Excellent data management combined with artificial intelligence is an unbeatable team. AI helps to process and analyze large amounts of data. To be able to decide where the biggest potentials for promising artificial intelligence applications are in a value chain, you need to understand and evaluate them.
Find out how here, when and why data analytics should be upgraded with artificial intelligence.
To use data better and more intensively and to introduce data-driven decision-making processes is a goal of many companies. With the help of AI applications, new products and services as well as data-based business models are already emerging today.
Experts from McKinsey forecast that the EU could increase its economic output by 19 percentage points by 2030 through a consistent focus on artificial intelligence – without any negative impact on the job market.
Intelligent systems support more efficient production processes, reduce energy consumption in systems, predict malfunctions or arrange day-to-day business in a more resource-efficient way. In the Digitalization Index for SMEs 2020/21, 31 percent of the companies surveyed said they expected disruptive changes (triggered by AI) in their industry. 77 percent would like to improve their service and product quality with the use of corresponding algorithms and thus strengthen their competitiveness.
What is Artificial Intelligence?
Artificial intelligence (AI for short) is a key technology and crucial for the success of companies – regardless of the industry. Human learning and thinking is adapted to mimic these skills and behaviors in an efficient and scalable way.
For example, insights can be quickly derived from data that is no longer comprehensible to a human due to its complexity or quantity. Decisions that were previously made on the basis of "gut instinct" under time pressure can be supported with data and facts or even made completely autonomously in this way.
Good to know: By 2025, the German government expects more than one-third of a company's value creation to be done by algorithms based on artificial intelligence.
Artificial intelligence is basically divided into two levels
Weak artificial intelligence
Weak artificial intelligence refers to systems that have developed extraordinary capabilities in a limited area, but are unable to evolve beyond that (e.g., Digital Assistants, Automated Data Analysis for Process Optimization, Alexa and Siri, etc.).
Strong artificial intelligence
Strong artificial intelligence refers to systems that have human characteristics in many aspects. In terms of independently recognizing problems, learning new skills or transferring what has already been learned to new circumstances.
While weak artificial intelligence has made major progress in recent years (complex issues such as customer traffic in stores can be accurately predicted by Google), strong artificial intelligence is still a long way off. Although there is agreement among most experts that strong AI is possible, it is more likely to be realized in the next few decades than in the next few years.
Why Is Artificial Intelligence Needed?
Artificial intelligence and machine learning are playing an increasingly important role in companies. There are many possible ways in which they can be used. They offer increased productivity and efficiency as well as new business values. The best-known example are chatbots in customer service, which act as virtual employees and enable 24/7 processing of customer inquiries.
In companies today, artificial intelligence is primarily used when repetitive tasks (i.e., recurring, rule-based activities) need to be automated. Accelerating data analyses with complex decision-making processes is also part of this.
AI can support companies in a wide range of strategic objectives and contribute significantly to the achievement of these goals:
- Increasing revenue and business growth
- Increasing cost efficiency
- Improving market positioning
- Developing and implementing innovative business models
- Improving decision-making (in terms of quality, efficiency and innovation)
- Increasing customer satisfaction
- Improving working conditions and work performance
- Increasing the productivity of employees
- Reducing empty times, rejects and downtimes
According to IDC, 94 percent of the surveyed companies are convinced that artificial intelligence offers a significant competitive advantage. The decisive factor here is deriving the right use cases.
Before you introduce AI, you first need to be clear about the specific business case you want to use it for and what you hope to gain from it. First, identify a central area that artificial intelligence can sustainably optimize and thus contribute to the company's success.
The use of AI solutions lends itself to all areas such as marketing, sales, logistics, customer service, etc. Learn more about the possible uses of AI – with exciting application examples, we show you what artificial intelligence can already do in everyday business.
How Artificial Intelligence Is Changing the World of Work
The widespread use of artificial intelligence in companies has now established itself as a global trend. On the one hand, companies are constantly exposed to high cost and competitive pressure. On the other hand, the processing power of computers is faster than ever before and vast amounts of data are now available.
Once the prerequisites for artificial intelligence have been created in the company, significant potentials arise for the implementation of strategic goals:
Intelligent analyses of large amounts of data enable the generation of accurate forecasts and the derivation of valuable decisions
- Quality, efficiency and innovative power of decisions increase
Efficient end-to-end processes
Intelligent analytics improve resource utilization and asset effectiveness and make useful predictions about maintenance requirements
- Cost efficiency increases
- Blank times, rejects and downtimes decrease
Excellent Customer Experience
Intelligent analytics enable a comprehensive 360-degree customer view, outstanding 24/7 interaction and hyper-personalization
- Customer satisfaction and sales increase, company growth benefits
Profitable business ideas
Intelligent analyses quickly and efficiently uncover market gaps and identify outstanding services or products
- Development and implementation of innovative business models are accelerated, positioning in new markets is advanced
Intelligent analyses and smart process automation take over routine, recurring work activities.
- Working conditions and work performance of employees are improved; employee productivity increases
If companies actively deal with the use of artificial intelligence, it quickly becomes clear how complex the influencing factors can be and how significantly different business processes are affected across departments.
It is important to first identify and prioritize the overarching business goals in order to achieve the maximum benefit from the use of artificial intelligence in the company. Only then measures can be derived in a target-oriented manner and the use of AI can be driven forward with suitable tools, optimized processes and expert advice.
How Is Artificial Intelligence Being Used?
AI applications can do much more than efficiently evaluate large amounts of data. Artificial Intelligence offers companies the opportunity to automate processes and thus save costs and work more efficiently.
There is (to say the least) still potential for catching up and improving in terms of the design and implementation of AI scenarios in companies. This field has changed a lot in recent years and has developed rapidly.
Artificial intelligence has the potential to fundamentally change our working world. These three fields of application have already found extensive use:
Digital language and text processing (Natural Language Processing)
Natural Language Processing is used to automatically understand the content and context of texts and speech or to generate them
e.g. chatbots, text processing, text mining and voice assistants
Robotics and autonomous systems
Artificial Intelligence provides the key technology to enable autonomous machines to act on their own, solve complex tasks, and respond to unpredictable events
e.g. vehicles, machines, devices or software systems
Pattern recognition in large data sets
AI analyses are used to identify patterns in events that occur significantly often or rarely together or sequentially in the data
e.g. Predictive Maintenance, Shopping cart analysis, fraud or manipulative behavior, healthcare diagnostic systems
AI Use Cases in Companies
Artificial intelligence offers companies the opportunity to automate repetitive tasks, comprehensively analyze large volumes of data, make forecasts and predictions, recognize patterns in data and information, and derive recommendations for action. This does not require turning the entire corporate structure upside down. Small adjustments are often enough. And a slight increase in productivity can already mean a huge payoff.
The specific use cases of artificial intelligence in companies are diverse:
Production plants or machines often go through regular maintenance cycles. With the help of sensor-based AI systems, these cycles can be optimized. On the basis of automatic classifications, wear and tear or incipient defects are predicted or detected at an early stage. Machines are thus only maintained when necessary – not proactively. Downtimes are minimized.
The digital age makes it possible to almost completely monitor the supplier market for individual products. For example, all current prices for electronic items can be identified via price search engines. If the big players adjust their prices, it can be observed that smaller retailers react to these price adjustments within hours. The goal: to be taken into account by the consumer or, if market prices are too low, to deliberately avoid demand.
This is possible for a large number of items, as identical items are easily identifiable via product numbers. However, for items such as clothing, this is difficult to realize. While similarity can be easily assessed by humans, it is difficult for simple algorithms. Artificial intelligence can help here. By automatically comparing item properties and images, similar items can be identified.
Another typical use case for artificial intelligence is the subdivision of customers into different customer segments. AI is significantly more powerful than the classic ABC analysis, in which customers are simply divided into segments according to a central metric.
A considerable number of key figures or customer characteristics can be included in the analysis with the help of artificial intelligence, and additional information can be provided for a suitable or targeted approach. Ideally, the result of the customer segmentation is automatically fed into the campaign management tool and updated when new contact is made with the customer. This means that the campaign still has to be designed by the marketing manager and assigned to a customer segment. The customer approach is thus automated.
Artificial intelligence can help to predict the ability of customers to pay. Data on customers who did not comply with their payment requests in the past can be used as a basis. Based on this data, AI-supported algorithms predict the willingness of new customers to pay. This information can then be used in the online store, for example, to automatically restrict the payment terms of the new customer.
Today, information is often available in keywords or individual data points. For example, stock market prices are simple lists of prices at specific points in time, news agencies, provide news as collections of keywords or articles are initially provided by the manufacturer as descriptions for individual properties, such as color or material.
Artificial intelligence enables the generation of complete continuous texts from individual terms that are easy for the user to understand. Web pages on the development of stock market values, as well as summaries of soccer matches or detailed product descriptions, are already generated today using appropriate methods. They are only reviewed by humans before they are made available to the consumer.
Customer care, or helping the customer make a purchase, is the most common AI application. Today, it is possible to analyze how a prospect or customer interacts with a company across all touchpoints.
This information is used to specifically serve or address customers as well as to uncover and improve the weak points in the customer interaction. Customer satisfaction thus increases significantly.
In addition, transparency about customers and prospects is created, which strategically creates far-reaching added value throughout the entire customer journey.
Artificial Intelligence Use Cases in Practice
AI is changing processes across all industries and areas of responsibility. AI use cases are possible along the entire value chain in companies:
- Automation of quality control
- AI-based route planning
- Optimized warehouse utilization
- Defect or anomaly detection
- Automation of quality control
- AI-based assistants (e.g. data glasses) for employees
- Further development of smart products for new business models
- Optimization of the supply chain
- Intelligent sales forecasting
Purchasing and procurement
- Automated warehousing through autonomous vehicles
- AI-based processing from order transaction to delivery
Service and customer management
- Automated customer review analyses
- Intelligent customer interaction (automated CRM)
Research and development
- AI-based simulation of product behavior
- Analyses for product development
- Automation of market analyses
- Personalized customer interaction
- Dynamic optimization of the product portfolio
- Digital assistants in the sales process
- Real-time market analysis
- Support of presentation and sales process
Artificial Intelligence and Data Management – The Importance of Fundamental Data for the Implementation and Operation of AI
In the past, only transactional data was collected, such as the sales value of a purchase, a customer's complaint, sales figures for a product group. Nowadays, movement and behavioral data is also collected: the click behavior of users on websites, the geographical position of users during the ordering process, the time of day at which a user seeks interaction or the voice pitch with which a voice assistant is addressed.
Huge amounts of new data are generated and stored by companies every day. In our increasingly digitalized world, it has become a considerable success factor for companies.
The requirement: excellent data analytics – whoever analyzes data in a targeted manner, derives important insights and exploits potential, wins.
Artificial Intelligence Needs Data
According to the IDC study "Data Age 2025", the global volume of data will continue to grow significantly. By 2025, the analysts expect exponential growth to a data volume of 175 zettabytes.
It is also predicted that 2/3 of this data volume will originate from enterprises in 2025. A foundation for successful AI applications would thus be created, as these are based on machine learning processes that require large amounts of data. The challenge is to keep the data in the necessary quality.
Economic success of AI projects is inseparably linked to high data quality. For this reason, comprehensive data management with a strategic focus belongs on the business agenda of every company.
Data must be properly collected and ideally prepared in order to generate correct and meaningful analyses and to derive useful forecasts. This is the only way for companies to exploit the hidden potential of data as a raw material.
Modern master data management (MDM) solutions, customer data platforms (CDP) and product information management systems (PIM), are a major asset. These modern systems for data management create the basis for making a large volume of master and transaction data as well as Big Data accessible for AI analysis.
Machine learning methods support data management. However, data management is also the precondition for machine learning. An AI system can only be as intelligent as the data it is based on. Bad data = bad results. This is a challenge that many companies still have to face.
In projects with Artificial Intelligence, an average of 80% of the processing time is invested in the collection and aggregation of data. This proportion can be significantly reduced by appropriate data management. Tackle your data strategy now. Our whitepaper shows you the ideal roadmap for planning, implementing and ongoing management.
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Artificial Intelligence Needs Rules
Anyone who uses a technology such as Artificial Intelligence in a company bears responsibility. In the interaction of strategic data management and AI-supported data analysis, large volumes of data can be processed quickly and effectively, and data science can be used to its full potential.
However, collected data is subject to strict legal regulations, for example to protect the interests of a natural person. Data must not be processed randomly. Data governance introduced throughout the company protects against legal risks. The data governance strategy defines guidelines, responsibilities, and standards for data-related processes – also for the use of AI.
For trustworthy Artificial Intelligence solutions, it is important to regulate the accompanying processes consistently. This enables companies to achieve the best possible quality, performance, data security and user acceptance.
A powerful team: Big Data, Data Management, Data Governance, Artificial Intelligence
Forms of Artificial Intelligence – Three Terms Explained
Although the term " Artificial Intelligence" is quite old, it is not clearly defined. This is due to the fact that the word intelligence itself is not clearly delineated.
Usually, Artificial Intelligence is understood to mean a system that replicates human behavior. In the most simple case, Artificial Intelligence can be based on a rule system that implements predefined decision rules.
Artificial Intelligence can also include systems that independently monitor the environment, develop behavior based on the monitored data, observe the reaction of the environment to its own behavior, and make decisions again for future situations based on the observations.
Today, Artificial Intelligence usually subsumes systems that use machine learning to recognize (learn) patterns on historical data and apply what they have learned to current data. A current trend is not to learn patterns once and apply them until what is learned becomes outdated, but rather to learn continuously and adapt the model to new data on an ongoing basis. Machine learning identifies and abstracts patterns and regularities in the learning data.
Deep Learning is a subfield of Machine Learning. Deep Learning refers to modeling approaches with artificial neural networks that have at least one intermediate layer called a "hidden layer" in addition to an input and output layer. More complex models, especially in the area of image recognition or text processing, have architectures with dozens of such hidden layers. In addition, there are architectures deviating from feed-forward networks with more complex interconnections between individual layers and knots.
Frequently Asked Questions About Artificial Intelligence
Artificial Intelligence as a term was shaped in the 1950s. Due to the rapidly increasing processing speed of computer systems at that time, people were sure that they would be able to develop algorithms within a short period of time that would eventually prove to be strong AI systems. After initial enthusiasm for this topic, the lack of success quickly led to disillusionment.
The following epoch with little interest on the part of the research community is generally referred to as the "AI winter".
It was not until the increasing processing speed of computers, improved software and the ubiquity of data that the topic of artificial intelligence experienced a renaissance around the turn of the millennium.
The learning of Artificial Intelligence begins in principle with the Data Scientist's understanding of the Data Science problem to be solved and the ability to recognize data that contribute to the solution of the problem.
Required data is obtained by the Data Scientist, cleaned and aggregated if necessary. Subsequently, a model is created. Usually, training and validation is performed on historical data used for learning the patterns and relationships.
In the training step, the model parameters are adjusted in such a way that the results of a training data set can be optimally predicted. Subsequently, the model is validated using a test data set whose data was not used to train the model. This ensures that the model will produce valid results when new data is used in the future.
If the quality of the model is satisfactory, the AI solution is ready to be deployed and can be provided as a service or integrated into dashboards. If the quality is not sufficient, the process starts again. If necessary, additional data or other models are used or the question is adapted.
In short: The learning of an Artificial Intelligence is the interaction of human intelligence, a lot of data and the selection of a suitable algorithm.
Artificial intelligence is essentially about imitating human behavior. The focus is on replicating human perception, learning from historical data and imitating human decisions
One of the strengths of humans is their ability to quickly separate the important from the unimportant. For example, people immediately recognize other people in pictures and can quickly assign them to people they know. A subfield of Artificial Intelligence is concerned with replicating this behavior in various areas. The focus is on image, speech and text processing.
Understanding and learning
The strengths in human perception are the result of "lifelong learning". How this works is not yet fully understood. Artificial Intelligence follows different approaches to replicate this, such as machine learning, reinforcement learning or even crowd sourcing.
The goal is to use past experiences to recognize patterns with a computer-based system and make them usable for the future. AI systems learn through feedback loops during their use.
If appropriate patterns have been learned, they can be used to act in future decision-making situations. A distinction is made here as to how strongly behavior is intervened in.
With predictive analytics, the human decision-maker is presented with data and figures based on which he or she can make decisions. Expert systems go one step further. Here, Artificial Intelligence acts as an advisor, making a proposal for the decision that the human must accept or reject. In robotics, artificial intelligence makes simple decisions without human intervention.
Do you have plans for AI in your business?