QA Test Client — Manual QA Engagement
How would you rate the AI maturity level in each of the following categories?
Level 1: Initiation
Initial thoughts on AI use, no clear strategy.
Level 2: Development
Development of first AI strategies, pilot projects, initial thoughts on transforming the business model.
Level 3: Application
Clear AI strategy aligned with business goals, business model adapted to AI.
Level 4: Integration
AI is fully integrated into the business model, driving innovation and market opportunities.
Level 5: Optimization
AI strategy leads to new business models; the company uses AI to reshape markets.
No organizational anchoring of AI.
First organizational structures and processes for AI projects.
AI is firmly established in selected processes.
AI is integrated across departments.
AI is a central part of the organizational culture, with continuous process optimization.
Low awareness and low AI knowledge.
First training and awareness-building for AI.
Widespread AI competencies and acceptance in daily work.
AI competencies are part of professional development, with interdisciplinary collaboration.
AI excellence across the workforce, continuous learning and an innovation culture.
Basic IT infrastructure, without AI orientation.
First AI-specific technologies and data structures.
Advanced technologies support AI applications; data is used systematically.
Integrated technology platforms and a structured data strategy supporting AI.
Leading technology and data infrastructure for continuous innovation and real-time adaptation.
No use of AI in processes or customer interaction.
First pilot projects for process automation and customer interaction.
AI optimizes processes and improves customer interaction.
Fully integrated, AI-supported processes and personalized customer interactions.
AI transforms processes and delivers tailored, real-time customer experiences.
Little consideration of legal and ethical questions.
First thoughts on compliance, data protection, and ethics.
Clear guidelines for legal and ethical questions, training, and implementation.
Comprehensive integration of legal and ethical standards, continuous review.
Pioneering role in developing and implementing best practices in law and ethics.
No consideration of data protection and compliance.
First engagement with data protection requirements.
Clearly defined data protection and compliance guidelines are implemented in AI projects.
Data protection and compliance guidelines are fully integrated into all processes.
Data protection & compliance as leading standards, proactive measures, and an industry role model.