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This blog post is part of the SAP AI Business Services introductory–, and product portfolio series (see further published parts of the series listed below under “More Information”). 


There is hardly a topic that concerns businesses more than digitalisation. Almost every company pursues a corresponding digitalisation strategy. In addition to robotic process automation, big data and blockchain technology, artificial intelligence in particular is seen as groundbreaking for future technologies. But what exactly is artificial intelligence or machine learning and what are the differences? Which technology is the right technology to choose for my business needs? In this blog post I would like to create clarity on the various intelligent technologies and their functionalities, as well as show you the potential of artificial intelligence in solving your business needs and taking you ahead of your competitors.

Demarcation and Classification of Terminology


The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on this subject together with Marvin Minsky, Claude Shannon and Nathaniel Rochester. Thanks to increased data quality and volumes, advanced algorithms, and progresses in computing power and storage in the meantime, AI today has become indispensable for humans and businesses alike.


Artificial Intelligence (AI) is understood as a part of computer science that enables computers or robots to simulate human performance. That is typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people, enhancing human cognitive performance or replacing people on execution of non-routine tasks. Examples include autonomous vehicles and automatic speech recognition.

Machine Learning (ML) on the other side is a subset of the broader field of AI. It is the practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. Machine Learning describes algorithms that can learn from experience without having to be explicitly programmed. Instead of having a programmer write instructions on how to solve a problem, the computer learns from experience, usually in form of data. Learning in this context means optimising the parameters for a machine learning model to solve the task.

Deep Learning is a machine learning method built on a new generation of neural networks which are capable of learning better representations from data and which have pushed the state of the art in machine learning to new levels. Neural networks take inspiration from the human brain: they consist of small neuron-like computing units resembling the synapses of the brain. These networks can learn complex, non-linear problems from the input data. Deep learning networks derive their name from their “deep architectures” with several hidden layers. Deep learning networks have led to breakthroughs in several machine learning tasks and are currently the best bet in getting us closer to some of the goals of AI, for example making computers see and understand language.

Big Data is an umbrella term for technology that can process data with high volume, velocity, and variety, beyond what traditional databases can offer. The availability of Big Data is one of the driving forces behind the progress in machine learning in recent years. But not every aspect of Big Data is about machine learning.

How can Businesses make use of AI?


Artificial Intelligence hence enables computers to emulate human intelligence, while machine learning is a subcategory in which a machine is able to learn by its own without being explicitly programmed. But how does artificial intelligence actually work in practice? How can it help especially businesses in easing processes and accelerating efficiency?

No matter the industry, or size of the business – each business is interacting with literally hundreds of thousands of persons every day. Just think customer service hotlines, emails, ticketing systems or internal communication. We can agree that customer relationship management is critical to business success, but only the sheer volume and complexity of some customer interactions makes it nearly impossible to deliver excellent or first-class relationship management in all cases. And this is exactly where AI comes in. Intelligent systems such as chatbots can respond to customer queries regardless of the time, or the automation of internal processes and routine works, such as email or invoice processing can help employees do their daily jobs faster and with a smaller error rate.


Sectors such as Retail, Banking and Manufacturing are already heavily depending on AI technologies. Robots for example are used in the productions of large automobile manufacturers, and classically tasks such as welding, or painting can be automated as well. In Retail AI can help enhance the shopping experience by offering personalised experiences and help with purchase options. In the Finance and Banking sector AI techniques can be used to help ease manually intense data management tasks and identify fraudulent transactions for example. These are industries that have already realised the worth and benefit of AI, other industries are still slow on the adoption although possibilities are endless – basically wherever huge amounts of data are being processed.

AI can also help generating valuable business and market insights, thus helping management in better decisions making and making them more agile overall.

Why it Matters – AI for the Good


We have seen that the fields of application of artificial intelligence are particularly diverse today. Nearly all industries can benefit from the use of AI technologies. However, we do not consciously notice the use of AI in our day to day life.

In medicine in particular, the use of artificial intelligence is considered a success with intelligent machines already performing numerous surgical steps today, and enormously easing the evaluation of test results for example. Only recently the first AI-developed drug has made its way to testing on humans, after a development time of only 12 months compared to usually 5 years (without AI). Moreover, thanks to AI-based technologies the industry of assistive technology has advanced way beyond wheelchairs, prostheses or vision and hearing aids. For instance, visual assistive technologies are helping blind people overcome daily challenges by facilitating simple everyday tasks and breaking down accessibility barriers. Computer vision methods such as object recognition, scene understanding, Visual Question Answering (VQA) and Visual Dialogue hold great promise in making the lives of blind people much easier.

Finally, also when thinking about ways to tackle the worlds' most challenging socioeconomic challenges AI has proven its potential, with “capabilities that could help tackle cases across all 17 of the UN´s sustainable development goals”. Here, a mixture of machine learning and  IoT technologies is being used to combat wildlife slaughter in Southern Africa for example, and machine learning is being used to increase the value of renewable and sustainable wind energy. Thanks to big data is has also become easier to study earthquakes and analyse seismograms in order to determine whether the seismic activity is an earthquake or simply low-level noise. These are just few of many possible examples of why AI technologies matter, and why both humans and businesses can’t turn a blind eye anymore.

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