Thesis Student (f/m/d) - Unleash the potential of conversational AI to democratize the EU-Taxonomy

We help the world run better. At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

Where you belong: As a thesis student in the team of the industry Division Automotive, you will work directly in the ID Management department and get all insights into one of the most exciting industries in Germany. We offer a unique work environment and as a fully‑fledged member of a highly motivated team you will have the opportunity to gain practical experience and theoretical know‑how. The individual support provided by a personal mentor ensures rapid familiarisation with the task environment.

The EU Commission aims to make Europe the world’s first climate‑neutral continent by 2050. To support this transition, the EU Taxonomy was introduced as a regulatory framework to distinguish sustainable from non‑sustainable economic activities. Starting in 2025, approximately 50,000 companies will be required to report the proportion of their business activities, CAPEX, OPEX, and revenues that are Taxonomy‑compliant.

A key bottleneck in this reporting process is the identification and screening of eligible business activities—a process that is currently manual, resource‑intensive, and prone to inconsistency. This complexity not only risks creating an unfair advantage for large, homogenous businesses but also undermines the EU’s goal of a fair and democratic green transition.

The aim of the thesis is to prepare and conduct expert interviews with five selected SAP customers to derive design principles for an AI‑based prototype from the customer's specific requirements.

What you’ll build

The aim of this thesis is to further mature and scientifically evaluate an existing AI‑supported prototype for semi‑automated eligibility screening under the EU Taxonomy. Following the principles of Design‑Science Research (DSR), the work focuses on the rigorous evaluation of the prototype in collaboration with several renowned SAP customers from different industries.

Building on a functioning prototype that conceptually operates as a “taxonomy robo‑advisor”, the thesis investigates how well the current artifact aligns with real customer processes, data landscapes, and interpretive requirements. To achieve this, the student will carry out a structured, multi‑method evaluation design that combines standardized customer interviews, scenario‑based prototype walkthroughs, and expert interviews with domain specialists (e.g., auditors, SAP architects, and sustainability experts).

The expert interviews will provide an additional analytical layer that goes beyond end‑user perspectives. They will help capture regulatory interpretation nuances, industry‑specific constraints, and architectural requirements that influence the determination of the eligibility screening scope. Together, customer interviews and expert interviews will allow for triangulation of insights, increasing the validity of the evaluated design principles.

The overarching goal is to better understand customers’ target processes as well as the data sources and interpretive rules they rely on when determining their eligibility screening scope. These findings will inform the refinement of the existing design principles embedded in the prototype and help identify the data foundations required for democratizing EU Taxonomy screening.

Because the empirical part requires methodological rigor, the student should be able to design and conduct standardized interviews and expert interviews, supported by qualitative analysis tools such as MaxQDA. Given that the artifact is AI‑based, foundational knowledge of artificial intelligence is essential for interpreting the empirical insights and translating them into technically grounded design principles.

• Descriptive evaluation: Analysis of customer processes, expert perspectives, and relevant data sources using standardized interviews and expert interviews.
• Prescriptive refinement: Validation, extension, or modification of existing design principles for the semi‑automated prototype based on triangulated empirical evidence.
• Improved artifact: Iterative refinement and presentation of an enhanced prototype mock‑up that reflects both customer needs and expert knowledge.

The results are intended to be published jointly with the student in a scientific journal. The publication process does not influence the grading but supports academic dissemination and strengthens the methodological contribution of the thesis.

What you bring

• Student (f/m) at a university or a university of applied sciences
• Preferred fields of study:
  • Computer Science, IT, AI Engineering, or related field
  • Knowledgeable in methodical work, especially in relation to expert interviews
• Design‑Science‑Research Knowledge is mandatory (ideally v. Brocke; Hevner; Peffers)
• Computer skills:
  • Natural Language Processing (NLP); Chat‑Bot; Large Language Models
  • IT Design and IT Architecture (technical & functional)
• Language skills:
  • German – as first language or at least fluent
  • English – fluent or first language

Your set of application documents should contain a cover letter, a resume in table form, school leaving certificates, current university transcript of records, and if available, references from former employers (including internships).

Work Experience

Practical experience with corporate customers and/or basic knowledge regarding EU Taxonomy desirable.

This is a SAP global, strategic, paid working student position that provides students with opportunities to find purpose in their careers.

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