«  View More Posts

Machine Learning vs AI in Pricing Software – What’s the Difference?

June 8th, 2022 (Updated 03/10/2023) | 7 min. read

By Sylvain Rougemaille

Congrats! You have made the big decision to switch over to the automated solution of pricing software from your outed Excel spreadsheet pricing set-up. But now, the choices for which pricing software you want are becoming blurry and confusing. On one hand, from one work colleague you have heard that pricing software powered by Artificial intelligence (AI) is great, but another of your colleagues in the pricing team has read that Machine Learning (ML) powered pricing systems are best. But who is right and what is the difference between Machine Learning vs AI in pricing software anyway? That is what we are here to do in this article – clear up the myths and misconceptions and give you all the information you need to know on the topic of Machine Learning vs AI in pricing software. 

At Pricefx, we are helping business organizations to overcome pricing dilemmas like these daily and set up a recommendation for the pricing software solution that best suits each company’s unique set of business objectives. 

To help you understand the complex technology further, we have compiled this article to explain the differences in Machine Learning vs AI in pricing software and how, when they are used in combination, they can form a powerful tool to help your business grow.  

However, to kick things off, let’s consider drill down and define exactly what Machine Learning & AI both are, and how the terms apply in the pricing software environment. 

What is Machine Learning? – The Definition 


Many people tend to use terms like artificial intelligence and machine learning as synonymous and the difference between the two is often overlooked. However, these two terms are genuinely two different concepts even though machine learning is actually a part of AI.  

In broad terms outside of pricing software, Machine Learning can be defined as the pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions. 

In other words, Machine Learning is one of the building blocks for AI.

Machine Learning is basically the process which provides your system with the capability to learn automatically on its own through experiences and improve accordingly without being explicitly programmed to do so, or without any specific objective in mind.  

ML is an application or subset of AI focusing on the development of programs so that it can access data to use it for themselves. The major aim of ML is to allow a system to learn by themselves using statistical methods enabling machines to improve with experience without any kind of human intervention or assistance. 

How is Machine Learning Used in the Pricing Software Environment? 

Almost exclusively in the pricing software environment, Machine Learning is used to set up the predictive model that the AI will apply in building your price optimization model for your company’s unique set of products and required business outcomes. 

What is Artificial Intelligence? – The Definition 


Artificial intelligence is a computer system designed to think the way human intelligence does, and that is what separates it from machine learning.

In other words, AI does more than a machine learning type-repetitive task like verbally requesting Siri to play your favorite song on your phone. 


Informed by true artificial intelligence, Siri can actually analyze your favorite song’s genre or style and select another similar song that you will probably also like; and simultaneously provide further additional suggestions of a similar music style or suggest songs to add to your favorite playlists that other people with similar music tastes to you have in their playlists!

So, taking that into account, Artificial Intelligence is defined as a method of creating computer systems,  that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference. 

Worldwide, companies are incorporating AI techniques such as computer vision natural language processing — the ability for computers to interpret images and use human language ­— to automate tasks, enhance realistic customer chatbot conversations and experiences, and accelerate decision making (including in the pricing industry). 

How Does Artificial Intelligence Work in Pricing Software? 

AI when applied in pricing science allows your business to turn its data, such as sales and transactional data, product data and customer data into the intelligence to make informed pricing decisions to progress your company towards your business objectives. Machine Learning cannot do that. 

AI solves tasks that are like human intelligence while ML is only able to solve specific tasks by being instructed to learn from structured datasets and make limited predictions accordingly.  

In other words, ML is limited by requiring clear instructions from its programmer, while true artificial intelligence can dissect data, make decisions, and learn.

What that means is biggest difference for AI is that the latest and most sophisticated AI pricing software is more powerful and allows you to do more pricing functions; 

  • With more complex use cases, solving a broader set of problems considering more complex circumstances 
  • More accurately and intuitively 
  • In a less supervised or pre-determined environment. 
  • Across a broader set of objectives and constraints 

Using AI-informed pricing decisions, companies can set their pricing software to accurately take concrete actions and make more accurate margin leakage risk predictions and set prices more precisely.  

Want to set a price to raise your sales volume on one of your items, while increasing profit margin on another? Sure, no problem and it is AI that makes it possible and makes it happen. 

AI is all about optimizing pricing – and it can be done flexibly – in whatever way you see fit for your organization’s pricing purposes – and with more insight than you can practically do on your own by manually manipulating your Excel spreadsheets. 

You can use AI to assist you to better segment customers in different geographies, segment, or group related products in the same price pool for optimization, assist in constructing better quotes and overall offers and prices, and initiate intuitive customer service responses that are more appealing to your customers. 

How AI Can Be Used in Pricing Software – an Example Case 


Imagine you own a tire company, and you sell three types of tires: high performance tires for sports cars, tires for tractors, and winter tires for most standard cars. 

Having determined your pricing strategy for each of your three products, you have decided that you want to: 

  • Increase Sales volume on your high performance sportscar tires to lower production costs
  • Have a standard 20% profit margin on your tractor tires
  • Have a standard 15% profit margin on your winter tires and;
  • You want to apply all these rules in every market you sell in except for Estonia, where a standard 25% profit margin applies across all your tires. 

With the AI of pricing software, these four business outcomes can be built into a single rule for your pricing software system to calculate your prices worldwide automatically. 

Machine Learning vs AI in Pricing Software– What’s the Difference? – A Table Snapshot 


Adopting AI to Stay Pricing Competitive 

Now you know the differences between Machine Learning and AI, you’re probably looking to learn more about AI-informed pricing software like the Pricefx pricing solution.  

Click on the image below for a simple explanation and to learn more about how AI works in pricing software: 



Sylvain Rougemaille

Senior Product Manager , Pricefx

Sylvain Rougemaille PhD is Senior Product Manager at Pricefx based in France. He has 15 years of experience in the IT industry and AI. He obtained his PhD on Software Engineering applied to AI in 2008. Since then, he has participated the creation of two startups aiming at the diffusion of AI to solve complex industrial problems as aircraft optimization, genomic simulation, and ultimately price optimization. In 2015 he co-founded Brennus Analytics where he occupied the position of Chief Product Officer. The purpose of it was to bring the PO&M software market unrivalled optimization capabilities thanks to Multi-Agents’ AI. Since 2020 and its acquisition by Pricefx he is pushing pricing science even further as the Price Optimization and Science Manager.