026 – In our last article dedicated to Artificial Intelligence, we gave an overview of this vast topic that opens up many areas. For this reason, we wanted to make a second installment and expose more of this topic.

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AI has become an overall term for applications that perform complex tasks that previously required human input, such as communicating online with customers or playing chess. The term is often used interchangeably with its subfields, which include machine learning (ML) and deep learning

Developers use AI to perform tasks more efficiently that would otherwise be done manually: customer communications, pattern identification and problem solving. But one of the most important premises of AI is to help organizations and businesses.

The fundamental principle of AI is to replicate, and then surpass, the way humans perceive and react to the world. It is fast becoming the cornerstone of innovation. AI, driven by various forms of machine learning that recognize patterns in data to enable predictions, can add value to your business by providing a more complete understanding of the abundance of data available and relying on predictions to automate overly complex or mundane tasks.

Today, AI technology improves business performance and productivity by automating processes or tasks that previously required human effort. AI can also make sense of data on a scale that no human ever could. This capability can generate significant business benefits. For example: Netflix uses machine learning to provide a level of personalization, which helped the company increase its customer base by more than 25%.

Most companies have made data science a priority and are investing heavily in it. A 2021 McKinsey survey on artificial intelligence (AI) found that the proportion of companies reporting having adopted AI in at least one function had increased to 56%, up from 50% the previous year. In addition, 27% of respondents reported that at least 5% of revenue could be attributable to AI, up from 22% a year earlier.

AI has value for almost every function, business and industry. It includes general and industry-specific applications, such as:

  • Use of transactional and demographic data to predict how much certain customers will spend over the course of their relationship with a company (or customer lifetime value).
  • Price optimization based on customer behavior and preferences.
  • Using image recognition to analyze X-ray images for cancer symptoms.

Today the business world uses AI for different purposes, the main ones are:

  • Detect and deter security intrusions (44%)
  • Solve users’ technology problems (41%)
  • Reduce production management workload (34%)
  • Measure internal compliance in the use of approved vendors (34%)

And many are being driven to adopt it for 3 main reasons:

Affordable, high-performance computing power is now available. The abundance of commodity computing power in the cloud enables easy access to affordable, high-performance computing power. Prior to this development, the only computing environments available for AI were not cloud-based and were cost prohibitive.

Large volumes of data are available for training. AI must be trained on lots of data to make the right predictions. The emergence of different tools for labeling data, plus the ease and affordability with which organizations can store and process structured and unstructured data, allows more organizations to design and train AI algorithms.

Applied AI provides a competitive advantage. Increasingly, companies are recognizing the competitive advantage of applying AI insights to business objectives and making it an enterprise-wide priority. For example, targeted recommendations provided by AI can help companies make better decisions faster. Many AI features and capabilities can reduce costs and risks, speed time to market, and much more.

As artificial intelligence capabilities have made their way into mainstream business operations, a new term is evolving: 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 highly scalable computing infrastructure.

These applications essentially make your business smarter. This allows you to offer your customers better products, recommendations and services, which generates better business results.

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