Artificial Intelligence (AI) is a technology that allows a system, machine, or computer to perform tasks that require intelligent thinking, that is, to mimic human behavior for gradual learning using the information gained and solving specific problems.

The integration of AI into mechanisms and systems allows the automation of routine, labor-intensive or complex processes, including increasing their accuracy and productivity. Therefore, this technology is an important business resource.

The benefits of implementing AI
The use of artificial intelligence and AI-based solutions provides businesses with a number of benefits.

Eliminating the human factor. Using programmable, self-learning algorithms eliminates the human error factor and allows you to find even non-obvious human solutions.
Reduced risks. AI machines can be used in situations involving risk to humans. For example, AI robots can replace humans in certain production areas or when working in natural disasters.
Round-the-clock availability. Intelligent machines can be used without breaks, weekends, and do not respond to distractions.
Adaptability. Within established conditions, the use of AI solutions allows for quick solutions. For example, AI in chatbots helps better understand the “live” language of customers, find answers to difficult questions, cope with a large flow of simultaneous requests and questions.
Quick decision making. AI-powered applications, machines, devices, and other tools make decisions faster than humans, which can be used in manufacturing processes, data analytics, predictive modeling, calculations, and other tasks.

Challenges of implementing AI
There are several reasons that slow down the adoption and use of artificial intelligence.

For supervised learning (with a teacher) neural networks need to mark up (label) data sets manually. This takes a lot of time.
Training models requires a large amount of data, which must first be collected from different sources, structured, cleaned of unnecessary information, and brought to a common format. Such work requires a well-built system and a staff of specialists.
The result obtained from the work of AI algorithms is difficult to interpret and understand in terms of decision-making logic.
Models are task-oriented. For example, if an AI algorithm is used to detect a specific type of fraud, it will not recognize other types of fraud – each task and each condition requires its own model.
If the initial training dataset is distorted or insufficient, the results of the AI may be distorted. For example, if only red objects are used in the training sample, errors or discrepancies may occur when a blue object appears in the self-training process.