Welcome to AI Maturity Assessment

A multi-dimensional assessment that measures your company's AI readiness and capabilities. The assessment covers strategy, data, technology, and human resources dimensions. The goal is to make the organization's current maturity level visible for AI initiatives. Outputs are complemented by insights and tracking elements specified in subsequent questions.

The scope includes the combined consideration of strategy, data, technology, and human resources areas. This structure acknowledges that AI readiness depends on multiple dimensions, not a single point. Each area is evaluated with its own criteria. Results are interpreted as a whole.

"Comprehensive Assessment" addresses your AI readiness and capabilities holistically. Through a multi-dimensional approach, the organization's strategic, technical, and human resource dimensions are evaluated together. This clarifies the current state. It is supported by additional outputs in other sections.

It provides detailed recommendations and a roadmap to improve AI capabilities/applications. These insights focus on areas identified in the assessment. The roadmap explains which steps to take. This makes the improvement direction concrete.

It offers the opportunity to track the AI readiness journey over time. It provides context by comparing with industry standards. The organization's progress from where to where becomes visible. Tracking makes the assessment more than a one-time event.

Five areas are evaluated: Data Technology (DT), Analytics Technology (AT), Platform Technology (PT), Operational Technology (OT), Organizational Readiness (OR). These areas together form an end-to-end framework. Each represents a different set of competencies. Results can also be viewed by area.

Data collection, storage, management, assurance, and governance are examined. The state of infrastructure, level of automation, and the ecosystem's ability to support growth are evaluated. Data quality metrics, access controls, validation, and regulatory compliance are reviewed. Security and resilience topics such as encryption, backup, and disaster recovery are also included.

The level of data usage for ML and the development, deployment, monitoring, and integration of models into business processes are examined. Adoption maturity, retraining, and the scope of inference/tuning processes are evaluated. MLOps, lifecycle management, explainability, performance tracking, data drift, and automatic retraining are addressed. GPU usage and LLM/GenAI infrastructure suitability are also included in this scope.

Infrastructure that enables integrated, scalable, and secure AI-focused operations is evaluated. Cross-system interoperability and data flow automation are examined. Real-time orchestration, edge computing, and cloud adoption status are addressed. Scalability, fault tolerance, reliability, and protection against cyber threats are also considered.

The integration of digital tools in operational environments is evaluated. HMI/dashboards, real-time data access, and user interaction are focal points. Predictive features in dashboards and the automation level of maintenance and updates are examined. Decision support systems, autonomous systems, and the frequency of feedback of insights into processes are also reviewed.

Strategic and cultural readiness to adopt and scale AI is measured. Having expert roles and leadership supporting innovation with a clear AI strategy/roadmap is examined. Data-driven decision making, collaboration, and the level of integration into core processes are evaluated. Rapid transformation of insights into operations and regulation/compliance processes are also within scope.

Standard A–D multiple choice questions are used. The meaning of options in each question is defined in the relevant context. For example, topics such as collection, storage, security, governance, or integration are graded with options. Answers are given on this scale.

Encryption (at rest/in transit), access control, and backup practices are evaluated. Targets such as RPO/RTO for disaster recovery are considered. Integrity is ensured through validation and governance policies. Thus, data security and resilience become visible.

The production use of LLM-based services and business use cases of Generative AI tools are considered. The number of LLM/GenAI projects or products is evaluated. Fine-tuning approaches and GPU usage are addressed. These elements are examined in an integrated manner within the AT scope.

Model lifecycle management and MLOps tools are evaluated. Real-time performance tracking, data drift monitoring, and automatic retraining topics are addressed. Explainability practices are included. Orchestration and CI/CD practices are also part of the scope.

There is a scale from manual methods to ad-hoc scripts, then to partially automated pipelines and end-to-end automated integration. The processing model is evaluated as batch and real-time approaches. Orchestration technologies support this classification. Thus, the platform's maturity level is revealed.

Dashboard existence and the dashboard update method (manual, partial automation, full automation) are considered. Dynamic filtering and predictive analytics features are reviewed. Operational action integration and real-time recommendations are evaluated. Anomaly detection, personalization, autonomy, and continuous improvement cycles are also included.

Expert role existence and training plan/budget are considered. Dedicated team, leadership support, and long-term AI strategy/roadmap are examined. Data culture, data-driven decision making, and regulation/compliance processes are reviewed. Process integration, cross-functional teams, and operationalization speeds are also evaluated.

Progress is made visible by tracking development over time. The assessment is contextualized by comparing with industry standards. This approach enables tracking of the journey. Thus, areas of development become clear.

It provides detailed recommendations and a roadmap to improve AI capabilities/applications. The roadmap defines the steps to be implemented. Insights are based on assessment results. Thus, the improvement process gains a concrete framework.