What is Artificial Intelligence—AI?

 In the simplest terms, artificial intelligence (AI) refers to systems or devices that mimic human intelligence to perform tasks and that can improve themselves based on the information they gather. 

What is Artificial Intelligence—AI?

Artificial intelligence manifests itself in a number of forms. Some of these examples:

  • Chatbots use AI to understand customer problems faster and provide more efficient answers
  • AI uses it to analyze critical information from a large set of text data to improve scheduling
  • Recommendation engines can provide automated recommendations for TV shows based on users' viewing habits

AI is more about the ability to think and analyze data than it is about a particular form or function. Although AI presents images of high-performance human-like robots taking over the world, it is not intended to replace humans. It aims to significantly enhance human capabilities and contributions. This makes it a very valuable business asset.

Artificial intelligence terminology

Artificial intelligence has become an umbrella term for applications that perform complex tasks that once required human input, such as communicating with customers online or playing a game of chess. The term is often used interchangeably with its sub-fields, which include machine learning and deep learning. However, there are differences.. 

For example, machine learning focuses on creating systems that learn or improve their performance based on the data you consume. It is important to note that although all machine learning is AI, not all AI is machine learning.

To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with scientific knowledge to analyze data collected from multiple sources.

How AI can help organizations

The main principle of AI is to simulate and transcend the way humans perceive and interact with the world around us. Which is fast becoming the cornerstone of innovation. Now that AI is equipped with many forms of machine learning that recognize patterns in data to make predictions, AI can add value to your business by:

  • Provide a more comprehensive understanding of the wealth of data available

  • Rely on predictions to automate highly complex and routine tasks

Artificial intelligence in the corporate sky

AI technology improves enterprise performance and productivity by automating processes or tasks that once required manpower. AI can also understand data on a scale that no human can achieve. This ability can bring significant benefits to business. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25 percent in 2017.

Most companies have made data science a priority and are still investing heavily in it. According to a Gartner survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the best distinguishing technology for their organizations. The CEOs surveyed see these technologies as the most strategic for their companies and, therefore, they attract the most new investment.

AI offers value to most jobs, businesses, and industries. It includes general applications and applications for specific areas, such as:

  1. Using transactional and demographic data to predict how much specific customers are spending over their relationship with the company (or customer lifetime value)
  2. Optimizing pricing based on customer behavior and preferences
  3. Using image recognition to analyze X-ray images for signs of cancer

How companies use artificial intelligence

According to Harvard Business Review, companies use AI primarily to:

  • Detecting and deterring security intrusions (44 percent)
  • Solve users’ technical issues (41 percent)
  • Reduce production management work (34 percent)
  • Measuring internal compliance when using approved suppliers (34 percent)

What are the driving factors for the adoption of artificial intelligence?

Three factors are driving the development of AI across industries:

  1. It provides easy and affordable high-performance computing. The abundance of business computing power in the cloud has enabled easy access to affordable high-performance computing. Prior to this development, the only computing environments available for AI were non-cloud-based and cost prohibitive.
  2. Having large amounts of data available for learning. AI needs to learn from a lot of data to make correct predictions. The emergence of different tools for collecting disaggregated data, in addition to the ability of organizations to easily and affordably store and process this data, both structural and unstructured data, has led to more organizations being able to create and train AI algorithms.
  3. Applied AI technology provides a competitive advantage. Companies are increasingly realizing the competitive advantage of applying AI insights to business goals and making them a business-wide priority. For example, targeted recommendations made by AI technology can help make better decisions faster. The many features and capabilities of AI can lower costs, reduce risks, speed up time to market, and more.

5 common myths about enterprise AI

While many companies have successfully embraced AI technology, there is a lot of misinformation about AI and what it can and cannot do. Here are five common myths about artificial intelligence:

Myth #1: AI requires a DIY approach. 

Fact: Most companies embrace AI by combining both in-house and unconventional solutions. Developing in-house AI allows companies to customize unique business needs; Pre-built AI solutions enable you to simplify implementation with a ready-to-use solution to the most common business problems.

Myth #2: AI delivers magical results instantly.

Fact: The road to AI success takes time, thoughtful planning, and a clear idea of ​​the results you want to achieve. You need a strategic framework and an iterative approach to avoid introducing an arbitrary set of disconnected AI solutions.

Myth #3: AI does not require people to operate it.

Fact: AI is not about bots controlling it. The value of AI is that it increases human capabilities and reduces the burden on your employees to devote themselves to more strategic tasks. Moreover, AI relies on people to provide it with the right data and work with it in the right way.

Myth #4: The more data, the better.

Fact: Enterprise AI needs smart data. To get the most effective business insights from AI, your data needs to be high-quality, up-to-date, relevant, and rich.

Myth #5: Enterprise AI only needs data and models to succeed.

Fact: Data, algorithms, and models are the starting point. But the AI ​​solution must be scalable to meet changing business needs. So far, most enterprise AI solutions have been designed by data scientists. These solutions require extensive manual setup and maintenance and are not scalable. To successfully implement AI projects, you need AI solutions that are scalable to meet the needs as you move forward with AI technology.

Benefits and challenges of activating artificial intelligence

There are many success stories that prove the value of artificial intelligence. Companies that add machine learning and cognitive interaction to traditional business processes and applications can dramatically improve user experience and enhance productivity.

However, there are some obstacles. Few companies have deployed AI on a large scale, for several reasons. For example, if they do not use cloud computing, AI projects are often very expensive. They are also complex to construct and require expertise in high demand with insufficient supplies. Knowing when and where to integrate AI, as well as when to turn to third parties, will help reduce these difficulties.

Artificial intelligence success stories

Artificial intelligence is the driving factor behind some important success stories:

  • According to the Harvard Business Review, the Associated Press produced 12 times more stories by training an AI program to write short earnings news stories. This effort freed the agency's journalists to write more in-depth articles.
  • Deep Patient, an AI-based tool developed by the Icahn College of Medicine at Mount Sinai, allows clinicians to identify high-risk patients before diseases are diagnosed. The tool analyzes a patient's medical history to predict nearly 80 diseases one year before onset, according to insideBIGDATA.

Ready-to-use AI makes it easier to activate AI

The emergence of AI-powered solutions and tools means that more companies can benefit from AI at lower cost and in less time. Off-the-shelf AI refers to solutions, tools, and software that either have built-in AI capabilities or automate algorithmic decision-making.

Off-the-shelf AI can be anything from autonomous databases, which are self-repairing using machine learning, to pre-built models that can be applied to a variety of data sets to solve challenges such as image recognition and text analysis. It can help companies achieve value faster, increase productivity, reduce cost, and improve customer relationships.

How to get started with artificial intelligence

  1. Communicate with customers through chatbots. Chatbots use a natural language processing method to understand customers and allow them to ask questions and obtain information. These bots can also learn over time so they can add more value to customer interactions.
  2. Data center monitoring. IT operations teams can save massive amounts of time and energy on system monitoring by putting all web data, application data, database performance, user experience, and log data into a single cloud-based data platform, which automatically monitors thresholds and detects anomalies.
  3. Conduct business analysis without the need for experts. Analytical tools with a visual user interface allow non-technical people to easily search within the system and get understandable answers.

Discover barriers to realizing the full potential of AI

Despite the promises of AI, some companies are not realizing the full potential of machine learning and other AI functions. Why? Paradoxically, it turns out that the problem is, in large part, with people. Inefficient workflows may also prevent companies from capturing the full value of their AI implementations.

For example, data scientists may face challenges in obtaining the resources and data they need to create machine learning models. They may have trouble cooperating with teammates. They have many open source tools to manage, while sometimes application developers need a thorough recoding of the models that data scientists develop before they can embed them in their applications.

And with a growing list of open source AI tools, IT administrators are spending more time supporting data science teams by constantly updating their work environments. And this problem is exacerbated by limited standardization in the way data science teams want to work.

Finally, CEOs may not be able to envision the full potential of their companies' investments in AI. Thus, they do not provide enough care and resources to create the collaborative and integrated ecosystem necessary for the success of AI technology.

Creating the right culture

Making the most of AI, and avoiding the issues that prevent successful implementations, means creating an overall culture among teams that fully supports the AI ​​ecosystem. In this type of environment:

  1. Business analysts work with data scientists to define problems and goals
  2. Data engineers manage the data and the data platform, fully operational for analysis
  3. Data scientists prepare, explore, visualize, and model data on a data science platform
  4. IT engineers manage the critical infrastructure needed to support data science at scale, whether on-premises or in the cloud
  5. Application developers deploy models in applications to create data-driven products

From artificial intelligence to adaptive intelligence

As AI capabilities reach core enterprise operations, a new term has emerged: adaptive intelligence. Adaptive intelligence applications help companies make better business decisions by combining the power of real-time internal and external data with decision science and high-level computing infrastructure.

These applications essentially make your work smarter. This in turn enables you to provide your customers with better products, recommendations, and services, all of which leads to better business outcomes.

Artificial intelligence as an imperative and competitive strategic advantage

AI technology is an imperative strategic technology that works to gain greater efficiency, new revenue opportunities and enhance customer loyalty. It is also rapidly becoming a competitive advantage for many organizations. With AI, companies can get more done in less time, create personalized and engaging customer experiences, and predict business outcomes to increase profitability.

But artificial intelligence is still a new and complex technology. To get the most out of it, you need expertise in how to create and manage AI solutions at scale. The AI ​​project requires more than just hiring a data scientist. Companies must implement tools, processes, and management strategies to ensure the success of AI technology.

Best practices for getting the most out of AI

Harvard Business Review made the following recommendations for getting started with AI:

  • Apply AI capabilities to the activities that have the most and immediate impact on revenue and cost.
  • Use AI to boost productivity with the same number of people, rather than getting rid of or adding employees.
  • Start implementing AI in the back office, not the front office (you will benefit greatly from applying it to IT and accounting).

Get help with your AI experience

There is no choice to get out of switching to artificial intelligence. To stay competitive, every company must ultimately embrace AI and create an AI ecosystem. It is natural for companies that fail to embrace AI in some capacity over the next 10 years to stay behind.

Although your company may be an exception to this rule, most companies do not have the in-house skills and experience to develop the kind of ecosystem and solutions that can augment AI capabilities.

If you need help developing the right strategy and access to the right tools to succeed in your AI transformation journey, you should look for an innovative partner with extensive business experience and a comprehensive AI portfolio.

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