Gartner Hype Cycle for Supply Chain Execution Technologies 2020
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Artificial intelligence (AI) is a major driver of digitalization. As part of the value chain, AI enables data-based decisions to be made.
Artificial Intelligence is a branch of computer science that enables machines to mimic cognitive, human behavior, for example reasoning, learning, interacting or problem solving. Since these cognitive skills are complex, AI is an umbrella term for various disciplines such as robotics, planning, problem solving, speech processing and machine learning (ML).
Above all, machine learning is considered the core of AI, namely the ability to make predictions based on comprehensive big data analyses. Large retail companies such as Amazon have been using AI for years to provide their customers with suggestions that match the products they have bought and searched for. American computer science professor John McCarthy coined the term in 1956. Since then, this scientific field has developed into one of the driving IT megatrends.
Today, we associate the term with technologies such as autonomous drones, robots, chatbots and virtual assistants. Artificial Intelligence is the key to innovation and new business models. It has the potential to revolutionize the economy as well as society.
With the help of Artificial Intelligence, companies are able to optimize and automate routine processes and tasks. This not only saves them time and money, but also relieves their employees, who thus have more resources for strategic tasks and decisions. Through more efficient utilization, AI-based technologies increase productivity and contribute to increased sales. Companies can use Artificial Intelligence to improve the user experience and provide their customers with individual information and offers in an end-to-end customer journey. Artificial Intelligence uses data analysis to make faster, better decisions, enabling companies to respond quickly to changing market demands.
The easier it becomes to collect and analyze large amounts of data, the more important AI-based, data-driven processes and decisions become. One of the biggest hurdles for the use of artificial intelligence, however, is the availability of data. In addition, most companies lack specific expert knowledge on how to integrate artificial intelligence into their business processes and technical personnel to implement and operate AI solutions. Another reason why only a few companies are currently using artificial intelligence is the lack of a foundation in the form of automated processes and high-performance IT landscapes. Last but not least, an ethical component and the vague fear that intelligent machines could replace and surpass humans, despite the obvious advantages of using them, also contributes to the skepticism towards AI-based technologies.
Sooner or later there will be no way around Artificial Intelligence. In the future, companies will be expected to have the people, tools, processes and expertise to make use of AI solutions. It will be crucial that AI is not seen as an individual discipline, but as an integral part of a comprehensive digitalization strategy, the aim of which is to derive the greatest possible benefit from the growing volumes of data. AI is not a stand-alone solution, but is linked to other digital megatrends such as user experience, big data and the Internet of Things. As a result, today’s companies will struggle in the future to compete with the companies that use AI-driven automation and decision making.
Many processes within the value chain can be automated using AI technologies. Simple tasks are delegated to machines – this allows employees to focus on strategic decisions while incorporating results and recommendations from AI platforms. Thus, the use of artificial intelligence helps to reduce costs and increase productivity. By optimizing business processes with ML technologies and introducing innovations, Artificial Intelligence can increase the profitability of companies in the long term.
SAP Data Intelligence is designed to develop and maintain data pipelines across systems and the data lifecycle, so you can transform your business into an intelligent enterprise.
Machine Leaning (ML) enables systems to learn from past data and adapt to new data. Machine Learning is used in many different use cases, mostly with respect to automation tasks and intelligent decision support.
Data volumes in companies are constantly growing: business-critical information and reports must be available at the right time and in the right form in order to guarantee fast and well-founded decision-making.
We help our customers to gain a comprehensive understanding of how they can exploit the potential of AI. In innovation and design thinking workshops, we develop strategies and goals. As a pioneer in value chain management, we tailor our AI offerings to your requirements and applications.
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