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AI is changing businesses.
Only those who proceed strategically will reach their goal.

AI generatedwith DALL·E 2

AI generated with DALL·E 2

What has made AI so promising in recent years?


The popularity of artificial intelligence (AI) has increased significantly since the 2020s. With the release of ChatGPT by OpenAI, interest in AI exploded. The AI chatbot demonstrated the power of LLMs (Large Language Models) and introduced an easy-to-use interface that allowed people from all walks of life to utilize and explore the benefits of this groundbreaking tool. 


This technology actually feels like it could be artificial intelligence, even if it's just predicting consecutive tokens using a probabilistic model.


Many companies are currently trying to take advantage of this groundbreaking tool and integrate it into their own offerings and services.

The increasing popularity of AI in recent times is also due to its ability to solve complex problems in a variety of industries and its integration into everyday life, from smartphones to cars and household appliances.


"Attention is all you need"

The foundation for the current AI boom was laid by Google Brain in 2017: a team from Google Brain presented an advanced deep learning model for artificial intelligence (AI) called Transformer. The paper presented was titled "Attention is all you need". Essentially, the Attention layer in a Transformer model allows it to dynamically "focus" on different parts of the input, leading to improved performance and more accurate results. 

Since then, the Transformer has become the standard for tackling various natural language processing (NLP) tasks in academia and industry. LLM base models, such as Bert, GPT model series or T5, are based on this Transformer technology.


In summary, the following points contribute to the success of AI today:


  1. Advances in technology: transformer models and attention mechanisms have significantly improved the efficiency and breadth of application of AI. Attention mechanisms allow models to filter out relevant information from large amounts of data and focus on important aspects, leading to more accurate and contextually appropriate results. These models are effective in dealing with large amounts of data and offer parallel processing capabilities that enable faster learning.​

  2. Versatility of application and advanced capabilities: AI is used in a variety of fields, from speech processing and image recognition to music composition and biomedical research. Due to the massive amounts of data on which they are trained, LLMs are developing a profound ability to generate text, ranging from simple conversational text to complex technical descriptions.

  3. Increasing accessibility and ease of use: Tools and platforms based on LLMs are increasingly user-friendly and accessible to developers and end users, leading to wider adoption and use. Base models such as GPT, BERT, T5 and others are being customized and further developed by thousands of developers worldwide. This customization is necessary to effectively tailor the models to specific applications and industry requirements. 

  4. Economic and social impact: Large language models (LLMs) and other AI technologies are having a significant impact in various sectors, from education to healthcare to customer service, underscoring their importance in the modern economy.


AI in recent years
Domain specific adoption
Domain-specific adaptation of models makes all the difference:


Each model has its own strengths, weaknesses and specific use cases. For this reason, it is essential for successful use in the company that a model is carefully selected for a corresponding use case and adapted accordingly. The way in which an LLM is pre-trained and fine-tuned makes the difference between a model with acceptable performance and a state-of-the-art, high-precision LLM.


  1. Fine-tuning: After a model has been pre-trained on general data, it is often re-trained on specific data sets relevant to a particular industry or task. This improves the model's performance in specific scenarios, such as medical diagnostics, legal analysis or customer-specific interaction.

  2. Data enrichment: Developers often add specific terminology or examples from a particular industry to the training data to increase the understanding and accuracy of the model in that area.

  3. Model customization: Changes can be made to the architecture of the model to better adapt it to specific requirements. For example, a model could be modified to better handle short texts (such as tweets) or very long documents (such as contracts).

  4. Prompt engineering: Prompt engineering involves the creation of inputs for LLMs (prompts) that effectively communicate the task to be performed to the LLM and cause it to deliver accurate and useful results.

  5. Reinforcement Learning (RL) with Human Feedback (RLHF): Method for adapting pre-trained LLMs that uses human feedback to improve their performance. There are also other approaches, such as RL with AI feedback.



Domain-specific adaptation is a critical step in maximizing the practical applicability of AI models in the real world. Developers and data scientists are continuously working to refine and adapt these models to new challenges and opportunities in different industries.


Examples for Domain specific adoptions:



  • BioBERT and ClinicalBERT: These variations of the BERT model have been specifically trained to process biomedical and clinical text. They understand medical terminology and can assist in making diagnoses, creating medical reports or extracting information from clinical trials.

  • GPT models: Can be used to generate medical reports or answer questions based on medical data.


  • FinBERT: A variation of BERT that has been specially trained to analyze financial texts. FinBERT can be used for analyzing sentiment in financial reports, fraud detection and predicting market trends.

  • GPT models: These can be customized for automated reporting, generating summaries from financial documents or developing chatbots for financial customer support.


  • Legal-BERT: A BERT model specialized in legal texts that helps to understand the complex language of laws and contracts and extract information from legal documents.

  • GPT models: Can be customized to help automate contract reviews and other legal documentation processes.

Customer Service

  • DialoGPT: A GPT-based model specifically designed for creating dialogs in chatbots. It can effectively respond to customer queries and have a natural conversation.

  • BERT for question-answer systems: BERT can be customized to effectively respond to specific queries from customers by extracting relevant answers from a knowledge base.


These models provide the basis for numerous applications thanks to their flexibility and extensibility and are often customized for the needs of a specific industry or task. By training on specialized data sets that are typical for a particular sector, they become valuable tools for the respective departments.

Best Practices for Integration

Business Change Lifecycle:

Business Change Lifecycle
Best practices for successful AI integration: 


Integrating AI into your business processes opens up a wide range of opportunities to increase efficiency, drive innovation, deepen customer relationships and boost productivity.


However, the integration of AI is not just a technical undertaking, but changes the company as a whole: people, organization, IT and processes (POPIT). A holistic approach should therefore be taken to the change process:

Business Change Lifecycle:

A holistic approach ensures that the project is aligned with the organization at all times and that its needs are taken into account. 

The project approach should support this alignment and also provide an organizational framework that provides responsibilities and accountabilities as well as project governance. 


AI projects are experimental at many points and require several iterations to deliver the desired result. An agile approach is useful from conception to implementation. Prince2 Agile provides a flexible framework that can be tailored to the needs of the project.


An AI project roughly comprises several iterative phases, from initial definition to final implementation and maintenance. These phases are critical to the success of AI initiatives and involve a series of steps to ensure that the end product meets expectations and can be effectively integrated into existing systems.


Important steps in the implementation of AI projects:

Set clear goals: Define clear, measurable goals for your AI initiatives. Think about what you want to achieve with the introduction of generative AI, be it increasing efficiency, improving customer interaction or promoting innovation.

Partner with experts: Consider working with AI experts and consultants to benefit from their expertise and ensure your AI projects are successful. Choosing an experienced partner can be critical to overcoming implementation challenges and getting the most out of the technology.

Ensuring data quality: Make sure your data is clean, relevant and diverse to ensure optimal performance. Cleaning and enriching your data set before training can significantly improve the accuracy and effectiveness of the results generated.

Selecting the right model: Choose an AI model that fits your business needs and technical capabilities. Consider factors such as integration capability, scalability and ongoing support. 

Start with pilot projects and MVPs (Minimal Viable Products): Start with small, controlled pilot projects to test the effectiveness of AI in your specific use cases. This allows you to gain experience, minimize risks and strategically plan the scaling of your AI applications.

Provide adequate training: Invest in training your team to ensure they know how to use generative AI effectively. Encourage experimentation and continuous learning to maximize your team's creativity and problem-solving potential.

Ensure compliance and ethical use: Be mindful of ethical considerations when using generative AI, especially when generating content or making decisions that affect customers or stakeholders. Keep up to date with regulations and best practices in AI ethics. Responsible use of generative AI, taking into account data protection and non-discrimination, strengthens customer trust and brand integrity.

Our Services

Our Services

Project Management

AI projects are experimental at many points and require several iterations to deliver the desired result. An agile approach is useful from conception to implementation.

Prince2 Agile provides a flexible framework that can be tailored to the needs of the project and your organization.

If you do not have enough resources available, we will support you in filling the roles with the right experts.

Customized training


We support you in integrating  AI into your existing systems and processes, taking into account current regulations regarding data protection, compliance and IT security. From data preparation to integration, we support you to ensure a smooth transition. 

We can also support you in the software development and integration of an AI application (e.g. AI Chatbot).

Invest in training your teams to ensure they know how to use generative AI effectively. Encourage experimentation and continuous learning to maximize your team's creativity and problem-solving potential.

Training also offers the opportunity to reduce fears and reservations associated with AI and create an understanding of the opportunities and risks of the technology. They help your employees to better assess the potential of AI and understand how AI applications can be used in your company.

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