Discovery

Awareness Building

AI Innovation Framework

Strategic Level

Tactical Level

Operational Level

Support & Sustainability

Digital maturity assessment

QA Test Client

Manual QA Engagement

← Back to Potenzialanalyse-Agenda

0 / — responses received

https://ai-strategy.pro/q/42f1043e-ade1-46b7-8ec5-b7f71c7bbbb0/reifegradmodell Digital maturity assessment

Overall score: 2.8 / 5

4 / 7 categories scored

Business model & strategy

• Is there a formulated AI strategy linked to the overall business strategy?
• What role should AI play in the future business model?
• Are there any AI pilot projects already underway?

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.

Organization & processes

• Are there dedicated roles or bodies responsible for AI topics?
• How are AI projects currently organized and governed?
• Are AI-related processes documented or standardized?

Level 1: Initiation

No organizational anchoring of AI.

Level 2: Development

First organizational structures and processes for AI projects.

Level 3: Application

AI is firmly established in selected processes.

Level 4: Integration

AI is integrated across departments.

Level 5: Optimization

AI is a central part of the organizational culture, with continuous process optimization.

People & culture

• How aware is the workforce of AI in general?
• What AI-related training has taken place so far?
• How open is the company culture toward change driven by AI?

Level 1: Initiation

Low awareness and low AI knowledge.

Level 2: Development

First training and awareness-building for AI.

Level 3: Application

Widespread AI competencies and acceptance in daily work.

Level 4: Integration

AI competencies are part of professional development, with interdisciplinary collaboration.

Level 5: Optimization

AI excellence across the workforce, continuous learning and an innovation culture.

Technology & data

• What does the current IT infrastructure look like?
• How accessible and well-structured is the existing data?
• Is there existing experience with AI tools or platforms?

Level 1: Initiation

Basic IT infrastructure, without AI orientation.

Level 2: Development

First AI-specific technologies and data structures.

Level 3: Application

Advanced technologies support AI applications; data is used systematically.

Level 4: Integration

Integrated technology platforms and a structured data strategy supporting AI.

Level 5: Optimization

Leading technology and data infrastructure for continuous innovation and real-time adaptation.

Processes & customers

• Is AI already used in operational processes?
• Are there any AI-supported touchpoints with customers?
• Where are the biggest potentials for process automation seen?

Level 1: Initiation

No use of AI in processes or customer interaction.

Level 2: Development

First pilot projects for process automation and customer interaction.

Level 3: Application

AI optimizes processes and improves customer interaction.

Level 4: Integration

Fully integrated, AI-supported processes and personalized customer interactions.

Level 5: Optimization

AI transforms processes and delivers tailored, real-time customer experiences.

Legal & ethics

• Are there guidelines for the ethical use of AI?
• How are legal risks of AI applications handled?
• Have employees been trained on legal and ethical aspects?

Level 1: Initiation

Little consideration of legal and ethical questions.

Level 2: Development

First thoughts on compliance, data protection, and ethics.

Level 3: Application

Clear guidelines for legal and ethical questions, training, and implementation.

Level 4: Integration

Comprehensive integration of legal and ethical standards, continuous review.

Level 5: Optimization

Pioneering role in developing and implementing best practices in law and ethics.

Data protection & compliance

• What is the current state of data protection for AI applications?
• Are there defined compliance guidelines for AI projects?
• How is compliance with data protection requirements monitored?

Level 1: Initiation

No consideration of data protection and compliance.

Level 2: Development

First engagement with data protection requirements.

Level 3: Application

Clearly defined data protection and compliance guidelines are implemented in AI projects.

Level 4: Integration

Data protection and compliance guidelines are fully integrated into all processes.

Level 5: Optimization

Data protection & compliance as leading standards, proactive measures, and an industry role model.