Neural networks have become a breakthrough in the last couple of years. According to McKinsey's estimates, thanks to AI, in the next 30-35 years, about half of all the processes that people currently perform will be automated. At the same time, the economic effect of the use of neural networks can reach 2.6-4.4 trillion dollars a year. Let's find out how to use neural networks in business and how they can help entrepreneurs.

How Neural Networks Help Business

According to a recent Gartner study, AI became the top technology in 2023. The company conducted a survey among 2,500 top managers, and 70% of them said that their companies are interested in the development of AI. 68% of executives believe that the pros of using neural networks outweigh the possible cons. 45% of companies increased investments in this area after ChatGPT appeared; 38% invested in neural networks to improve customer service and retention; 26% did it to increase revenue; 17% invested to optimize costs.

Creating Texts 

Neural networks create different texts depending on the algorithm and the tasks you set for them. AI tools help you come up with a name for a company or write a blog post, an expert article, or a sales message.

AI algorithms can adapt the style and tone of content to the given parameters, which speeds up the process of creating high-quality texts. Moreover, neural networks are used to edit and correct existing texts, improving their readability and SEO effectiveness.

In most cases, it's hard to distinguish texts written by a neural network. But you should consider a few signs:

• Grammar and sentence structure. Neural networks can generate illogical or incorrect sentence structures. What's more, texts are often saturated with clericalism and stamps.
• Emotional coloring. Neural networks can imitate emotional expressions, but they often sound "flat" or unnatural.
• Knowledge and facts. Neural networks can provide accurate answers to specific questions, but they don't always have a broad perspective or up-to-date knowledge of real-world events. Neural networks are trained on a large but limited sample of documents and can make factual errors in texts. So, check and confirm the information you receive from reliable information sources. 
• Answers to unexpected questions. Neural networks may have difficulty generating meaningful responses to non-standard or unexpected questions, especially if they are outside the scope of their learning model. 

But note that neural networks are constantly evolving, and the results of their generation are becoming increasingly difficult to distinguish from human text.

Graphics and Illustrations

With neural networks, companies can quickly generate realistic images and art, create designs for packaging, and illustrations for posts. Neural networks can turn texts into beautiful videos or edit ready-made videos in a certain style.

The use of neural networks to create graphics and logos is a real trend in the world of design. It's connected with the desire to save money. In conditions of limited budget, it's easier for a customer to use a neural network rather than pay a designer. In this case, the question always arises: How legitimate is it to use graphic content created by a neural network for commercial purposes? It's really a subtle point. Especially in terms of logo creation. If a painting can be removed, a brand logo is a story for several decades.

Generating Ideas

If you blog on your website or on social media, you've probably faced a shortage of ideas for new posts. Neural networks can help here too.

They are becoming assistants in social media management. They help generate ideas for reels, shorts, stories, and posts.

Neural networks are also a great feature for selling content to generate catchy headlines that will attract the attention of the target audience and reflect the benefits of the offer.

Customer Communication

AI can automate communication with customers. For example, banks are actively introducing bots that help customers solve problems.

Furthermore, neural networks help collect and analyze data on user experience and make personalized offers to customers.

Neural networks are trained to recognize and analyze emotional coloring comments, reviews, or social media mentions. This provides insights into how the public feels about a particular product, brand, or company.

Pros of Neural Networks in Business

Neural networks are handy for business. They do the following tasks best:

• Company scalability: automation and increased productivity. AI can work even at night; the company is less dependent on staff, as well as sick days, working days, and holidays. Neural networks increase the speed of one-time processing of requests, removing elementary and frequent requests from the team. The staff will be freed from routine tasks and can shift to more creative and strategic tasks.
• Reducing errors and reducing corruption. It's worth considering the human factor. People aren't machines; moods, weather, fatigue, or simple inattention can lead to errors of varying magnitude. Also, the personal interests of people in the role of company employees who perform specific, often routine or bureaucratic tasks lead to corruption. AI is a machine that is impersonal, objective, and not interested in specific results.
• Analyzing large amounts of data. AI is most useful in tasks requiring multi-parameter analysis. A person doesn't have time to process a large volume of requests that require analytical work, and the model, if it's well built, can instantly do the analysis and retrain itself in automatic mode, updating data daily, which is no longer possible for a person.

Although neural networks are being actively developed, they're still an imperfect tool and shouldn't be relied upon for everything.

The main problem with implementing AI is the lack of trust in neural networks. The robot can make mistakes, and this is difficult to predict at the start.