Select the statements that best apply to your company.

In 10 targeted questions, we identify your strengths and weaknesses in relation to the application and use of GenKI in your company. We show you where you stand and help you get to the next level with specific recommendations for action.

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What applies most to your organization?

Our organization increasingly recognizes the importance and transformative potential of AI, but is not sure what specific role AI should play in our context.

We are aware of the impact of AI on our industry and our ecosystem and have developed a vision to guide our AI initiatives (our "AI Goal").

We have a clear understanding of how we can adapt to AI-related changes. Our managers communicate a detailed AI strategy that also takes ethical and legal implications into account.

In our organization, the potential of AI is constantly being considered and applied by employees in their day-to-day work.

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How does your organization deal with AI?

AI-related decisions in our organization are made spontaneously and without a fixed plan.

Our organization has specific strategies for implementing AI in line with our overarching AI goals.

Our AI strategy has a major impact on our business processes, products and services and is measured using defined key performance indicators (KPIs).

AI is a central component of our business model and our overall strategy, which is why we do not need a separate AI strategy.

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What situation is your organization in?

The AI use cases in our organization have largely developed spontaneously from the ideas of individual team members.

Our organization has structured procedures for identifying, evaluating and prioritizing AI use cases, with each project having clear and measurable objectives.

Our employees evaluate potential AI solutions for work tasks and have the necessary resources and tools. We have a dynamic pipeline for AI use cases in which projects are regularly reviewed for priority, synergies, risks and impact on our AI strategy.

In our organization, we take a holistic view of AI use cases and use AI-based tools across industries to build on existing solutions. AI projects are implemented along the entire value chain in order to identify and exploit synergies.

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How do you deal with AI projects?

In our organization, the initiative for AI projects lies with individual employees who are spread across the entire company.

We have defined clear responsibilities for AI-related tasks in central and decentralized units and set up a specialized team to support the implementation of AI use cases.

Our organization has an effective culture of collaboration within and between departments when it comes to AI. The IT department and the central AI team work closely together, with the AI team defining standards and best practices and setting guidelines for the ethical use of AI and compliance with legal regulations.

AI systems can be developed decentrally in our organization, as the necessary expertise is available in all departments. They are used via technical platforms or self-service tools. To ensure fairness and avoid bias in the AI algorithms, clear testing mechanisms and processes ensure the robustness and security of our AI applications.

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What expertise does your organization have in relation to AI projects?

Our organization does not have the necessary skills and personnel to drive AI initiatives and develop AI projects.

We have the necessary expertise to manage AI activities in our organization.

Our organization actively recruits external AI experts, promotes the development of internal talent for AI roles and retains them in the long term through career development programs, competitive compensation and flexible working conditions. AI tasks are assigned to clearly defined roles for which targeted recruitment takes place.

Our company is recognized as a top employer in the field of AI and benefits from collaborations with leading industry and research institutions in this field. Working on AI projects with us is synonymous with excellence, and our AI specialists have the opportunity to work at the forefront of AI research and share their findings with others at specialist conferences.

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What is the culture in your organization with regard to AI?

So far, we haven't thought about what kind of culture is necessary for AI to succeed.

Our organization places great value on experimentation and collaboration in interdisciplinary teams. This is the only way to ensure successful AI development.

Our company is data-driven. Our managers lead by example by undergoing further training in the field of AI and take AI issues seriously. The implementation of AI is seen as a transformation process that requires clear communication and change strategies.

Trust in AI is firmly anchored throughout our organization. Our employees are able to work with probabilistic systems and software whenever necessary.

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How is your organization set up technologically for AI projects?

It is often unclear how access to the AI tools and hardware required for specific projects is regulated.

Our data scientists and machine learning engineers always have access to the hardware and tools they need for their AI developments.

Our AI teams benefit from a standardized approach to the use of software and tools, a centralized code base and clear guidelines and documentation that define which AI models should run on which infrastructure.

The deployment and scaling of AI solutions is seamless, with easy integration into existing pipelines by other teams. Our infrastructure enables automatic scaling and supports the rapid activation, deactivation or replacement of models. Automation tools for workflows and deployments facilitate the use of AI solutions.

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How does your organization handle data?

We often lack the data we need for AI projects, or it is of poor quality and only available to a limited extent.

We are actively looking for valuable data sources, both internally and externally, to support our AI initiatives. Within our organization, there are specific roles responsible for building and maintaining data pipelines.

Our organization has clear definitions and processes regarding data value, responsibilities and accessibility. AI-relevant data is stored centrally and is easily accessible to employees. Efficient tools and systems support the organization and the flow of data.

Our central data system enables employees to search for specific data records and obtain information on how to access them. In addition, data quality assurance mechanisms have been implemented to minimize disruptions, errors and distortions and ensure a robust data structure.

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What is your organization's relationship to your AI ecosystem?

So far, we have given little thought to our AI ecosystem or which partnerships could be important for us.

Our organization exchanges ideas with universities, service providers and industry colleagues and pursues a clear strategy for partnerships. We also know the AI start-ups in our industry.

We have defined processes for deciding whether to develop AI solutions internally or purchase them. We also exchange data with partners in order to generate added value and have regulations on cost and risk sharing for data-based projects. We make targeted use of academic partnerships to recruit and retain AI talent.

Our company actively participates in organizations that set global standards for AI and contributes by sharing resources and knowledge. We are working to build an ecosystem around our products, services and methods.

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How does your organization implement AI projects?

Our organization does not have a set approach to managing AI projects.

There is a clearly defined financing process for AI projects and accompanying change management for the implementation of AI solutions. At the start of the project, assumptions about the data and feasibility of the solution are checked in order to minimize the risk of subsequent failure.

In AI projects, we pay attention to cost control and the development of technically sustainable models. We integrate AI solutions into our IT infrastructure and pool technical expertise. There are clear guidelines for the cross-departmental use of code, models and data as well as regular data quality checks and training.

Our teams in AI projects are diverse. We are constantly striving to integrate feedback loops into AI-based solutions in order to collect data and continuously improve the solutions. We are also working to increase the level of automation in the provision of AI solutions.

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View Result

Newcomer

Strategist

Quick starter:in

Innovator:in

Pioneer:in

Evaluation:

You are an innovator (points 31-36)

Companies at this stage are experimenting with AI, but have not yet established any fixed strategies or processes. There is a fundamental awareness of and interest in AI, but the applications are sporadic and unsystematic.

Our recommendation for action:

We recommend developing a preliminary AI strategy with clear and achievable goals. Through industry-specific AI case studies, opportunities can be identified while raising awareness of the possibilities and impact of AI. In our workshops, we support companies in identifying potential fields of application for AI and developing a fundamental strategic perspective for its introduction.

→ We show you how to reach the next level