SAP RoadmapSAP S/4HANA CloudSAP Implementation - GreenfieldSAP Industry SolutionsRISE with SAPGROW with SAPSAP for new customersSAP Central FinanceSAP Analytics CloudSAP software for your supply chainSAP SuccessFactors HCMSAP EWMSAP Fiori app developmentSAP developmentSAP SecuritySAP Data TransformationSAP data migrationSAP AMSChange ManagementEducation Management
What is Artificial Intelligence (AI)?
Artificial Intelligence is not new, but it has been getting new attention since end of 2022 with the introduction of ChatGPT. Read here what you need to know about AI.
Artificial Intelligence - Basic knowledge, application areas and trends
Artificial Intelligence - Basic knowledge, application areas and trends
Is this the final breakthrough for Artificial Intelligence?
With the AI-based chatbot ChatGPT from the Californian start-up OpenAI, the topic of AI has received a lot of attention since the end of 2022.
With ChatGPT - it seems - the topic of AI has become suitable for the masses. Users simply call up the provider's site, enter their question, and receive an answer within seconds.
This development is attracting the attention of the tech giants:
- Microsoft has been cooperating with OpenAI since 2019 and has already integrated ChatGPT into its own search engine.
- Apple restricts the use of the tool for its own employees and is working on its own solution.
- And Google is holding its own with its own chatbot Bard, which was launched in March 2023.
But AI is not just for end users. Currently, numerous useful AI functions are already making their way into companies - including ERP systems such as SAP S/4HANA.
Experts agree that companies can tap into enormous potential with the help of AI.
- Many errors can be identified before they occur.
- Companies can optimize customer relationships, logistics chains and flows of goods.
- In addition, complete processes can soon be automated.
But what exactly is behind the term Artificial Intelligence? How long have AI applications been available and why are they becoming increasingly popular? What can AI already do today and where are the limits?
What do terms like "machine learning" and "deep learning" mean? How will Artificial Intelligence change companies and society in the future? And how does SAP deal with artificial intelligence?
On this page you will find answers to these and many other questions about AI.
Artificial Intelligence: Definition
Artificial Intelligence: Definition
Artificial Intelligence (AI) is a discipline of computer science. In Germany, the synonym Artificial Intelligence (AI) is also used.
AI deals with methods that enable machines (computers) to solve tasks in the same way as a human would do with his intelligence.
AI therefore encompasses not only aspects of information technology, but also psychology, neuroscience, linguistics, communication sciences, mathematics, and philosophy.
Computer science is thus more of a means to an end. It brings the various disciplines together and enables implementation.
In order for AI to be able to solve tasks independently, it must be trained. To do this, experts use special algorithms and provide the AI with training data. Through constant training, the AI continues to improve - until it can solve the set tasks itself.
There are basically two or three types of Artificial Intelligence:
1. Autonomous and automatic completion of tasks (weak AI)
The most widely used AI today is a "Weak AI", a weak Artificial Intelligence.
Solutions such as intelligent software assistants, Internet search engines, self-driving cars or voice recognition systems already work with AI and are increasingly finding their way into our everyday lives.
Although corresponding systems have already achieved enormous performance, they are called weak AI in technical jargon.
The reason for this is that weak AI solutions operate at a relatively superficial intelligence level and do not develop a deep understanding of problem solving.
2. Imitation of human thinking and behavior (strong AI)
Strong AI (also called "Strong AI") aims to match or exceed human intellectual capabilities.
Systems with strong AI can recognize patterns and learn. Most importantly, they can apply the knowledge they acquire to many new tasks not covered by existing algorithms.
Strong AI acts actively and flexibly and on par with humans. However, systems with strong AI do not currently exist.
3. Artificial Superintelligence
Systems with "artificial superintelligence" theoretically have a consciousness and human characteristics such as emotions.
To date, it has not yet been possible to develop such a superintelligence. In research circles, there is controversial discussion about whether the development of such an AI is even feasible.
Difference between Artificial Intelligence, Machine Learning and Deep Learning
Difference between Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence, Machine Learning and Deep Learning are different.
- Artificial Intelligence (or Artificial Intelligence) is an umbrella term that encompasses technologies and methods that enable machines to mimic human thinking.
- Machine Learning is a subfield of AI that stands for the ability of machines to continuously improve themselves with the help of pattern recognition and training data.
- Deep learning, in turn, is a subfield of machine learning. Here, the machines train themselves with the help of multilayer neural networks and can solve highly complex tasks.
Let's take a closer look at the approaches below to further highlight the differences.
Machine Learning
Machine Learning
Machine learning describes mathematical methods that enable a machine to independently generate knowledge from empirical values.
Or to put it more simply: Machine Learning lets computers do useful things without first programming them to do so. Only the algorithm is programmed.
From a technical point of view, machine learning uses algorithms (calculation rules) that can independently recognize regularities and patterns in data. To do this, they must first be provided with extensive data and trained.
In the development phase, a programmer ensures that the machine learning model is continuously adapted and optimized.
In this way, the algorithm becomes "smarter" from data set to data set through pattern recognition until it can finally perform its task independently with new and unknown data.
The main goals of machine learning are,
- to recognize correlations,
- to link data intelligently,
- to draw conclusions and
- make precise predictions.
In the business environment, machine learning applications have the potential to relieve employees of tedious, unproductive tasks. This frees up resources for new areas and makes work more efficient and economical.
For example, learning software can independently scan paper documents, recognize the text, initiate further steps and organize archiving.
A more complex scenario in which machine learning is already being used today is predictive maintenance. The algorithms used are able to detect possible damage to technical equipment and error patterns at an early stage in order to request maintenance if necessary.
Voice recognition on cell phones, spam filters in e-mail inboxes, and even facial recognition in photo management are all controlled in significant parts by machine learning algorithms.
Often, we humans are already in contact with machine learning today without even knowing it. This is the case, for example, when we are shown personalized advertising.
Deep Learning
Deep Learning
Deep Learning is a subfield of Machine Learning. It is a special method that uses so-called "deep" artificial neural networks (Deep Neural Networks or DNN) and huge amounts of data to learn particularly efficiently.
The method is based on learning processes in the human brain. Based on the information available, corresponding systems can repeatedly link what has been learned with new content and thus continuously learn new things.
At a certain point, the machine is then able to provide forecasts, make independent decisions and question them. If the result of the decision is not satisfactory, it is adjusted in a new attempt.
Humans do not normally intervene in this learning process. This is also the essential difference to Machine Learning.
Deep Learning is particularly suitable for scenarios in which large amounts of data are to be examined for models and patterns. Examples of applications here are also speech, object or face recognition.
In speech recognition, for example, Deep Learning makes it possible to independently expand the vocabulary of systems with new words and word variants. Other areas of application include autonomous vehicles or robots, AI in computer games, or a prediction of customer behavior as part of CRM solutions.
What is ChatGPT?
What is ChatGPT?
ChatGPT is a chatbot from OpenAI, a US-based company involved in AI development and research.
Through ChatGPT's interface, users can enter their questions and receive answers generated by the chatbot. The possible uses are many:
- from the creation of a wide variety of texts
- to checking spelling,
- from the rephrasing of given texts to the listing of questions
- to listing questions on a specific topic.
OpenAI's language model has been and is being trained with real texts (based on texts up to the year 2021) and is constantly improving. However, the answers are not always error-free, as many examples have already shown.
Development and use of Artificial Intelligence
Development and use of Artificial Intelligence
Since its emergence, Artificial Intelligence has made enormous leaps in development. This is especially true for the recent past. In the management of corporations, AI is currently a topic of the future, in which the potential is seen to fundamentally change companies and society.
Through the use of AI, opportunities arise to optimize one's own business and even to reorganize entire industries. But how did Artificial Intelligence come about and what forms have corresponding systems actually reached to date?
History: The beginning of Artificial Intelligence
History: The beginning of Artificial Intelligence
The origins of Artificial Intelligence date back to the 1950s. As early as that decade, the term was used at a science conference in the United States. Scientist Marvin Minsky is considered one of the founding fathers of AI.
In 1966, Minsky defined AI thus: "Artificial Intelligence is the science of making machines do things that would require intelligence if done by men." Loosely translated, this means artificial intelligence is when machines do things that require human intelligence to do.
Also, an early milestone in AI was the Turing Test. Developed in the 1950s by British mathematician Alan Turing, it was designed to allow a human to communicate synchronously with other humans and a machine via chat software.
In the 1960s, the so-called "General Problem Solver" was presented. This was an AI system that could solve simple problems. In the same decade, the ELIZA software caused a sensation. At the time, the chat system made it possible to simulate therapy conversations.
In 1996, a computer beat the then reigning world chess champion Garri Kasparov. In the following decades, the capabilities of Artificial Intelligence improved continuously. This was largely due to steadily improving memory capabilities and computing power.
In 2011, IBM's Watson software was introduced. It was able to win the quiz show Jeopardy against human opponents. AlphaGo is another AI program that achieved the feat, previously considered nearly impossible, of beating the world's best professional Go player in 2016.
Today's use of Artificial Intelligence
Today's use of Artificial Intelligence
Artificial intelligence is now present in both our personal and professional lives. A classic example is speech recognition, which has gained notoriety through applications such as the voice assistants Siri (from Apple) and Alexa (from Amazon). In addition, work is being done on translation systems that translate conversations live into another language.
In addition, for some years now we have encountered so-called chatbots, which are used by companies to increase the efficiency of customer communication.
This is software that can conduct a dialog with users through extensive databases, linguistic knowledge and a full-text search engine. In English-speaking countries, chatbots already act as contact persons for medical questions.
Another important area of application for Artificial Intelligence is predictive analytics. Here, algorithms create predictions for the future development of customer relationships, results and business processes on the basis of historical data from a wide range of sources. The financial sector uses corresponding applications in claims management and for fraud detection.
Predictive analytics can also be used to optimize the planning of marketing campaigns. For example, algorithms can predict which customer will buy a particular product, at what time, and where.
AI applications are also used in service management. Here, for example, Artificial Intelligence takes over the categorization of incidents and the management of support dialogs. In addition, support staff can be assisted in analyzing and solving problems, provided a suitable knowledge database is available
In IT security, AI has also led to significant improvements. Here, AI-based solutions now make it possible to identify attack patterns and prioritize security incidents. Security systems are continuously evolving with machine learning methods by collecting, analyzing and classifying threat data.
What does Artificial Intelligence mean for SAP?
What does Artificial Intelligence mean for SAP?
SAP's current direction suggests: The Walldorf-based company wants to position itself as a software provider that makes intelligent companies possible with its products.
AI - in addition to the focus on cloud ERP systems - is a central element for SAP on this path.
With Artificial Intelligence, SAP aims to automate all business processes and improve the user experience, among other things - for all end-to-end processes from Lead-to-Cash to Design-to-Operate to Recruit-to-Retire.
Since 2023, SAP has been stepping up its efforts to integrate as many AI solutions as possible into the entire portfolio and thus make them usable. SAP has introduced the generic term SAP Business AI for this purpose.
Currently (as of May 2023), SAP offers over 130 AI application scenarios that are already integrated into SAP software. According to SAP, these solutions are already being used by around 23,000 customers.
In the future, the company will also focus on Generative AI. This form of AI stands for systems that can be used to generate something new based on data (for example, texts, images or videos).
One of the long-term goals is, among other things, an ERP solution that can be operated primarily by means of speech recognition.
For this purpose, the integrated assistant called "CoPilot" was already introduced a few years ago. It enables users to interact with the system by voice (or keyboard and chat function) and, for example, to search for information.
In purchasing and sourcing, for example, voice-based elements are intended to speed up and simplify purchase requisitions. Sales staff, on the other hand, will be able to convert offers into orders via voice command.
Another vision is an intelligent assistant that is able, for example, to understand the agenda of meetings and automatically provide the appropriate key figures. The corresponding software should learn from the user's habits and adapt intelligently on this basis.
Another important area of application for artificial intelligence in the SAP environment will be the automation of processes. This will result in enormous savings for companies.
Current AI solutions from SAP.
Current AI solutions from SAP.
SAP already has a large number of AI and machine learning applications in its portfolio - and for almost every business area and core business process. Artificial Intelligence and Machine Learning are already integrated in the SAP product SAP S/4HANA.
For example, these include:
Lead-to-Cash Process
- SAP Emarsys Customer Engagement: predicting customer purchase intent with AI lead scoring and increasing online sales with personalized product recommendations
- SAP Sales Cloud: improve forecast accuracy with automated pipeline health assessments
- SAP CPQ: improve close probability with discount recommendations
Design-to-operate process
- SAP Integrated Business Planning: predict demand with intelligent demand assessment and automatically classify and categorize quotes with intelligent segmentation
- SAP Predictive Asset Insights: Efficiently complete maintenance tasks with object part and damage code suggestions
Source-to-pay process
- SAP Fieldglass Solutions: Finding external employees with intelligent resume screening
- SAP Ariba solutions: Correctly classify invoices with automated data enrichment services
Recruit-to-Retire Process
- SAP Fieldglass Solutions: Find the best candidates with intelligent resume screenin
- SAP SuccessFactors solutions: Tailored training recommendations to improve training and talent development
Finance
- SAP Cash Application: Automation of receivables reconciliation
- Intercompany matching and reconciliation (ICMA): Simplify the financial close process by identifying and resolving discrepancies between intercompany transactions
Ethical principles already defined.
Ethical principles already defined.
Artificial Intelligence is accompanied by enormous societal changes and privacy challenges. SAP has already addressed these "downsides" of AI in depth as well.
For example, the software group has developed guidelines to guide the introduction and development of AI components. The overarching goal here is to "improve the operations of the global economy and the lives of people."
The principles include the following aspects:
- Value-driven (respect for human rights and UN Guiding Principles).
- Focus on people and user experience
- Businesses acting without prejudice
- Transparency and integrity
- Quality and security
- Data protection and privacy
In addition, SAP aims to address the societal challenges posed by AI. Aspects such as economic redistribution, economic development, social security and normative issues play a role here.
Ethical and moral issues in the field of artificial intelligence.
Ethical and moral issues in the field of artificial intelligence.
Several ethical dilemmas arise in the context of artificial intelligence.
- For example, there is a critical need to question whether decisions made by autonomous machines may pose a threat to free will and the assumption of responsibility.
- In addition, developers may imbue AI software with tendencies that lead, for example, to the exclusion of individuals or to discrimination. This circumstance is particularly problematic if these tendencies arise unintentionally in the context of machine learning.
- Further, there is a risk that people can be identified, categorized, and evaluated through profiling algorithms based on their activities, preferences, opinions, or other information they share or generate online. This could put cultural and political pluralism at risk.
- In addition, AI systems need vast amounts of data to meaningfully undergo learning processes. This includes personal data. Current data protection laws are in considerable conflict with this.
- However, the large amount of information poses further challenges. For example, it is sometimes difficult to filter out correct and error-free information. If the database is not of undoubtedly high quality, software results and decisions cannot be trusted to a high degree.
Outlook: Where is the trend for Artificial Intelligence heading?
Outlook: Where is the trend for Artificial Intelligence heading?
Artificial Intelligence is undoubtedly a highly emotional topic. Extreme positions often dominate the discussion.
One camp sees AI as a threat to all of humanity, while the other often sees the technology as a panacea for all of our problems.
No one can yet judge whether one of these scenarios will come to pass. The fact is, however, that AI will become significantly more important in the coming years and decades. Experts agree that Artificial Intelligence is a key technology of the digital revolution.
For companies that want to make progress in terms of digitization, there is therefore hardly any way around using the possibilities of Artificial Intelligence for themselves and driving forward automation via AI. SAP already offers a wealth of opportunities for this.
- It is very likely that employees will be relieved of routine activities by AI in the future. In particular, standard processes that are repeated with high frequency are the potential area of application.
- However, machines will not only automate processes in the coming years. There is also enormous potential in the analysis of big data. In contrast to classic approaches, which are based purely on the evaluation of past values, AI enables a look into the future.
- On this basis, precise decisions can be made and new business models and smart services (such as predictive maintenance of machines) can be realized.
- The optimization of goods flows and logistics chains is another area of application in which AI is likely to become established.
And where does that leave humans? Will they soon be completely replaced in the working world by robots with Artificial Intelligence? Certain job profiles could indeed disappear as a result of AI, but at the same time new jobs will be created.
Human skills such as creativity and empathy could once again come to the fore. There will be freedom to concentrate on one's own strengths again and to develop innovations. This also has a positive effect on employee satisfaction.
The intuition factor will also continue to be in demand. Decisions will be based much more on data from intelligent analyses. However, if the decision-making scope is high, the human being will still have the last word for the time being.
Conclusion: Opportunity and risk at the same time
Conclusion: Opportunity and risk at the same time
There is an opportunity for AI to evolve positively and empower people to better solve the problems of modern society and achieve more.
There is a risk in allowing AI to act beyond the limits of meaningful control. For companies, such an approach would be unacceptable, and not just in terms of reputation and ethics.
Management would potentially delay or even stop innovation entirely if it failed. The degree of security and control in the use of Artificial Intelligence could therefore determine whether intelligent machines become a curse or a blessing for us.
