1. Background and Motivation

Many organizations have tested initial Generative AI use cases and demonstrated technical feasibility through proofs of concept. However, the transition from isolated pilots to stable, widely adopted, and economically valuable solutions remains challenging. Current studies indicate that AI use is increasing rapidly, while scaled deployment and measurable enterprise-level value continue to fall short of expectations.

Successful scaling requires more than access to high-performing models. Organizations need suitable data and platform architectures, reusable technical components, effective governance, clearly assigned responsibilities, employee capabilities, workflow integration, and systematic performance measurement. Organizations that generate value from Generative AI tend to redesign workflows, embed AI into business processes, and establish feedback and KPI mechanisms rather than treating AI as an additional stand-alone tool (McKinsey, 2025).

Development organizations face additional challenges, including sensitive product knowledge, intellectual property, heterogeneous engineering data, long product life cycles, and strict requirements for quality and traceability. Industrializing Generative AI is therefore not merely a technical deployment task. It represents a transformation of processes, architecture, governance, organization, and capabilities.

2. Objective

The objective of this thesis is to develop and validate a 3DSE AI Transformation Maturity Model for development organizations, including a practically applicable assessment instrument.

The central research question is:

Which technical, organizational, and economic capabilities do development organizations need to transform Generative AI pilots into scalable solutions that deliver measurable business value?

Supporting research questions include:

• Which recurring barriers prevent technically successful proofs of concept from entering productive and scaled use?
• Which capabilities distinguish isolated experimenters from organizations that systematically scale Generative AI?
• How can organizational maturity, capability gaps, and expected value be assessed?
• Which development paths are appropriate for different organizational starting points?

The thesis should include the following activities:

• Review scientific and practitioner-oriented approaches to AI scaling, organizational readiness, governance, and maturity assessment.
• Conduct expert interviews or case studies in development organizations.
• Identify and structure the relevant maturity dimensions, including:
  • strategy and use-case portfolio,
  • business case and value tracking,
  • processes and workflow integration,
  • data and intellectual property,
  • IT, LLM, and integration architecture,
  • governance, risk, and compliance,
  • roles, competencies, and operating model,
  • adoption and change management,
  • operations, evaluation, and continuous improvement.
• Define transparent maturity levels and assessment criteria.
• Develop an assessment questionnaire, scoring model, and maturity profile.
• Pilot and validate the assessment with selected experts or organizations.
• Derive maturity-specific recommendations and a transformation roadmap.

The expected results are:

1. a scientifically grounded 3DSE AI Transformation Maturity Model,
2. an assessment questionnaire with a transparent scoring methodology,
3. a visual maturity profile,
4. a catalog of recurring scaling barriers and success factors, and
5. a roadmap from isolated pilots to industrialized Generative AI use.

A combination of Design Science Research, qualitative empirical research, and established maturity-model development methods is recommended.

3. Profile

We are looking for a student in Information Systems, Industrial Engineering, Technology and Innovation Management, Business Administration, Computer Science, or a comparable degree program.

The ideal candidate has:

• an interest in AI transformation, organizational development, and product development,
• an understanding of the interaction between strategy, processes, data, IT, and organization,
• knowledge of qualitative and, optionally, quantitative research methods,
• an interest in expert interviews, case studies, and the development of assessment instruments,
• basic knowledge of Generative AI, IT governance, or digital transformation,
• a structured, analytical, and results-oriented working style, and
• an interest in translating research findings into a practical consulting instrument.

4. Contact

Prof. Dr. Clemens van Dinther

3DSE Management Consultants GmbH

c.vandinther∂3dse.de

5. Starting Literature

• Jöhnk, J.; Weißert, M.; Wyrtki, K. (2021): Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20.
• Mikalef, P.; Gupta, M. (2021): Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance. Information & Management, 58(3), 103434.
• Schneider, J.; Kuss, P.; Abraham, R.; Meske, C. (2024): Governance of Generative Artificial Intelligence for Companies.
• McKinsey & Company (2025): The State of AI: How Organizations Are Rewiring to Capture Value.
• NIST (2024): Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1.
• ISO/IEC (2023): ISO/IEC 42001:2023—Artificial Intelligence Management Systems.
• European Union (2024): Regulation (EU) 2024/1689—Artificial Intelligence Act.
• Becker, J.; Knackstedt, R.; Pöppelbuß, J. (2009): Developing Maturity Models for IT Management—A Procedure Model and Its Application. Business & Information Systems Engineering, 1(3), 213–222.

Peffers, K. et al. (2007): A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77.