It's clear about artificial intelligence (AI)

Simply put, artificial intelligence (AI) is a system or machine that can mimic human behavior to perform tasks and gradually learn using the information it collects. AI has many incarnations, for example:

• chatbots use AI to quickly analyze customer requests and give appropriate answers;
• "Smart assistants" use AI to extract information from large data sets in any form and optimize planning;
• recommendation systems automatically select similar programs for viewers based on previously viewed ones.

AI is not a format or a function, it is a process and the ability to think and analyze data. With the word "artificial intelligence," many imagine intelligent humanoid robots that seek to conquer the world. However, AI is not intended to replace humans. Its goal is to expand human skills and capabilities. Which makes it a valuable business resource.

Terms used in the field of artificial intelligence

Today, the term "AI" is widely used to refer to applications for complex tasks that previously only humans could perform, such as customer service or a game of chess. It is often used as a synonym for machine learning and deep learning, which are actually subsections of the science of artificial intelligence. and have their own specifics. For example, machine learning focuses on creating systems that are trained and developed through data science. The difference is that machine learning always involves the use of AI, however, AI does not always imply machine learning.

To harness the power of AI to maximize business benefits, you need to hire data scientists. Data science is a field at the intersection of statistics and computer science that uses the methods of these two disciplines to analyze data from a variety of sources.

AI and developers

Developers use artificial intelligence to more efficiently perform tasks that would otherwise have to be done manually, interact with customers, identify patterns and solve problems. To start working with AI, developers will need mathematical knowledge and the ability to use algorithms.

If this is your first time using artificial intelligence to create applications, it is recommended to start small. By creating a relatively simple project like tic-tac-toe, you will master the basics of artificial intelligence. Learning by doing is a great way to develop any skills, and artificial intelligence is no exception. Having successfully completed several small projects, you will understand that the possibilities of artificial intelligence are truly limitless.

How AI technology can be useful for companies

AI makes it possible to reproduce and improve how we perceive the world around us and react to it. This property of AI is at the heart of innovation. AI is based on various machine learning technologies that recognize patterns in the data and generate predictions. It creates surplus value for businesses through the following capabilities:

• helps you harness the full potential of your data.
• makes reliable forecasts and automates complex tasks.

AI and Corporations

AI-based technologies are helping to increase efficiency and productivity by automating processes and tasks that were previously performed by humans. AI is also able to interpret amounts of data that cannot be interpreted by a person. This skill can bring significant benefits to the business. For example, Netflix uses machine learning for personalization, which helped increase audiences by 25% in 2017.

Most companies have made data exploration a priority and are investing heavily in it. According to a recent survey conducted by Gartner among more than 3,000 CEOs, respondents named data analytics and business intelligence as the main technologies for success. According to respondents, these technologies are of the greatest strategic importance, so they account for the bulk of investments.

AI offers benefits to all aspects and industries of business of all sizes, both general and specialized:

the use of operational and demographic data makes it possible to predict the amount of profit from the customer throughout the entire period of interaction (the value of the customer service cycle);
optimization of pricing based on the behavior and preferences of buyers;
Pattern recognition for X-ray analysis and cancer diagnosis.

Applying AI in Corporations
According to the latest Harvard Business Review report, companies predominantly use AI for the following purposes:

Detect and prevent security breaches (44%)
Troubleshoot technical problems with users (41%)
Reduced product management tasks (34%)
Assess internal compliance with approved suppliers (34%).
Why have AI technologies become so popular?
Three factors contribute to the widespread adoption of AI:

Availability of high-performance computing resources at a low cost. The presence of numerous computing resources in the cloud has made them available to a wide audience. Previously, computing systems for AI were local and prohibitively expensive.
Availability of large amounts of data for training. To teach AI to make accurate predictions, it must process large amounts of data. The emergence of various tools for marking data, as well as simple and affordable means of storing and processing structured and unstructured data, make it possible for an increasing number of companies to create and train AI algorithms.
Competitive advantages of AI. More and more companies are learning about the competitive advantages of AI for business and making the introduction of this technology their priority. For example, specialized AI recommendations help to make more informed decisions faster. AI also offers many tools and opportunities to reduce costs and reduce risks, accelerate time to market, etc.

5 Common Myths About Corporate Artificial Intelligence
Many companies have successfully implemented artificial intelligence technology in their processes, however, there are still many misconceptions about its functions. Therefore, enterprises do not always understand in which areas this technology can be useful, and in which it is not. In this article, we'll take a look at five common myths about artificial intelligence.

Myth #1: Enterprise AI technologies require the development of their own solutions.
Reality: Most enterprises are implementing AI using both their own developments and ready-made solutions from third-party suppliers. Proprietary AI technologies enable the enterprise to solve its unique problems, while ready-made AI solutions are easily implemented and simplify the solution of more common business problems.
Myth #2: AI magically delivers the desired results right away.
Reality: For AI technology to deliver tangible benefits, it takes time, careful planning, and a clear idea of what results need to be achieved. It is necessary to adhere to an iterative approach and have a certain strategy so that the AI environment does not end up being a set of useless, disparate solutions.
Myth #3: Employees won't have to monitor enterprise AI systems.
Reality: Corporate artificial intelligence is not robots out of control. The value of AI lies in the fact that it complements human capabilities and helps employees to solve more strategically important problems. In addition, it depends on the employees on the basis of what data the technology will work and how it will use this data.
Myth #4: The more data, the better.
Reality: Enterprise AI systems need to work on the basis of quality data. Only up-to-date, relevant, enriched, high-quality data can help you find truly useful business insights.
Myth #5: Only data and models are needed for enterprise AI systems to run effectively.
Reality: Data, algorithms, and models are just the beginning. An AI solution must be scalable so that it does not lose relevance in an ever-changing business environment. To date, most of the corporate AI solutions are developed by data scientists. These solutions have to be configured and maintained manually, which is quite time-consuming. Also, they don't scale. For AI technologies to be beneficial, solutions are needed that will scale as business needs change and the company's AI strategy is implemented.

Benefits and Challenges of IMPLEMENTing AI
The value of AI for business is confirmed by many examples of success. Adding machine learning and cognitive operations technologies to traditional business processes and applications provides increased convenience and productivity.

Nevertheless, the introduction of AI is associated with certain difficulties. Few companies are tapping into the full potential of AI, and there are several reasons for this. For example, if a company does not use cloud technologies, the cost of computing for AI will be too expensive. In addition, AI is difficult to develop and requires the involvement of scarce specialists. Understanding where and why AI is needed, as well as tackling the challenge of engaging third-party service providers, will help minimize these problems.
AI: Success Stories
AI has played an important role in these success stories.

According to a Harvard Business Review report, the Associated Press has produced 12 times as many articles, teaching AI to write short news articles. This allowed journalists to focus on working on larger stories.
Deep Patient, an AI-based diagnostic tool developed by the Icahn School of Medicine at Mount Sinai Hospital, helps identify patients at high risk of the disease before diagnosis. According to insideBIGDATA, this tool can diagnose almost 80 diseases in advance by analyzing patients' medical data.
Turnkey solutions simplify the implementation of AI in the enterprise
The advent of AI-based solutions and tools means that more companies can take advantage of this technology to save money and time. AI-based solutions, tools, and software include built-in AI tools or help automate algorithm-based decision-making.

These can be both offline databases that use machine learning for self-service recovery, and ready-made models that can be used to solve problems such as pattern recognition and text analysis. All of this helps companies accelerate time to value, increase productivity, reduce costs, and improve customer relationships.

Getting Started with AI
Using chatbots to communicate with customers. Chatbots use linguistic processing to analyze customer questions and provide answers and information. Chatbots are able to learn and over time begin to bring more and more benefits.

Data center monitoring. Centralize data about the network, applications, database performance, quality of service, and more with a single cloud platform that automatically tracks thresholds and detects deviations, helping IT professionals save time and effort.

Perform business analysis without expert assistance. Analytical tools with a visual user interface simplify the execution of queries against the system and provide visual results.

Obstacles to unlocking the full potential of AI
Despite the many opportunities of AI and machine learning, only a few companies manage to realize their full potential. Why? Oddly enough, the main obstacle is... people. Inefficient processes can prevent a company from realizing the full potential of AI.

For example, data scientists may have trouble obtaining the resources and data needed to create machine learning models. Or, problems may arise when interacting with colleagues. In addition, data scientists have to deal with numerous open-source tools, and application developers sometimes have to completely rewrite the code of training models in order to build them into applications.

The list of AI-based tools is constantly expanding, forcing IT professionals to devote more time to supporting the data science department by updating the production environment. In addition, existing standards limit the ability of data scientists.

In addition, managers are not always able to fully appreciate the return on investments in AI. As a consequence, they do not provide enough support and funding to create an effective integrated ecosystem, which is the key to the successful use of AI.

How to Create the Right Culture
To make the most of AI's power and overcome obstacles to the successful adoption of new technologies, it is necessary to create a team culture that will support the AI ecosystem. In such an environment:

Business analysts and data scientists work together to define goals and objectives.
Data engineers provide data and platform management for performing analysis;
Data scientists prepare, study, visualize and model data using a specialized platform.
IT system architects provide infrastructure management to explore data both on-premises and in the cloud.
Application developers deploy models to applications to create data-driven products.

From Artificial Intelligence to Adaptive Intelligence
AI is increasingly being used in manufacturing operations, which has led to the emergence of a new term – adaptive intelligence. Adaptive intelligent applications help you make better business decisions by leveraging real-time internal and operational external data and highly scalable infrastructure.

Such applications make it possible to "work smartly" in every sense of this expression and offer customers better products, recommendations and services – and, ultimately, increase profits.

Strategic necessity and competitive advantages of AI
AI is a strategic necessity for any company that wants to increase productivity, open up new opportunities for profit and strengthen customer loyalty. This technology has already helped many companies gain a competitive advantage. Thanks to AI, you can do more in less time, provide an effective personalized service and predict results, which means you can get more profit.

Nevertheless, AI remains a fairly new and complex technology. To reach its full potential, to create and apply AI-based solutions, a high level of skill is needed. To be successful, it's not enough to just hire data scientists. It is necessary to use the right management tools, processes and strategies.
Best Practices to Get the Most Out of AI
Harvard Business Review provides the following guidelines for getting started with AI:

Apply AI in areas that have the immediate and most significant impact on profits and expenses;
use AI to increase productivity instead of reducing or increasing staff;
Start your implementation with support units (preferably IT and accounting).
Getting help on the way to mastering AI
AI is becoming an integral part of business. Sooner or later, all companies will be forced to use AI technologies to create their own ecosystem and maintain competitiveness. Those who neglect progress are at risk of being left behind in the next 10 years.

Your company may be the exception to the rule, but most businesses don't have the in-house data scientists and the necessary resources to build an ecosystem and develop applications that will help put AI capabilities at their service.

If you need help developing the best strategy and accessing tools for successful AI adoption, seek help from a trusted partner who has extensive experience and a wide range of relevant solutions.

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