AI Consulting

What is artificial intelligence and why should companies use it?

Artificial intelligence (AI) has established itself as a central topic of the digital future and is attracting more and more attention from science, business and media. This technology is now firmly anchored in our everyday lives: With dialog systems such as ChatGPT Extensive texts can be generated, the pioneers of self-driving cars are already active in traffic and autonomous drones are being used successfully in the logistics sector.

In the operational sector, AI develops its greatest strengths: It optimizes processes, reduces redundant tasks and enables targeted and resource-saving use. This saves time and reduces waste, which creates more space for creative activities, but also allows employees to focus on demanding and strategic projects. AI also supports well-founded business decisions through accurate forecasts and trend analyses. In the age of data, the use of AI is significantly increasing. Another advantage is the ability of AI to create tailored customer experiences, which strengthens customer satisfaction and loyalty. Last but not least, AI helps to reduce risks, whether through early detection of fraud attempts or through preventive maintenance measures to avoid downtime.

The benefits of using your own artificial intelligence systems in companies

But even more is possible for companies than existing systems such as ChatGPT, Midjourney or Stable Diffusion to use!

Companies have the opportunity to develop and use their own AI systems! Of course, this opens up opportunities that go far beyond generic, commercially available solutions. First, tailor-made systems ensure more precise coordination with company-specific needs and challenges, which leads to more efficient and targeted problem solving. In addition, through in-house development, companies can complete control over AI data and processes keep, which is particularly important with regard to data protection and security. In addition, the company-specific development of AI systems enables a greater flexibility and scalability, so that companies can react quickly to market changes or internal requirements.

Another advantage lies in innovative strength: Companies that invest in their own AI technologies keep their finger on the pulse of the latest technological developments and can thus secure a significant competitive advantage. Finally, the internal development of AI systems promotes a Innovation culture, in which new ideas and solutions are continuously developed and implemented.

But in-house development also allows complete control over data, processing, and results, increasing data protection and security. Companies that invest in their own AI technologies are also often more flexible in adapting and scaling their systems so that they can react quickly to market changes or internal requirements.

And finally, internal AI development promotes innovative strength, as the company is constantly at the cutting edge of the latest technologies and is thus able to secure a competitive advantage — this, of course, also pushes the innovation culture. This culture of innovation can have a diverse impact on all levels of the company by encouraging employees to develop creative solutions to existing problems and to discover new business opportunities. In addition, continuous exposure to the latest technologies helps to increase the company's attractiveness as an employer by attracting and retaining talented professionals. This not only creates a talented and motivated team, but also establishes a learning and growth dynamic that ensures the company's long-term success.

By promoting a proactive innovation strategy, which is based on internal AI development, the company secures continuous adaptability and development capacity, which is essential in today's dynamic market environment. Ultimately, internal AI development not only supports technological progress, but also contributes to creating an agile and future-oriented working climate that sustainably strengthens competitiveness.

On the way to becoming a data-driven company — and how AI can help

Many companies dream of being a data-driven company. But in order to become data-driven, companies must not only use modern technologies, but also profoundly change their corporate culture. Employees must recognize the value of data and base their decisions on it — only then are truly data-driven decisions possible.

This change requires a combination of training, robust data strategies, and continuous communication to ensure that all team members are empowered to make data-based decisions. In addition, companies must implement the right tools and platforms to support data analysis and enable the integration of artificial intelligence. After all, building a culture that promotes innovation and experimentation is crucial so that data-driven approaches can not only be used but also continuously improved.

To do this, employees must recognize the value of data and base their decisions on it — Data-driven Decisions!

AI can act as a catalyst here. This is because AI systems can analyze huge amounts of data and gain valuable insights that human analysts might miss out on. These insights can inform, optimize operational processes, and drive innovation. In addition, the integration of AI promotes the development of a culture of continuous learning and adaptation, as continuous feedback and iterative improvements are at the heart of AI use. As a result, companies can react more quickly to changing market conditions and dynamically adapt their strategies. With AI, existing processes can not only be made more efficient, but also completely new business models can be developed that were not possible before. In addition, the constant availability of up-to-date data helps to ensure that decisions can be made on a solid and up-to-date information basis. This leads to more precise forecasts and greater agility in operational business and strategic orientation.

For this reason, companies that want to be data-driven should see AI as a tool that supports and accelerates not only technological but also cultural changes.

AI consulting — when a company needs it

If you want to become a data-driven company, optimize processes, reduce redundant tasks and extract the full wealth of your data, then AI is very obvious in the current times. But the expertise on how to best use artificial intelligence is very specialized.

This is where AI Consulting comes in. Because, of course, companies benefit from external expertise, especially if they do not have their own AI specialists in the company. An experienced AI consultant has a deep understanding of the latest technology trends and can develop tailor-made solutions for your company's specific requirements.

By using AI Consulting, you can not only get expert advice on choosing the right AI technologies and tools, but also support in implementing and adapting the systems to meet your individual business goals. In addition, an external consultant can help identify inefficiencies in your existing processes and suggest innovative solutions to increase your competitiveness.

For example, AI consulting can be helpful if a company is not sure how to best use its data for AI purposes, or if it is struggling to develop relevant AI models for its specific business needs. Even when internal teams don't have the necessary expertise or when it comes to understanding the latest AI best practices and technologies, a consulting firm can be invaluable. In short, any time a company feels uncertain or is looking for ways to optimize their AI strategy, they should consider helping an AI consulting firm.

This is what consulting on artificial intelligence at The Data Institute looks like

With The Data Institute It is very important to us to understand you and your company, to analyse the weak points and to use the strengths.

This also includes the fact that we are reluctant to “put existing concepts over a company” — we provide you with customized solutions.

Because especially in the area of artificial intelligence The requirements and goals that companies have are very individual. This includes the areas organization, architecture and culture.

Organization in the area of AI consulting

If companies decide to use artificial intelligence, this can have profound effects on their organizational structure, and AI consulting can significantly support this.

Placement and integration of data and BI departments

A central issue is the placement and integration of data and BI departments within the company. AI consultants analyze where data knowledge is best positioned and how it can be optimally used. This also applies to knowledge management, which ensures that data knowledge is broadly and effectively anchored in the company.

In addition, agencies can help decide whether data strategists should be integrated directly into specialist departments and how communication processes involving data must be designed.

Organizational models

There are various organizational models: from centralized to decentralized structures. Hybrid models such as data mesh or the hub-and-spoke system can also be proposed, depending on the specific needs and goals of the company. A well-thought-out organization and integration of AI makes a significant contribution to companies being able to increase their innovative strength and remain competitive.

Architecture in the area of AI consulting

The data architecture acts as a blueprint for managing an organization's data. It determines where, by whom and how data is collected, stored and further processed. By including AI consulting, companies can not only obtain a clear overview of their existing data structures, but also receive support from experts in identifying potential “data gaps.” Qualified AI consulting also provides valuable insights into how the data architecture can be adapted to create optimal conditions for use artificial intelligence , which in turn maximizes the benefits and value of the data for the company.

The data architecture acts as a blueprint for managing a company's data. It determines where, by whom and how data is collected, stored and further processed. By including AI consulting, companies can not only obtain a clear overview of their existing data structures, but also receive support from experts in identifying potential “data gaps.” Qualified AI consulting also provides valuable insights into how the data architecture can be adapted to create optimal conditions for use artificial intelligence , which in turn maximizes the benefits and value of the data for the company.

In addition, a well-thought-out data architecture enables efficient data processing, which is crucial for the performance of AI systems.

Thorough advice also helps the company develop scalable data architectures that can integrate future growth and new technologies. This includes implementing security protocols that ensure that sensitive data is properly protected and can only be viewed by authorized persons. Thanks to an optimized data architecture, managers also benefit from faster insights and more informed business decisions, which contributes to the overall competitiveness of the company.

Culture in the area of AI consulting

The culture of a company decisively shapes its ability to progress. It is significantly shaped by management and its openness to innovation and fresh ideas. AI consulting can make a decisive contribution to increasing awareness of the value and opportunities of AI. This expanded mindset must be anchored throughout the company in order to truly reach everyone. Even the most advanced analysis tools and reporting systems will be ineffective if they are not integrated into the company's culture and accepted and used by employees.

Establishing an innovation-friendly corporate culture is therefore just as important as implementing technological solutions. An open and agile culture promotes a willingness to continuously improve and adapt. In addition, dealing with AI can increase the digital literacy of the workforce, which creates a sustainable competitive advantage in the long term.

The changed mindset is ultimately reflected in a proactive and data-driven way of working that utilizes the full potential of artificial intelligence.

Develop an AI strategy — with the Data Institute as a strong partner at your side

In order to develop our own AI systems and build a holistic strategy, we first identify the specific challenges in the business. Our strong team of data engineers, data scientists and data strategists first gets an overview of their existing data sources and data architecture. This initial phase of analysis is critical to ensure that all available data can be used optimally and that no valuable information is missed.

We review the quality and relevance of the data in detail to ensure that it is suitable for training the AI models. We then develop tailor-made AI models that are precisely tailored to the specific needs of the company. Through continuous testing and optimization, we ensure that the AI models deliver the best possible results and function reliably.

Careful implementation and integration into the company's existing infrastructure makes it possible to utilize the full benefits of artificial intelligence and achieve sustainable competitive advantages.

Are you already using data efficiently enough to train artificial intelligence?

Using this data, an AI model is then developed and trained as a use case, which can use machine learning or deep learning, depending on the complexity of the problem. It is important that data is thoroughly prepared and cleaned in advance to ensure the best possible results. After multiple testing and validation phases, the model is finally integrated into the production environment, where it can demonstrate its capabilities in real time. Throughout the process, it is important that companies invest in appropriate infrastructure and tools and constantly collect feedback to continuously improve and adapt the system.

In addition, the use of monitoring tools should be considered to continuously monitor the performance and accuracy of the model. Self-developed AI systems enable a tailor-made solution that is precisely tailored to the specific requirements and needs of the company.

What is the difference between artificial intelligence and machine learning?

Machine Learning (ML) is an indispensable pillar of artificial intelligence and plays a decisive role in its impressive development. This approach enables computer models to learn output data and make predictions or make decisions based on this information without the need to explicitly program them.

In simple terms: ML is the method by which AI systems acquire the ability to learn from their experiences.

For example, a machine learning model could “learn” what a cat looks like based on thousands of images. When faced with a new image, it can predict whether a cat is depicted in that image or not based on its previous training. This process of learning from data and applying what has been learned to new contexts is a core part of what we understand as artificial intelligence. As a result, ML is used in many industries in a variety of ways to automate processes, increase productivity and create personalized user experiences. This ability to efficiently analyze large amounts of data makes it possible to identify trends and patterns that would be elusive for human analysts. This is how ML accelerates the development of new technologies and business strategies by providing in-depth insights and improved decision-making processes.

What is the difference between machine learning and deep learning?

Machine learning and deep learning are both sub-areas of artificial intelligence, but differ in their methodology and complexity.

Machine Learning (ML) uses algorithms that can learn from data and recognize patterns. This can happen through supervised, unsupervised, or partially supervised learning.

Deep learning, on the other hand, a specialized form of machine learning, uses neural networks with many layers to recognize and learn complex patterns in large amounts of data.

While machine learning models often require manually set features, deep learning models can extract features and patterns directly from the data, improving their ability to process complex tasks such as image and speech recognition. Deep learning uses so-called artificial neural networks, which are inspired by the structure of the human brain.

The biggest advantage of deep learning lies in its ability to process both structured and unstructured data, making it particularly useful for applications such as natural language processing and autonomous driving. However, since deep learning models require enormous computing power and large amounts of data, they are more resource-intensive than traditional machine learning models. Accordingly, a combination of both methods is often used to achieve optimal results in various fields of application of artificial intelligence.

Can I build an AI project without data?

Data is critical for AI projects because it is needed to train models to make predictions and recognize patterns. An accurate and efficient AI model cannot be developed without relevant and high-quality data.

While theoretically realistic, randomly generated data can be used to create prototypes, the quality and quantity of the real data strongly influences the efficiency of the AI system. That is why sufficient, relevant and high-quality data is crucial for successful integration and implementation of AI in companies.

It should be noted that companies can use existing AI systems, but cannot feed them with their own data, which can result in restrictions.

Companies must also ensure that their data is well structured and accessible to further optimize model accuracy. Furthermore, data protection and security aspects of data collection and processing are also important issues that must be considered to ensure trust and compliance.

Finally, it is advisable to continuously collect and update data to keep AI systems efficient and relevant in the long term.

What data is required for AI?

The type of data required for AI depends heavily on the project type and the intended goals.

For example, you need text data for natural language processing (NLP), image data for projects that use computer vision, or sensor data for IoT projects.

It is important that the data used accurately reflect the real conditions with which the AI will ultimately interact to generate a reliable model.

In addition to data accuracy, data quality also plays a decisive role; incomplete or incorrect data can lead to inaccurate models and incorrect decisions. In addition, the amount of data is essential, as larger data sets usually result in more robust models.

It's also important to update the data regularly to ensure that the AI model remains accurate and relevant. Finally, companies must ensure that data is collected and used ethically and legally to meet data protection and compliance requirements.

The data that companies need for their projects can basically be divided into four categories:

Training data for AI projects

Training data represents the problem area and should be labeled to show expected results. This data serves as the basis on which the AI model goes through its initial learning processes and recognizes patterns and relationships.

Validation data for AI projects

Validation data is used to refine and evaluate the AI model. They help improve the accuracy and reliability of the model and assess its performance by using them repeatedly during the training process to verify the accuracy and reliability of the model.

Test data for AI projects

Test data is used to evaluate the developed model. This data set should be different from training and validation data to allow an unbiased assessment of model performance and to ensure that the model not only performs well on training data but is also generally valid.

Production data for AI projects

Finally, there is the production data that is used by the model in the production environment to perform its tasks. This data reflects the real conditions under which the AI model must prove its capabilities in practice.