In the last decade, artificial intelligence has made huge advancements and has integrated into almost every field, especially businesses and marketing. One of the major uses of AI and machine learning is sales forecasting. With the ability to process so much information at an incredibly rapid pace, it has become extremely beneficial for companies to turn towards the AI rather than traditional sales forecasting simulations.


Most people get confused between artificial intelligence and machine learning, as they may carry out similar tasks, however, there’s a clear difference between these two technologies, and we’ll help break it down for you.
First, it’s safe to say that the terms “Artificial Intelligence” and “Machine Learning” are often used interchangeably because both describe the use of software and hardware that enable a machine to be “intelligent.” However, the difference is that “Artificial Intelligence” is a broader term for providing machines with the ability to perform rational tasks, while “Machine Learning” is a subset of AI that encompasses the use of data for the machine to learn.


In general, AI is a concept where it is possible for a machine to “think” or react like humans. AI, Neural Networks, can learn by examples to execute arbitrary tasks. For instance, an AI ‘CRM’ solution can learn to respond to emails like humans. In essence, they can generate by teaching examples and very complex rules that humans follow.
Some people believe that just by stacking deeper and deeper artificial neural network, we will get a self-aware AI, and they call that hypothetical event ‘The Singularity’. However, that is highly doubted by industry professionals.

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Machine learning is a bit more specific, as its main purpose is to read and learn the statistics and algorithms and learn from the historical data. In a nutshell, Machine Learning is taught to recognize patterns and make decisions based on statistical information. 


While machine learning is taught to gather data and learn it AI is mainly focused on applying the data. AI’s main purpose is to increase the successes it has in any objective rather than machine learning, which aims to increases the accuracy. In essence, AI aims to stimulate natural intelligence in the computers however the goal for machine learning is to maximize the performance of the machine by learning new things from the data. 
Sales forecasting that uses machine learning techniques, however, draws data from all historical sales forecasts and creates a model that shows a typical path for a successful sale, from start to close, and then compares it to current performance. Anomalies and an off-track forecast can be quickly detected in the data, which gives sales leaders the opportunity to step in and redirect ecommerce sales.
Utilizing Machine Learning technology such as Curve helps your team make more accurate sales and inventory decisions. All of these enhanced activities improve overall sales effectiveness and drive growth in an organization. At Curve, our mission is to help sales teams improve your sales outlook and drive growth across the organization.