered by our AI engine, answers these issues. Want to know more? Feel free to reach!

Share
Share on linkedin
Share on twitter
Share on facebook

Start making sense of  your data

Test easily Quaartz with our test data here

You might also like...

An Introduction to Predictive Analytics

Explainable AI for Small Data

Explainable AI for Small Data: How it Works

Explainable AI

How Small Data Impacts Explainable AI

MyDataModels joins the French Tech DeepNum20 program