1. Machine learning in the company – benefits of implementing automation in the enterprise
2. How the machine learning algorithm works in practice
3. …what other important areas does machine learning cover in your company?
4. What challenges does machine learning bring?
Is the implementation of automation in companies profitable? Machine learning will improve many processes? Well… yes and no? When yes and when not? Machine learning in the company is a challenge that will soon be necessary for the development of companies. First of all, let’s start with the fact that the basic factor that would determine the level of amortization of the costs incurred for investments in artificial intelligence (AI for short) is the size of our company. This factor should include such sub-factors as:
- the number of people required for full-time employment (and thus also health or social security contributions);
- the number of breakneck and time-consuming operations performed by humans – colloquially speaking – on foot, which would require improvement;
- type of enterprise;
- costs that would have to be allocated for a possible transformation of the company’s infrastructure.
The above subfactors are unquestionably interrelated. It is them that guide the company’s management when considering implementing some kind of costly improvement for their business. Machine learning in the company does not necessarily have to be necessary and profitable. Let’s consider everything individually, depending on the needs.
What are machine learning algorithms for anyway? First of all, we use them to recognize all dependencies and patterns. We will also analyze the individual possible return on implementation costs. Let’s call the process of implementing automation a kind of pilot in our company. Professional external companies that provide software implementation services for various types of activities conclude contracts with entrepreneurs. They clearly specify the rules of cooperation and provide pilot activities and possible support (technical support). The areas in which entrepreneurs most often undertake the implementation of automation are:
dynamic marketing – in this field, we mainly focus on collecting data about potential customers and efficiently analyzing their needs. All this so that we can produce and match a product that is adequate for them. We carry out these operations based on automatic online interactions with the modern consumer. Of course, this customer information is initially disordered and chaotic. An employee would not be able to track and process such a large database of potential consumers. The implementation of special software in this area is an investment that will certainly bring long-term profits for companies offering both services and products. Thanks to the improvements, marketing specialists will process the generated data and use it, for example, to send personalized marketing newsletters. Such collective actions can bring new customers to the company.
- Broadly understood planning of all components of a given enterprise. It has its English name Enterprise Resource Planning, which in free translation means “enterprise resource planning”. If we implement automation in this area, we usually use the ERP abbreviation. In this way, we define complex IT systems that are to improve the management of breakneck activities in our company. Here we have in mind such areas as: planning work in human teams, developing the distribution of material and financial resources. Thanks to ERP software, we are able to control (with simultaneous reduction of employees) the organization and collection of data, as well as the performance of various activities on them. Thanks to this type of software, we can also efficiently cooperate with other enterprises. Thanks to the implementation of ERP in our company, we can also successfully forecast statistics regarding the number of product sales. We also monitor customer opinions on an ongoing basis and can efficiently prepare reports on current market trends in our industry. In addition, through machine learning algorithms, we can successfully find the best schemes and solutions for us. At a later stage of operation, we can also optimize processes in devices in the network and improve repetitive and risky activities in terms of error.
- In the area of supply chains in smart factories – here we are increasingly reaching for loT devices and systems. It is an abbreviation of the English phrase Internet of Things, which we use interchangeably with Intelligence of Things. In simple terms, let’s explain it as cooperating electronic devices, thanks to which we can efficiently exchange data and converse. All, of course, using the virtual world and without human involvement. This concept also includes smart tools that fall within the definition of a smart home. The most common virtual space that we use for communication in the company is the popular “cloud”. The “cloud” is a network of servers and each employee of the company who has access to it can upload documents, files, videos or photos to it. This data is available to other users. Machine learning in the company, as you can see, is a very broad concept.
The implementation of automation is usually a good solution today in many fields. However, it sometimes turns out that in some areas it is difficult to replace a human (at least at this stage of AI development). The negative connotation of this appears, for example, in data analysis and too meticulous “finding” of cause and effect relationships by artificial intelligence. A tangible example of this is a graph that shows a strong relationship between margarine consumption and the number of divorces in one US state. The results of this humorous research can be found in the book Spurious Correlations by Tyler Vigan. Using the aforementioned graph, the author proves that machine learning is still quite biased, biased and prone to irregularities.
Unfortunately, all shortcomings (of both people and algorithms) are “assimilated” by machines. Contradictory dependencies, on the other hand, can propagate throughout the neural network. The challenge of machine learning is also its model, in which the results of the algorithm are impossible to break down into prime parts by a human. So we will remain with a question mark and uncertainty as to why a particular algorithm came to a particular conclusion. Fortunately, we already have more and more crisis management tools at our disposal. In addition, in order to minimize the risk of the problem described above, as a company, we can obtain successively updated guidelines. They are to concern taking care of AI technology, as well as drawing up reliable and proven protocols.