Companies that want to implement Artificial Intelligence in their organization successfully should understand what AI exactly is, how it creates value, where it currently stands, and how it can be executed. Multiple industries will benefit from the enormous value AI provides. Because of this, many companies in different industries are eager to start with the implementations of it. As McKinsey studies have shown, front-runners who rapidly adopt AI could realize a 122% gain in cash flow by 2030 while laggards experience a decrease in cash flow of 23%. Companies that didn’t start the adoption of AI yet, may lag behind their competitors already. The importance of evaluating what AI can do for your organization is growing. Therefore, a clear strategy is needed to make the adoption a success instead of a costly failure. In this article, we will further explain what AI is, how it works and the future perspective.
Explanation of AI
When people think of AI, they often think of robots or difficult programming machines, but it is much more simple and powerful than most people know. Using AI can deliver extreme returns on investments for companies by benefiting from automation or precise predictions. Its power has the ability to affect almost all sectors by being able to predict and detect patterns more continuously and accurately based on huge data sets. In specific domains, AI has currently surpassed human intelligence. AI was named and defined as the ability of machines to perform human-like tasks. Some extra clarification is needed because people are often confused when terms as deep learning and machine learning are mentioned when talking about AI.
In short, AI is a science and set of computational technologies that are inspired by the ways people learn, reason, and take action. We now experience a rise in machines powered by AI. Machine learning is as IBM state, enabling a machine to learn from data without explicitly programming it with rules because it can learn from the data it’s given. In this case, the algorithm is feed with data and adjusts and improves itself. In contrast with traditional science algorithms, machine learning is about applying an algorithm to fit a model to the data. And lastly, deep learning. Deep learning is not per se an algorithm, but more a family of algorithms implementing deep networks, layers. Raw data is given to these algorithms or neural networks and decides what relevant features are.
The more data can be added, the more powerful and better the performance. In addition, there is not one algorithm that could solve all different problems. The success of solving a problem depends on the problem itself and the availability of data. Sometimes, multiple algorithms are for instance needed to solve a certain problem.
The future perspective
The increasing computing power and the available data contributed to the potential value of algorithms. Companies have seen the possibilities that smart technologies can provide to drive innovation and growth. AI makes it possible for them to drive efficiency, enhance employee experience and capability, decrease costs, automate tasks, and move closer to the customer. In conclusion, an essential value. With these advantages, it is logical that artificial intelligence is the most transformative technology of the last few decades. For this radical innovation as AI, it becomes difficult to keep track of what AI will look like in the future. But the thing that is certain is that AI is starting to deliver on its potential and its benefits for businesses are becoming a reality.
The huge impact of AI is already seen in many different sectors and industries, varying from healthcare and medicine to retail and e-commerce.
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