What are neural networks?

Imagine that you need to develop a program that will automatically recognize cats in a photo. You can manually enter the commands "if there are ears, then it's a cat", "if there is a tail, then it's also a cat", and, let's say, the program will understand what a cat is, and easily recognize where it is sitting and where it is lying.

But even if you really want to, you can't take into account all the nuances: you will come across a picture where a cat is dressed in a bunny costume, and your software will no longer be able to cope with the task.

A neural network is a program that can learn based on the data uploaded to it. There are no ready-made algorithms, the AI will write them itself during training. In the example described, it is necessary to "feed" the neuron several thousand photos of cats, after which it will teach itself to recognize cats in any costume and scenery.

Why they are important for business

The use of neural networks increases the competitiveness of the company. With their help, you can analyze a large amount of data, automate processes, predict demand, optimize marketing activities and logistics.

In our experience, the following capabilities of neural networks are most useful for business.

Automate tasks

Neural networks help automate routine processes, such as order processing, inventory management, and customer support. This improves the quality of the product and service, reduces business costs, increases the speed of completing tasks and, consequently, the competitiveness of companies.

For example, Google has built a neural network into the Gmail service. Now the AI analyzes the content of emails and determines in which folder to put them — "General", "Social Networks" or "Advertising". As a result, the system reduces the amount of spam and helps users focus on more important tasks than sorting emails.

Big Data Processing

With the help of neural networks, you can process huge amounts of data and gain insights to make more informed management decisions.

For example, Netflix uses neural networks to analyze views and offers the audience films that coincide with their interests.

Increase efficiency

The ability to improve the quality of products, services and services and anticipate customer needs increases the overall efficiency of the business.

For example, Amazon has a built-in neural network that studies the user's previous purchases and makes relevant recommendations based on them. Thanks to this, customers buy more, and the company earns more.

Where AI can be used to improve performance

Artificial intelligence is used in areas that are characterized by an abundance of accumulated information, for example, in marketing, finance, and e-commerce.

Analytics

Due to AI, it is possible to identify hidden patterns and trends that are almost impossible to detect "by eye", to analyze large amounts of data on consumer behavior, preferences, interests, needs, goals and problems of customers.

In the past, services like YouTube collected and provided data, and it was up to you to interpret it.

Today, you can enter, for example, Google Analytics with the AI Insights function, which will not only provide data, but also interpret it, for example, by analyzing CJM.

Let's imagine that you sell tickets to events, you have a lot of traffic, but most users do not make a purchase: they click on tabs, study posters, go to the payment page, and exit in a couple of minutes. This indicates a problem with the interface or the purchase process, which the AI will tell you. Knowing this data, you can test and improve the process to increase the number of completed transactions.

Content Creation

Experts can confidently say that writing high-quality text or creating an image can take several days. But if you use neural networks, the process becomes many times faster.

Visual content

There are already many foreign and domestic neural networks available, which, based on the text description, can produce ready-made illustrations, logos, icons for applications, and more.

Some services can also "animate" the picture, turning it into a mini-video.

Online sales

In the current realities, when contacting the technical support of many companies, a robot will first answer you, try to understand the issue and only then send you to a specialist, and sometimes it will solve the problem itself without resorting to human help.

A couple of years ago, these robots were "mechanical". That is, as many phrases as the programmers recorded in it, exactly as many answer options were in the chatbot's arsenal.

CRM

There are many CRM systems on the market, but solutions with built-in artificial intelligence win. Like an invisible assistant, it analyzes incoming data and performs useful actions based on it, which, if you don't look closely, you won't always notice right away.

Therefore, when choosing a CRM for your needs, we advise you to focus on those systems in which AI is built-in.

Financial Management

Neural networks are also often used in the financial sector, for example, in banks to check the creditworthiness of a client. Such scoring reduces the approval time of an application from several days to minutes.

Artificial intelligence also prevents fraud. It analyzes customer behavior, transactions, events in the system, and identifies unusual activities.

There are already AI-based services that allow businesses of any size to handle the flow of financial transactions.

These examples show only a fraction of the capabilities of AI, but even partially integrating them into your workflows can have a big impact on business productivity.

Using Neural Networks: How to Get Started

Analysis of the company's needs

Neural networks are undoubtedly great helpers. But if you start improving all areas of your business at once, you can spend a lot of time transforming what is already working well or, conversely, ignore the aspects that are dragging down your business.

To prevent this from happening, first conduct a thorough analysis of the company's needs. Identify the weaknesses of the company and start with them.

This can be automating routine tasks, improving customer service, forecasting demand, or optimizing logistics. Understanding the specific problems you want to solve with AI will save you effort, time, and money.

Choosing a service

When you have identified the main pain that you want to fight with the help of neural networks, you need to study all the solutions. The most popular or most expensive program will not always be ideal for your business.

When choosing software, study the functionality, cost, and reviews of other users. It is also worth paying attention to factors such as the ability to integrate with existing systems in your company, the level of support and training, as well as the ease of setting up the program.

Possible difficulties

The introduction of neural networks provides advantages over competitors, but at the same time it is fraught with difficulties:

1. Resistance from employees. Few people like innovations, especially if they see them as a threat to their role in the company or do not understand how to use new tools. Conduct training, explain to employees why their work will become easier and faster with a neural network, and also analyze in detail all the functionality of the program.
2. Unreasonably high costs. Some neural networks will be completely free, but there are individual AI solutions that will require initial investments in development, infrastructure, and training. To prevent the budget from going down the drain, first assess the ROI, and only then immerse yourself in the implementation of your plans.
3. Need for large amounts of data. If you decide to create a neural network specifically for your company, for example, a chatbot for responding to customers, then you will need a lot of information to train it. As a solution, you can create your own database or use synthetic data. In any case, allow additional money and time for this stage.
4. Uncertainty in development and testing. The development and testing of neural networks can be unpredictable and take longer than expected. To reduce risks and adapt solutions, neural networks can be trained and implemented on pilot projects.

As practice shows, companies that implement new technologies are one step ahead of competitors.

Therefore, do not be afraid to implement neural networks in your work, of course, within reasonable limits.

Good luck and successful promotion!