Artificial Intelligence
What is Artificial Intelligence?
Generative AI: A Powerful New Technology
For anyone who wants to delve further into the topic of generative AI, the article behind the link below provides answers to a variety of in-depth questions, such as how generative AI came about in the first place, what the term generative AI actually means, what characterizes it and how it works. It also explains the challenges that need to be overcome and how companies can use it successfully.
Explainer
8 examples of Artificial Intelligence in action
Until recently, many companies have lived in a sort of purgatory of artificial intelligence (AI) development, conducting endless pilots and proofs-of-concept, but bringing very few AI-enabled projects through to enterprise production.
That’s changing fast. AI adoption has more than doubled over the past five years, according to a December 2022 survey by McKinsey. So, too, did the types of AI that organizations are implementing. According to McKinsey, on average, companies are using roughly four different AI capabilities today. Yet, many companies are still struggling to capitalize on the full business value that AI can deliver for their organizations. In Deloitte AI Institute’s fifth annual State of AI in the Enterprise research, the number of respondents who call themselves “AI underachievers” increased by nearly a third.
That’s likely in part because AI is a catch-all phrase for cognition-like capabilities, including everything from computer vision and natural language processing to deep learning and neural networks. But there are also just as many ways to get it wrong as get it right.
Here’s a dive into the details of eight enterprise AI projects, spanning a wide range of tasks, including,
Speeding drug development
Designing toy cars
Pollinating crops
Increasing efficiency in large-scale manufacturing
and much more.
Looking at what different companies are doing with AI–and how they’re doing it–can provide inspiration for others as they envision AI applications of their own.
SAP Insights article
The Promise of GenAI for Supply Chain Planners
Even minor disruptions in the supply chain can lead to significant problems in company processes and even complete production stoppages. With increasingly complex global supply chains, companies are therefore faced with enormous challenges due to unexpected supply chain stress.
The following article explains how generative AI combined with traditional AI and machine learning can help many companies improve supply chain processes and better manage problems in their supply chain. In addition to optimizing costs, increasing efficiency and productivity, this also includes running through different “what-if scenarios” to prepare for unexpected supply bottlenecks.
Illustrative fields of application show how innovative solutions and generative AI can make supply chains more resilient and sustainable. This is achieved, for example, through the use of chatbots, with the help of complex automation functions, machine learning or the integration of language models.
The basis for optimizing supply chains is always high-quality data, which is what makes forward-looking action possible in the first place. However, the ever-increasing complexity of supply chains and the associated flood of data cannot be grasped and interpreted by the human mind alone. Even experts are unable to assess the impact of certain changes. Generative AI supports decision-making. It helps to process the complex jungle of data, simulate scenarios and derive specific recommendations for action.
SAP Insights article
How Twins Are Driving the Future of Business
Digital twins can also be used to virtually run through and test a variety of application scenarios. Artificial intelligence makes it possible to take simulations based on digital twins to a new level so that business models and entire industries can be transformed.
Explained:
Digital twins
Digital twins are virtual representations of physical objects and their systems that accurately reflect their properties. These complex virtual models (including products, processes and even entire supply chains) are designed to essentially replicate the real-world entities they represent and also capture their functionality. In other words, the digital twin interlocks the digital world with the physical world and mirrors its physical doppelganger in every respect.
The article describes how companies test a variety of “what-if” models on digital twins and receive answers in seconds as to which adjustments would contribute to desired improvements. Machine learning and the use of data analysis functions also play an important role here. When implemented correctly, digital twins serve as a catalyst for strategic decisions, provide direct insight into a company's processes and drive process optimization. In factories, for example, not only can productivity and quality be increased, but predictive maintenance and AI can be used to make predictions about equipment failures, among other things.
Another example is a particularly challenging project in the city state of Singapore, in which the entire city was replicated in the form of a digital twin. This allows different scenarios to be played out using huge amounts of real-time data from different sources in order to optimize traffic or improve sustainability.
SAP Insights article
How Manufacturers Can Best Use Generative AI
Reducing downtimes is a decisive competitive factor in industrial manufacturing. Generative AI can also play a major role here in the future.
The article shows that manufacturing companies are increasingly testing and using generative AI in core functions such as product manufacturing, in addition to its use in the supply chain and customer service. For example, to accelerate the training of employees as part of training courses or during onboarding. Other areas of application range from product maintenance, improving production processes and quality control to the design of new subcomponents or entire products.