3 min.

Cognitive Computing

Data is considered the gold of our time, but its precise and adequate processing often remains unused. As part of artificial intelligence, cognitive computing refers to computer systems that are able to imitate or simulate human-like abilities. Cognitive computing offers valuable opportunities to improve and accelerate processes. As a pioneer in the field of process optimization with over 20 years of experience in the application of advanced AI technologies, we have summarized everything you need to know about the topic.
5/5 - (4 votes)

What is cognitive computing?

Cognitive means “knowledge-related” and in this context refers to the ability of human knowledge processing: computers with cognitive capabilities are able to process natural language and knowledge and make well-founded decisions.

Cognitive computing uses advanced, AI-based technologies to analyze data. One of the underlying techniques is machine learning, for example. Here, the data is searched for patterns to gain insights and rules that the computer can later apply independently.

The process of cognitive computing can be divided into various steps:

  • Data acquisition and preparation: once the data has been collected (from various sources), it must be organized and processed to create a coherent basis for analysis.
  • Pattern recognition and data analysis: Using technologies such as machine learning (ML), patterns and correlations are discovered in the data to be processed. The use of natural language processing (NLP) enables human language to be captured and analyzed.
  • Knowledge presentation: The acquired knowledge is integrated into internal models that make it possible to store and retrieve information.
  • Context understanding: Recording and understanding the context is an essential component that allows the systems to make connections and draw well-founded conclusions from the data.

Application areas & examples of cog­nitive com­puting

The fields of application for cognitive computing are diverse and the technologies can be used in any industry in which data needs to be collected and processed. They are particularly important for companies that are in direct contact with the end customer. In today’s fast-moving world, customers expect an immediate response or processing of their request. The use of cognitive computing technologies makes it possible to respond to customer inquiries in real time, whether through the use of virtual assistants or intelligent data analysis. As these technologies work without human intervention, problems can be solved more quickly. Improved customer management can help to build customer loyalty, but also to attract new customers and retain them in the company.

Cognitive computing can support the analysis of market trends in the financial sector, for example. The technologies can help to understand complex financial markets and make well-founded decisions based on large amounts of data (big data).

In the manufacturing industry, cognitive computing is used for process optimization, quality assurance and even machine maintenance planning (predictive maintenance). By applying cognitive skills and AI technologies, complex data can be analyzed, patterns identified and measures derived. This enables more efficient and precise control of production processes and increases the overall efficiency of industrial companies.

Watson computer system

The term Watson is usually associated with cognitive computing. The Watson computer system developed by IBM is world-famous because it competed against humans on the English quiz show Jeopardy in 2011. This computer was based solely on learning and was not connected to the internet. Watson symbolizes the progress made in the field of cognitive computing and shows how computers can simulate human-like thinking abilities.

One platform,
endless possibilities.

ExB is an Intelligent Document Processing platform that transforms unstructured data from any type of document into structured results. Our AI-based software can not only extract all relevant information from your documents, but also understand them. This allows you to automate your processes and save both time & money, while improving your customer experience and employee satisfaction. Win-win. 


Data processing with cognitive computing

Big data generates immense amounts of data that need to be strategically processed and analyzed. Cognitive computing goes beyond mere analysis: by integrating contextual understanding and decision-making, it is possible to react to changes, make data-based decisions and provide innovative solutions.

Cognitive computing therefore has enormous potential to revolutionize business processes. The use of such advanced technologies reduces the workload of employees and minimizes sources of error at the same time. As cognitive computing is a scalable system, it can be easily adapted to growing data volumes.

Cognitive computing faces these challenges in practice

One of the biggest challenges facing cognitive computing is unstructured data: As much of the information received by a company is usually in unstructured form, processing and analyzing such data requires advanced methods or data extraction and interpretation. However, data protection, ethics and the complexity of algorithms are also among the challenges in the field of cognitive computing.

Cognitive computing & ExB

Like cognitive computing systems, ExB can automate workflows that involve unstructured data. With more than 20 years of experience, we are one of the pioneers in the field of AI and document processing in particular. Get in touch with us without obligation and talk to our experts: We offer customized document processing solutions for every use case.


Written by:

Dr. Ramin Assadollahi

CEO & Gründer ExB

Dr. Ramin Assadollahi is a computational linguist, inventor and clinical psychologist and is considered one of the AI thought leaders in Germany.
Stay up to date:

Was this article useful?

5/5 - (4 votes)

These articles might also interest you

Document processing

Automation, cloud computing, robotics and artificial intelligence characterise the use of new technologies. Robotic process automation (RPA) in particular is growing due to its easy integration and applicability in various business areas. In contrast to physical industrial robots, RPA automates business processes and tasks with the help of software robots or bots. These emulate human interactions with the user interface and perform tasks in computer systems. RPA systems can automate repetitive, rule-based tasks that are normally performed by employees.

Process automation

Intelligent automation (IA) plays an important role in the constantly changing business world: it is an innovative technology that makes it possible to combine human expertise with artificial intelligence (AI) in order to efficiently optimize tasks, workflows and processes. Intelligent automation has the potential to fundamentally change business processes. At ExB, we recognize this opportunity and would therefore like to introduce you to the concept of intelligent automation in a practical way.

Document processing

Data is the fuel of our digital world. With the advent of artificial intelligence (AI) and machine learning (ML), efficient data extraction is more crucial than ever. Data extraction enables the processing of unstructured information and improves various operational processes. As a pioneer in the field of intelligent, AI-based document processing, we would like to offer you a comprehensive insight into the topic of data extraction and answer the most important questions below.

Free download:

Whitepaper: The future of logistics

Find out how Intelligent Document Processing (IDP) is revolutionizing the supply chain.

Our white paper covers:

  • Current challenges in logistics
  • What is IDP?
  • Advantages of IDP in logistics
  • Use cases from practice
  • Pitfalls and challenges


Download your free copy of the white paper right here and revolutionize your supply chain with the help of AI!

Free Download:

Whitepaper is AI worth it?

Seven typical questions about AI answered:

  • Can AI help us digitize our well-rehearsed processes?
  • Are there already AI solutions for administrative processes?
  • What is the difference between OCR and AI?
  • What is the difference between rule-based and AI solutions?
  • Can historical data be used for training?
  • Does AI-supported document processing always have to be expensive?
  • How do you calculate the costs and ROI of an AI project?


Download your free copy of the whitepaper right here and find out the answers to these questions!