Today, the collection and storage of user data by different services is not a grave problem. As computer memory constantly gets cheaper, storage is expanding. In light of this over the past years, most big companies have gathered enough data to conduct an analysis and apply it to solve all kinds of business problems. Machine and deep learning technologies have demonstrated their excellence over humankind. As never before they are a good fit to refine multiplex business processes which can include quite a big number of variables.

Any modern technology has to solve a specific task assigned within a business project. In order to fully describe all possible applications of the machine and deep learning, we’ll need to write a separate article. For now, let’s just focus on several areas: forecast analytics (scoring and customer outflow, defining goods’ shortage, recommendation systems, etc.), text analysis (reviews, topic emphasis, automatic moderation of content and more), speech analysis (text analysis based on videos, etc.) and computer visions (automatic picture analysis of goods, analysis of most interesting isles for clients based on video records and much more). All aforementioned tasks can be found in almost any business project which gives a certain level of liberty for application, deployment, and optimization of machine learning in business in general.

It’s important to clearly understand the apparent boundaries of artificial intelligence at this very moment and properly apply any given approach. After all the profitability of your business can depend on it. Many people make a wrong assumption about machine learning thinking of it as a big black box where you can throw any data and get a guaranteed right answer. Despite that opinion, any mathematical model works based on clear rules and can be analyzed.

For example, the work of forest solutions is easy for human interpretation as well as their more complicated mathematical model random forest that can be easily comprehended by humans too.

An example of human interpretation of how simple forest solutions work

Based on everything said before we can draw the following conclusion — application of any mathematical model has to be justified because in the end you’ll get proximal categories as the entire structure of artificial intelligence is tied to the theory of probability and stats. This means if there is a critical deficiency of data or we’re talking about a previously solved problem, you shouldn’t resort to machine learning methods. Otherwise, the results may be very sketchy and your expenses will fall short. For example, when we’re talking about the transportation of goods. Why not use the simplex method along with its modifications? This way you will get a more precise solution that when using machine learning as almost in all cases it shows an approximate answer instead of a sought one. The use of machine learning algorithms is justified in cases when there is no optimal solution and a human will fail to do quality work and most importantly fast.

The integration of any AI solution in business does not require multiple steps. In fact, with enough data, any company can use it. For successful integration, it’s important to define which business problems are going to improve and which metrics are going to be used to measure the results.

Implementation regulations of machine learning integration in a business project with the use of already built solutions

Let’s take a look at several examples of real problems

A business focused on selling certain goods decided to expand its product line and sell mobile phones. They had to face a lot of questions that could impact their ability to compete against their rivals, selling the same product. Based on the aforementioned step you need data to conduct an analysis. But as we are just opening, we don’t have any data to work with. What should be done in this situation is not yet unclear. But there is a solution: you can gather data from all available websites from several regions using scraps, prepare the knowledge and analyze it with a logic regression (or any other regression model depending on available data). As a result, we get a mathematical model that can set the necessary prices. This way we’re liable to all market specifications and can compete with other businesses. Another example from our experience involves shipment. Let’s say there is a company that ships furniture and is up to speed with necessary data. Such a company has a big expensive workforce that performs only a few important duties — order placement and classification of good and bad orders. In this case, it’s possible to mobilize company data over the past few years, prepare the knowledge and educate several mathematical models of machine learning to classify further orders. Thus, this company can downsize the number of workers to several people who will simply refer to the help of an intellectual assistant. During the first month of this implementation, the company saved significant sums of money.

Wrapping it up

From all the aforesaid we can draw a conclusion that the transfer of business tasks to artificial intelligence is an inherent part for big companies that are interesting in smart financial management to keep their profits. One of such companies is Starbucks. They stopped just brewing coffee and selling donuts a long time ago. Instead, they skillfully analyze all available information about their coffee shops, product range, trends and other things that bring them profit. This is an excellent lesson for many companies on how to intelligently connect business and technologies.