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Quick Check - AI Maturity Compass

Quick Check - AI Maturity Compass

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Get an initial assessment in just 5 minutes

Select the statement that applies to your company and benefit!

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First assessment of your AI maturity level immediately visible

Ambition *

Please select one statement that applies to your organization

Our organisation is aware of the rise of AI and its transformative potential but uncertain about the role AI plays for our organisation.

Our organisation understands the impact of AI on our industry and ecosystem and has developed a target picture where to focus AI activities ("AI ambition").

There is a clear understanding of how to adapt to AI-driven changes affecting our organisation. A comprehensive roadmap for AI is communicated by the C-levels, including potential ethical and legal implications of our organisation's AI ambitions.

AI and its potentials are continuously considered and used in the course of the day-to-day work by employees.

Steering *

Please select one statement that applies to your organization

Decisions regarding AI are made ad-hoc and do not follow a systematic approach.

Our organisation has defined strategic initiatives for the adoption of AI throughout our organisation in an AI strategy that is aligned with the AI ambition.

The AI strategy shapes processes, services and products in the company and its success is monitored through defined KPIs.

Using AI is at the core of our business model and company strategy, which is why there is no need for a separate AI strategy.

Use Cases *

Please select one statement that applies to your organization

The AI use cases we work on emerged more or less randomly from ideas of individual members of the organization.

Our organisation has processes to find AI use cases, describe, evaluate and prioritise AI use cases with explicit and measurable goals for success evaluation defined.

Our organisation's employees are able to assess AI solutions for work problems and have the necessary awareness regarding required resources and tools. An AI use case pipeline is continuously reviewed to prioritise, consider synergies between use cases, evaluate risks and re-evaluate and realign with the AI roadmap.

Our organisation considers AI use cases holistically. Particularly, AI-based tools are set up in a way for several use cases across our organisation to build upon and AI use case implementation is considered along the entire value chain to identify synergy potential.

Organization *

Please select one statement that applies to your organization

Our AI activities are driven by individuals scattered across the organization.

Our organisation has defined which AI-related responsibilities lie within more central vs. decentral functions or teams and has a dedicated team supporting the implementation of AI use cases.

Our organisation has an effective environment for collaboration within and between departments regarding AI and the AI core team and the IT department collaborate effectively on AI solutions. The AI core team defines AI standards and best practices and assigns responsibility for clear guidelines on ethical use of AI and adherence to legal requirements.

AI systems can be build decentrally (i.e., the required expertise is present in all units of our organisation) and in a standardised way (i.e., AI services are used via technical platforms or self-service tools). Robustness and safety are ensured through auditing mechanisms and clear processes to raise concerns on fairness and biases of AI algorithms.

Expertise *

Please select one statement that applies to your organization

We do not have the necessary skills and people internally to guide the AI activities and develop AI projects.

The necessary expertise for guiding AI activities is present in our organisation.

Our organisation is effectively hiring external AI talent, develop internal talent into AI related roles and keep the AI talent long-term (i.e., has a clear career development programme, offers competitive pay and flexible working hours). AI-related tasks are separated across roles that have been hired specifically.

We are perceived as a top employer in AI that benefits from connections to leading AI industry and research institutions. Working on AI projects in our organisation is considered a sign of excellence and AI talent gets the opportunity to apply state of the art AI research and publish on leading AI conferences.

Culture *

Please select one statement that applies to your organization

We haven't thought about what kind of environment is needed for AI projects to succeed.

Our organisation understands the necessity of experimentation and collaborative, cross-disciplinary teamwork for successful AI development is in place.

Our organisation considers itself as a data-driven organisation. Our management acts as a role model (e.g., by taking AI courses and upskilling themselves concerning the topic and taking fears regarding AI seriously) and treats AI adoption as a transformation process requiring dedicated communication and change management efforts.

There is wide-spread trust in AI across our organisation. Where required, employees are further able to understand and hence work with probabilistic systems and software.

Technology *

Please select one statement that applies to your organization

It is unclear whether access to necessary AI tools and hardware is available and it is organised for each AI project individually.

Data scientists and ML engineers have access to the hardware and tools required for AI development

AI teams benefit from standardisation (i.e., standardised software stack and tools are used), centralisation (i.e., program code is accessible through a central platform) and clear guidelines and documentation (i.e. guidelines on which types of AI model has to be deployed on which kinds of infrastructure).

AI solutions can easily be deployed and scaled (i.e., integration by other teams into their own pipelines is easy, the infrastructure supports autoscaling and allows models to be activated/deactivated and exchanged within minutes). Workshop automation or deployment automation engines further deploy AI solutions.

Data *

Please select one statement that applies to your organization

We typically don't have the necessary data for AI projects available or it suffers from low quality or limited size.

Our organisation actively tries to identify valuable internal and external data sources that could fuel our AI activities and have dedicated roles in our organisation that are responsible for building and maintaining data pipelines.

There are clear definitions and processes in place regarding the value of data, responsibilities and ownership, accessibility (i.e., AI relevant data is stored in central, accessible repositories, data can easily be ingested by employees), and organisation (i.e., tools and systems organise the flow of data).

There is a central system that allows employees to search for datasets across our organisation and provides information on how to get access to those datasets. Also, mechanisms to maintain data quality (e.g., by minimising noise or errors, bias, and ensuring good structure) exist.

Ecosystem *

Please select one statement that applies to your organization

We have not yet thought about our AI ecosystem, i.e. which partners we need and when.

Our organisation is engaging in exchanges (e.g., with academia, industry peers or service providers) with a clear partnering strategy and is aware of AI start-ups in the industry.

Our organisation has a clear process to decide on "make or buy" for AI use cases. We further exchange data with partners to create value and have a process for sharing data related costs and risks between us and the partners. Academic partnerships are used to gain access to and hire AI talent.

Our organisation actively participates in and exchange resources and knowledge with institutions or organisations that shape and define global standards for AI development and application and build an ecosystem around our products, services and/or methods.

Execution *

Please select one statement that applies to your organization

We have no specific approach to managing our AI projects.

There is a visible and known process to get funding for AI projects and change management efforts accompany the implementation of AI solutions. In the beginning of AI projects, assumptions held on the dataset and feasibility of the solution are validated to reduce the risk of later failure.

AI projects are set up and structured in a way that costs are considered and technically sustainable models can be developed. AI solutions are integrated into the IT infrastructure, technical competencies are combined. Clear policies for sharing code, models and data across departments as well as for monitoring data quality and trigger retraining exist.

Team diversity in AI projects is ensured. Our organisation continuously tries to integrate feedback loops into AI-based solutions to gather data and improve the solution. The degree of automation is continuously increased in the AI solution deployment.

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