There is confusion in the minds of technologists around artificial intelligence (AI) and machine learning (ML). This confusion is because most practitioners come from the experience of engineering and not data science. In this article, we focus on understanding what it means to run an AI-based ECommerce in real sense.

What is AI (Artificial Intelligence)?

So artificial intelligence systems invariably use rules to drive intelligent decision making. However, what differentiates a super performing AI from others is who defines these rules. If the users set the rules to build intelligence, then such systems are still artificial intelligence systems, albeit with a capability to produce intelligence in limited scenarios. With ever-increasing volumes of data and the need to build intelligence in real-time, the number of situations, and the complexity of creating intelligence has gone up by magnitude. Setting up an AI system with a fixed set of business data parameters isn’t sufficient. The AI systems need to take into consideration almost all the parameters for their correlation to the final intelligence outcome. 

Therefore there is a need for artificial intelligence systems to learn automatically as and when they are supplied with new data points. Hereafter, when we refer to AI systems in this text, we mean the learning AI systems and not the rule-based methods. AI systems need machine learning as the non-negotiable backend that can process several business data parameters and learn from the data newer insights. 

Why Learning AI in ECommerce?

Amount of data generated by the ECommerce company is only likely to increase as the variation in the data. With the evolving business scenario of ECommerce, ECommerce marketers running with fixed rule-based intelligence can not be competitive enough in the fierce marketplace. Too many moving data parameters now participate in the decision-making process. For example, predicting the price for particular merchandise not only depends upon the demand/ supply, but also depends upon the sales in the past, the propensity of the users to buy, and other external parameters such as time of the year, season. 

It is challenging to maintain a rule-based system with a fixed statistical model to live up to the expectation of the highest quality intelligence that is needed from these systems.

Evolve with Intelligence

Using learning-based artificial intelligence systems help the ECommerce companies to evolve as the business generated more data and more intelligence. Growing intelligence is the virtuous cycle of artificial intelligence. More data (volume and variety) leads to better knowledge with learning AI, which leads to an improved business that results in an evolved business model and more data.  

Reduced Management Overhead

AI systems that take the learning approaches tend to become self-sustained over some time. They need the least supervision and maintaining. The machine learning models that the AI systems use are self-learning and evolving in nature. Use of ML base AI helps the data scientists to focus more on the business aspects of the data than the statistical modeling. 

Adopting AI is not a tick in the box activity, but it is an ongoing concern. The real value of AI is when you can experiment with your data consistently. The AI systems aren’t supposed to be constrained or stop behaving intelligently in newer business scenarios. Integrating AI that is powered by machine learning helps you achieve a practically unbeatable competitive advantage.

Datoin is an AI platform powered by ML for ECommerce. Experiment with your data today with Datoin.