Generative AI - Definition and examples of applications

Content

What's generative AI?‍
How does generative AI work?
What are the application areas of generative AI?
What potentials offers generative AI for companies?
Generative AI applications and pratical examples‍
Successfully implement generative AI in companies‍
What are potential risk factors of generative AI?‍
Thinking generative AI and ethics together‍
Development of a strategy for generative AI
Find and prioritize generative AI use cases
Generative KI Anwendungen im Unternehmen erfolgreich umsetzen
Beispiele für Generative KI-Tools
Weiter hilfreiche Quellen zum Thema generative KI

What is generative AI?

Generative AI, or GenAI, is a branch of artificial intelligence that focuses on the generation of new, synthetic data. The generative AI definition from Gartner describes generative AI as a combination of AI techniques ...

"... that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data."

These results can appear in various forms, such as images, texts, music, videos or even 3D models. Generative AI models learn from large amounts of training data and try to capture the underlying patterns and structures of this data in order to generate new, similar content.

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How does generative AI work?

Generative artificial intelligence is based on the concepts of machine learning and deep learning. At the heart of this development are large language models (LLMs), which serve as the basis for many generative AI systems. This technology uses the principles of machine learning, in which computer systems learn from data and improve their performance without being explicitly programmed. Deep learning, a sub-area of machine learning, uses multilayer neural networks to recognize and process patterns in data.

However, LLMs also come with challenges. The reliability of the results can vary, and the timeliness of the training data used can lead to outdated or inaccurate information. To address these challenges and improve the performance of LLMs, various optimization methods can be used. One such method is Retrieval Augmented Generation (RAG), in which the model is enriched with additional, up-to-date information from external sources. This makes it possible to increase the accuracy and topicality of the generated content while at the same time utilizing the strengths of LLMs in language processing and generation. In this way, generative AI applications through RAG can better understand context, provide source citations and process multimodal information, leading to more holistic and trustworthy results.

The difference between artificial intelligence and generative AI

Generative AI is a specialized form of artificial intelligence that focuses on the creation of new content such as text, images or music. In contrast, AI in general encompasses a broader range of technologies and applications. While generative AI uses complex deep learning models to produce original output, general AI systems use various machine learning techniques for tasks such as analysis, prediction and decision making.

Unterschied zwischen KI und generativer KI — Figure: The difference between artificial intelligence and generative AI

Generative AI is mainly used in creative fields, while general AI is used in many sectors such as data analysis and process optimization. The main difference lies in the ability of generative AI to generate new data, while general AI systems mostly process and interpret existing data.

What are the areas of application for generative AI?

Generative AI can be found in various areas, although it is mainly used to create new content and support creative processes. The most important areas of application are:

Text generation: generation of articles, summaries, scripts or poems
Image generation: creation of images and graphics based on text descriptions
Audio editing: creation of music, sound effects and synthetic speaker voices
Video generation: generation of video sequences or animation of images
Code generation: creation of program code based on linguistic descriptions
3D modelling: generation of three-dimensional models for design and simulation
Translation: conversion of texts between different languages
Data augmentation: extension of data sets by generating synthetic data
Chatbots / virtual assistants: creation of specific answers in natural language
Product design: support for concept development and visualization of new products

These generative AI examples show the versatility of the technology in creating and processing different types of content and data.

What potential does generative AI offer companies?

Generative artificial intelligence offers companies enormous economic potential that can be used in various areas to increase efficiency and improve business opportunities. By using this technology, companies can not only optimize their internal processes, but also develop innovative products and services that are tailored to the needs of their customers.

Efficiency and productivity gains through generative AI

Automation of processes: Generative AI can automate repetitive tasks, saving time and resources. For example, tools such as Outset.ai can automate the interview process, allowing companies to work more efficiently.
Faster decision-making: With generative AI-powered analytics tools like Akkio, companies can make faster, more informed decisions because these systems analyze large amounts of data in a short amount of time.
Increased productivity: By automating routine tasks, employees can focus their time on value-adding activities, which increases the company's overall productivity.‍
Efficient market research: Tools such as YouScan Insights help companies to quickly identify current trends and developments, enabling them to adapt their strategies promptly.

New and improved business opportunities through generative AI

Accelerated innovation processes: Generative AI tools support the entire innovation process, from idea generation to validation, which accelerates the development of new products and services.
Promoting creativity: Generative AI tools such as Stratup.ai and Mymap.ai promote the generation and development of innovative ideas based on current market trends.
Optimization of product launches: Systems like IndieZebra can help companies increase the chances of success for product launches through data-driven validation of names, logos and text.
Validation of new business models: With tools such as Godmode.space, companies can validate new business opportunities and minimize risks during market launch.
Tapping into new markets: Improved market analyses enable companies to identify new business opportunities and adapt their offerings accordingly.

> Impulse: Philipp Klöckner's keynote at the OMR Festival 2024 and a look at current developments in the field of artificial intelligence and their impact on companies and our working world:

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Generative AI applications and practical examples

Generative AI offers companies a wide range of possible applications in various departments to optimize processes, drive innovation and increase efficiency. Here is an overview of generative AI examples in various areas of the company:

Research and development: In research & development, generative AI supports the entire innovation process. It accelerates the generation of ideas, promotes the visual development of ideas and helps with the validation of business models and iterative business development.‍
Marketing and sales: In marketing & sales, generative AI enables more efficient market research and trend analysis. It supports the optimization of product launches, the development of marketing strategies and the creation of personalized content in order to address customers in a more targeted manner.‍
Customer service: In customer service, generative AI automates customer surveys and enables extensive discussions in different languages. AI-supported systems improve customer service through fast and precise responses to inquiries and personalized support based on customer data and preferences.‍
Product management: In product management, generative AI supports the development and validation of product ideas, enables effective A/B testing of product features and helps to predict product trends and customer needs.
Human resources: In the HR department, generative AI can be used to automate application processes, create personalized training content and analyze employee feedback to improve employee engagement.
Finance and controlling: In the finance and controlling area, the technology supports the creation of financial forecasts and models, automates reporting and helps with risk assessment and risk management.
IT and Technology: In the IT & technology department, generative AI is used to develop AI-supported applications, automate software tests and improve cyber security.
Production and logistics: In production & logistics, generative AI optimizes production processes, enables predictive maintenance and improves supply chains through AI-supported analyses.
Legal and compliance: In the legal and compliance area, the technology supports automated contract review and creation, AI-supported compliance monitoring and the analysis of legal risks.
Order processing: In order processing, generative AI can be used to optimize the entire process from order acceptance to delivery. It can automatically process orders, set priorities, allocate resources efficiently and accurately predict delivery times. By analyzing order patterns and customer preferences, AI can also proactively make recommendations for cross-selling and up-selling.

What are the key success factors for generative AI?

Generative AI presents significant opportunities for companies, but requires careful planning. This section examines the key success factors that make the difference between a successful integration and a failure:

Clear objectives and strategy

Before generative AI is introduced, companies should define clear goals. It is crucial to clarify which specific business processes are to be optimized, where productivity can be increased and which innovations are being targeted. A well thought-out AI strategy helps to design the deployment in a targeted manner and create sustainable added value.

Data availability and quality

Generative AI applications require high-quality data. Relevant company data must therefore be made available and usable. Data quality should be regularly checked and ensured, while data protection and data security must be guaranteed in order to meet legal requirements.

Employee involvement and training

The successful use of generative AI requires the active involvement of employees. Raising awareness of the opportunities and risks of the technology is important, as is training on responsible use. Transparent communication promotes the acceptance and commitment of employees, which is crucial for the success of the implementation.

Ethical guidelines and governance

Companies should establish clear ethical guidelines for the use of generative AI. It is important to define responsibilities and implement quality assurance processes to ensure that the technology is used responsibly and in line with company values.

Technical infrastructure

The necessary IT infrastructure must be created in order to use generative AI effectively. This includes powerful hardware, secure cloud solutions and integration into existing systems. A robust technical basis is crucial for the smooth implementation and operation of AI applications.

Step-by-step implementation

A step-by-step approach is recommended when introducing generative AI applications. It is advisable to start with internal applications and carry out pilot projects. By gathering experience and iteratively improving, the technology can be optimally adapted to the needs of the company.

Legal protection

Legal aspects must be carefully considered when implementing generative AI. This includes clarifying copyright and intellectual property, regulating liability issues and complying with data protection regulations. Legal protection protects the company from potential risks and conflicts.

What are potential risk factors for generative AI?

Generative AI offers companies numerous opportunities, but also harbors risks. Careful consideration of these challenges is crucial in order to use the technology responsibly and successfully. Considering potential risks can help companies make strategic decisions and avoid undesirable consequences.

Data protection and information security: This includes the risk of data protection breaches, unauthorized data processing and problems with data transfer abroad, especially with cloud-based solutions.
Quality and reliability of results: Inaccurate or distorted outputs can lead to wrong decisions and raise questions of transparency and explainability.
Legal and ethical challenges: These include intellectual property and copyright issues, liability issues and ethical concerns about fairness and potential discrimination.
Compliance and regulation: Companies must ensure that their AI systems comply with current and future legal requirements.
Organizational challenges: These include the need for adequate employee training and the promotion of acceptance of AI technologies within the company.

Thinking generative AI and ethics together

The rapid development and spread of generative AI not only brings with it technological advances, but also important ethical questions. As a company and society, we face the challenge of using this powerful technology responsibly and in line with our values.

A central principle in the ethical handling of generative AI is transparency and explainability. It is important to disclose the functioning and decision-making processes of AI systems in order to create trust and ensure traceability. Closely linked to this is the principle of fairness and non-discrimination. AI systems must be designed and trained in such a way that they avoid bias and do not cause unfair treatment of certain groups.

Responsibility and accountability play a crucial role. It must be clear who is responsible for AI-generated content and decisions. At the same time, data protection and privacy must not be neglected. The protection of personal data and respect for privacy in data processing are essential.

The security and robustness of generative AI systems against manipulation and misuse must be guaranteed. It is important that human control is not lost. Human oversight and ultimate decision-making power over AI systems should always be maintained.

Generative AI should not only serve economic interests, but should also be used to promote the common good and sustainable development. To achieve this, ethical training and awareness-raising among developers and users of AI systems is essential.

What are the components of a generative AI strategy?

A well thought-out strategy is the key to effectively harnessing this transformative power of generative AI and gaining a competitive advantage.

The following points form the foundation for a future-oriented generative AI strategy that will take your company to the next level of digital evolution.

Inventory and target definition for the use of generative AI

A thorough assessment of the current company situation is essential for an effective strategy for implementing generative AI. Companies should analyze their existing strategy and identify areas in which generative AI could create significant added value. Based on this, clear, measurable goals for the use of generative AI should be defined. These goals should be closely linked to the overarching corporate goals and aim to achieve concrete improvements or innovations. A precise definition of objectives not only helps to focus efforts, but also enables success to be measured at a later date.

Carrying out a potential analysis

Once the goal has been defined, a detailed potential analysis should be carried out. Companies should identify specific use cases for generative AI in various areas such as product development, customer service or internal process optimization. Both existing challenges and new opportunities that could arise through the use of generative AI should be taken into account. These use cases should be prioritized based on their potential business value and technical feasibility. This prioritization enables an effective use of resources and a focus on the most promising applications.

Assessment of necessary resources and skills

A critical step in strategy development is the evaluation of existing technical resources and data infrastructure. Companies must examine the extent to which their current systems and data are suitable for the use of generative AI and what adaptations or extensions may be necessary. At the same time, it is important to evaluate employee skills in relation to AI technologies. Existing skills and skills gaps must be identified and a plan for necessary further training measures or the recruitment of specialists must be developed. A realistic assessment of resources and skills is crucial for the successful implementation of the AI strategy.

Technology selection

Choosing the right technology is a key aspect of the AI strategy. Companies should evaluate various generative AI technologies and platforms in terms of their suitability for the identified use cases. Factors such as scalability, integrability into the existing IT landscape, costs and future viability of the technologies should be taken into account. It should be checked whether it makes sense to use existing solutions or to develop your own models. The careful selection of technology lays the foundation for the successful and sustainable implementation of generative artificial intelligence in the company.

Consideration of ethics and compliance

An often underestimated but crucial aspect of AI strategy is the consideration of ethical and legal aspects. Companies need to develop clear guidelines for the ethical use of generative AI. These should cover topics such as data protection, fairness, transparency and accountability. It must be ensured that the AI strategy complies with relevant data protection regulations and compliance requirements. Consider potential societal impacts and reputational risks that may be associated with the use of AI. A proactive approach to ethics and compliance builds trust with customers and employees and minimizes legal and reputational risks.

Find and prioritize generative AI use cases

Companies are increasingly recognizing the importance of generative AI for their business success. However, a key challenge is to find the right starting point and identify relevant use cases and applications. The following five steps provide guidance on how companies can proceed to define possible use cases at both departmental and cross-divisional level.

Step 1: Inventory and analysis

To begin with, companies should carry out a comprehensive inventory of their existing business processes and challenges in all departments. This includes identifying areas with a high potential for optimization as well as analysing the existing data sources and their quality. This basis helps to find targeted starting points for the use of generative AI.

Step 2: Generating ideas

In this phase, cross-departmental workshops should be organized to collect creative ideas for possible generative AI applications. The use of techniques such as design thinking can help to promote innovative approaches. It is important to also consider industry-specific best practices in order to develop a broad spectrum of ideas.

Step 3: First evaluation

The next step is the initial evaluation of the collected ideas based on clearly defined criteria. Important aspects include business value, strategic relevance, technical feasibility, the availability of necessary data and the cost of implementation. This evaluation makes it possible to create a ranking list of the most promising use cases.

Step 4: Detailed qualification

A more in-depth analysis should be carried out for the highest-rated use cases. Here, the potential ROI is estimated and possible risks and challenges are taken into account. This detailed qualification helps to make informed decisions about the feasibility and benefits of the identified generative AI applications.

Step 5: Prioritization and selection

Finally, the identified use cases should be prioritized based on the detailed assessment from the previous step. One or more use cases are then selected for pilot projects, whereby the focus should be on quick wins. This selection lays the foundation for the subsequent implementation and measurement of success.

Develop generative AI applications

In order to successfully implement generative artificial intelligence in a company, it is important to develop a clear roadmap that ranges from the identification of suitable use cases to full integration into the business processes. We have provided an example of a rough approach below:

Step 1: Strategic planning and goal setting

First of all, companies should carry out a comprehensive inventory to analyze existing business processes and identify optimization potential. It is important to define clear goals for the use of generative AI in order to achieve the desired results. In addition, relevant generative AI applications should be identified and prioritized to ensure that the technology is used in a targeted manner.

Companies should start with smaller, internal generative AI applications to gain initial experience with the technology. At the same time, it is crucial to ensure the necessary IT infrastructure, be it through cloud solutions or on-premise implementations. The results of the pilot projects should be carefully evaluated in order to adapt the strategy accordingly and further optimize the use of the technology.

Step 3: Data management and quality assurance

Another step in the development of generative AI applications is to identify relevant company data for training the AI models. Companies must implement processes for data preparation and quality assurance. This ensures that the data used is of high quality. Continuously ensuring data availability and quality is crucial for the success of AI applications.

Step 4: Employee involvement and competence building

Employee involvement is a key aspect in the implementation of generative AI. Companies should inform their employees about the potential and limitations of the technology and offer training on the responsible use of AI. By promoting acceptance and commitment, companies can ensure that their employees actively participate in the transformation process.

Step 5: Ethics, governance and legal aspects

It is important to develop ethical guidelines for the use of generative AI applications so that the technology is used responsibly. Companies should define clear responsibilities and implement quality assurance processes to minimize potential risks. Legal aspects such as copyright or data protection should also be clarified to ensure compliance with relevant laws and regulations.

Step 6: Continuous evaluation and adaptation

After implementation, companies should continuously monitor the performance and benefits of the AI application used. Based on the insights gained, it is important to regularly adjust the strategy and, if necessary, identify new optimization or scaling options for the generative AI applications. This iterative process helps to ensure the long-term success of the technology in the company.

Examples of generative AI tools

ChatGPT is a generative AI-based chatbot that processes and generates natural language. It works by being based on a large language model that has been trained on enormous amounts of text. In companies, ChatGPT can be used for customer service, content creation, idea generation and research support.

Perplexity is a generative AI-powered search engine that understands natural language queries and provides precise answers. It uses large language models to analyze and summarize information. Companies can use Perplexity for quick information searches, market research and to support decision-making processes.

DALL-E is a generative AI system for image generation based on text descriptions. It uses a neural network to generate images from textual input. In companies, DALL-E can be used for product design, marketing visualizations and creative concept development.

Midjourney is also a generative AI tool for image generation based on text descriptions.It uses machine learning to create detailed and artistic images.Companies can use Midjourney for marketing materials, product visualizations and creative ideation.

DeepL is a generative AI-based translation tool that uses neural networks for precise translations.It processes context to produce natural-sounding translations.In companies, DeepL can be used for international communication, localization of products and translation of documents.

GitHub Copilot is a generative AI assistant for software developers.It uses machine learning to generate code suggestions based on the developer's context and comments.In companies, GitHub Copilot can be used to increase development productivity, speed up prototyping and support code documentation.

Canva is a generative AI application for visual design and graphic design.It uses generative artificial intelligence to help users create professional-looking designs for various purposes.In companies, Canva can be used to create marketing materials, presentations, social media content and infographics.