Artificial Intelligence in Business: Best Use of AI

Artificial intelligence in business has grown into such an important domain in the business world that companies are developing at a very rapid pace with the introduction of ChatGPT, Copilot, Midjourney, and many more.

35% of companies will be investing in AI by 2024, according to experts, which quite informs about the role that AI is playing toward operational efficiency and transformation processes to businesses. Here we tell you how and where businesses can apply AI today.

How artificial intelligence works

Artificial Intelligence in Business

In general, artificial intelligence is a term used to refer to a rather wide set of technologies directed toward granting machines the ability to think like human beings, learning to act from vast amounts of unstructured data such as text, images, or video.

Combined with data, analytics, and automation, these technologies enable businesses to:

  • automate routine processes;
  • ensure cybersecurity;
  • improve decision-making processes;
  • improve operational efficiency, providing a competitive advantage and driving growth.

Use of AI in Business

Artificial Intelligence in Business

There is virtually no limitation to what one can achieve with artificial intelligence, and corporations are looking to avail them in any possible way that will keep them competitive in the marketplace and guarantee success. Impressive facts will judge the influence of AI in business, according to research by the authoritative publication Yakov and Partners :

  • Some 66% of generative AI implementations are within the area of marketing and sales, 54% in the customer service area, 49% in the R&D section, and 31% in IT.
  • 94% of surveyed companies note cost reduction as a key effect of introducing AI into business processes.
  • Almost 70% of companies estimated the real financial effect on EBITDA from implementing AI at up to 5%.
  • About 50% of companies invest approximately 1-5% of their IT and digitalization budget in AI.

The global market is projected to reach $2 billion by 2030, contributing $15.7 trillion to the global economy. All these steps mean that AI has a very strong and convincing influence on modern business. Several companies are applying the technology at present or plan to enlarge their use of it. This, in other words, is an indication that, for sure, AI is useful in business.

Subfields of Artificial Intelligence

Artificial Intelligence in Business

Machine learning (ML)

This is a field of computer science that emulates human activity to solve hard problems. It is the work to use data: photographs, numbers, and text.

The more data entered, the more accurate the results are. After the preparation of data, the definition of a machine learning model is to apply the data for training for the model to learn how to generalize from the data to be able to recognize patterns or make predictions.

The recommendation system in YouTube and Netflix is born from machine learning. Similarly, the type of information visible on your Facebook page and the product recommendations made there are also machine learning.

The more data you have, the better the program works.

Natural Language Processing (NLP)

NLP marries computational linguistics with machine learning and deep learning models to process human language: human text and voice data.

Examples of using NPL:

  • Chatbots and virtual assistants.
  • Spam detection.
  • Machine translation, for example, from Google.

It is, therefore, a vital inclusion in enterprise solutions aimed at enhancing the efficiency of business operations, the performance of employees, and facilitating business processes.

Computer Vision (CV)

Computer vision is the technology that allows systems to understand and process images, videos, and other visual forms of data. The small defect or incongruity in products, or the processing of thousands per minute, can be identified by computer vision. For example, this is how you can detect defects in production or recognize a person in a photograph. Many of the applications using computer vision relate to the classification of an image into one or more categories, finding objects within boundaries, and even identifying the class of an object within an image.

These can be tasks in a CV, like pixel-level classification of an image, key point detection in the image (detection of eyes, nose on the face, for instance), generation of a caption for an image which should describe the image, not only a computer vision task but also an NLP task.

Deep learning

These are machine-learning approaches that naturally train a computer to perform what human brains have been programmed to do. Meanwhile, deep learning models apply the inference of the most accurate conclusion and prediction since it is capable of recognizing even the most complex patterns from the text, graphics, and audio.

Deep learning models have the potential to train on plenty of labeled data. In other words, they learn the features of the data without being extracted through the use of the neural network architecture. With deep learning, human tasks that could otherwise require human services are automated. The self-driving car from Yandex is one example of the application of deep learning.

Generative AI

This is a type of AI that can use existing data to create new content similar to the original.

For example, a program can accept the JSON picture, and create a new one, but with another background, or even other details. It means that from any kind of template, you can make a new picture, text, or even music without any kind of template. Generative models are applied in design, image processing, music, and many more. Generative models represented by the neural network have many strong representatives, like OpenAI’s ChatGPT.

Expert system

An expert system is an artificial intelligence system that possesses knowledge in some narrow subject areas; they have an attribute of weak structure and difficulty in formalization. The system can provide to the user with well-reasoned decisions containing knowledge, logical deduction, and a system of explanation. The applications are fairly universal for this system, ranging from expert systems, credit analysis, and stock market trading to virus detection, warehouse optimization, and flight scheduling.

What are the benefits of AI in business?

Artificial Intelligence in Business

Always ready to work and quickly responds to requests.

The reason lies in the fact that that is one of the reasons it is so popular with any scale of company within reach. It also helps to keep in touch with customers at any time using chatbots and virtual assistants built on artificial intelligence. It boosts customer satisfaction to a brand-new level with increased trust in the company and long-term cooperation.

Today, most of the daily duties are already carried out by the chatbots, such as customer service and informing employees. Some examples of current popular virtual assistants include Amazon’s Alexa, Google’s Google Assistant, and Microsoft’s Elsa Speak.

Helps build personalized communication with clients.

All this kind of data will enable the company to learn exactly what each client would prefer, how his or her behavior is, what most often he or she buys, etc. You can develop custom ads, product offers, and even support messages for each customer.

Some AI products are even able to tell whether a certain response is within the “norm” to help identify problems with customers.

Analyzes large amounts of data quickly and improves productivity

AI works better with the processing of data and makes it useful in conducting the work quickly, and more precisely than people. It can help human beings in their case since something repetitive will not be done with a mistake.

We work out AI solutions for the customer’s projects; for instance, today, an AI-based order product choice is being implemented to automate it. Thus, the company will be able to cut down operational costs, increase sales numbers, and reach a new level of development.

Predict business threats

It helps one establish weaknesses, fraud, or potential threats to the business much before they become serious problems. This will help evade financial losses, damage to reputation, and legal problems.

Develop new ideas and reduce business costs

Artificial intelligence can process large amounts of data to generate new ideas, help analyze the new opportunities that arise in the market, and greatly aid in the development of new products and services. This, therefore, means that companies will be more empowered to bring new products into the market much faster, gain a competitive edge over their rivals in such a dynamic industry, and hence control a much greater share of the market.
For example, ChatGPT is an AI-powered software. It helps businesses develop strategies and automate various processes:

  • create marketing materials;
  • ideas for brainstorming;
  • program code;
  • assist in client adaptation;
  • attract new clients;
  • analyze data and much more.

How to Implement AI in Business

Artificial Intelligence in Business

Defining the goals and expected results helps you build a strategy that goes in line with the overall goals of your company.

It should serve in carrying out a thorough analysis of the business process, from the point of recognition of bottlenecks and those areas where work can be optimized by applying AI technologies.

Select AI technologies that correspond to the strategic goal. For instance, it can be machine learning, natural language processing, or a generative neural network.

Engage with active systems. Every deployment of AI must provide various considerations about all current platforms and programs that exist. This is important to ensure compatibility and thus reduce the chance of failures.

Train staff on how to use AI. Engage key people in the development of strategic decisions and build a team that will be responsible for implementing AI in your company. This will help develop personnel professionally and thus create conditions friendly to innovation to bring outstanding results in the use of new AI technologies.

Monitor your AI system and respond quickly to problems to ensure it runs smoothly and efficiently.

Periodically assess the implementation results, and by doing so, adjust your strategy to get the best out of the technology.

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