AI-Enabled Systems Engineering: How Is Generative AI Changing Systems Engineering Roles, Methods, and Processes?

1. Background and Motivation

Systems Engineering integrates technical disciplines and coordinates decisions throughout the life cycle of complex systems. Its core activities include analyzing stakeholder needs, specifying requirements, developing system architectures, modeling system behavior and structure, conducting trade-off analyses, managing risks, and planning verification and validation.

Generative AI can support many of these activities. It can structure information, analyze and review requirements, identify potential inconsistencies, explain system models, generate alternatives, and prepare engineering reviews. The decisive question, however, is not merely which tools are available. Organizations need to understand how Generative AI changes the division of work, engineering roles, methods, decision rights, competencies, and accountability.

Some activities may become largely automated. Others may be fundamentally redesigned around collaboration between engineers and AI. Human judgment and accountability will remain essential for safety-critical, normative, strategic, and highly context-dependent decisions. Development organizations therefore require a coherent target state for AI-enabled Systems Engineering.

2. Objective

The objective of this thesis is to develop an empirically grounded Target Operating Model for AI-enabled Systems Engineering.

The central research question is:

Which Systems Engineering activities can be automated or augmented by Generative AI, which activities will fundamentally change, and where do human decision-making and accountability remain indispensable?

The thesis should include the following activities:

  • Structure Systems Engineering activities along an established system life-cycle or process framework.
  • Develop a taxonomy for classifying Systems Engineering activities, for example:
    • largely automatable,
    • AI-assisted or augmented,
    • performed collaboratively by humans and AI,
    • necessarily subject to human decision and accountability.
  • Define evaluation criteria such as standardizability, context dependency, uncertainty, criticality, explainability, verifiability, and accountability.
  • Assess the effects of Generative AI on engineering roles, competencies, methods, governance, and collaboration.
  • Validate the findings through expert interviews, workshops, or case studies in development organizations.
  • Develop a Target Operating Model with specific design options and implementation pathways.

The Target Operating Model should address at least the following dimensions:

  1. processes and AI-enabled workflows,
  2. roles and responsibilities,
  3. decision and escalation rights,
  4. methods and engineering artifacts,
  5. data, model, and tool landscape,
  6. governance and quality assurance,
  7. competencies and organizational learning, and
  8. performance and impact measurement.

Expected results include a Systems Engineering task and automation portfolio, a future role and competency model, design principles for human–AI collaboration, a Target Operating Model, and a roadmap for implementing AI-enabled Systems Engineering.

3. Profile

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

The ideal candidate has:

  • a good understanding of product development or Systems Engineering,
  • an interest in organizational design and human–AI collaboration,
  • the ability to combine technical and organizational perspectives,
  • knowledge of qualitative research methods, particularly expert interviews and structured content analysis,
  • a basic understanding of Generative AI and data-driven systems,
  • strong analytical and conceptual skills, and
  • an interest in developing an academically grounded but practically applicable target model.

4. Contact

Prof. Dr. Clemens van Dinther

3DSE Management Consultants GmbH

c.vandinther∂3dse.de

5. Starting Literature