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Master Thesis – Strategic Approach for Agentic AI Adoption in Sourcing

Sweden - Lundmid
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Job TitleMaster Thesis – Strategic Approach for Agentic AI Adoption in Sourcing Job Description Category Computer Science, Industrial Engineering, Supply Chain Management, AI & Data Science.

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Master Thesis – Strategic Approach for Agentic AI Adoption in Sourcing

Computer Science, Industrial Engineering, Supply Chain Management, AI & Data Science. Opportunity Mapping, Gap Assessment, and Feasibility Analysis.


Master Thesis

30 hp per student, conducted in pair

Spring 2027, flexible start

Axis Communications is constantly evolving how it manages its global supply chain to ensure robustness and quality. Recently, we have piloted the Supplier Quality Assistant Agent within the internal AI environment. This agent demonstrates the power of agentic workflows, where AI doesn't just chat, but reasons, uses tools, and follows complex process flows (e.g., gathering evidence, weighing it against Axis requirements, and classifying audit findings).


While the pilot proves the technical feasibility for specific quality audit tasks, there is a vast, untapped potential across Sourcing. Many processes in Sourcing are still characterized by repetitive, data-heavy, and rule-based manual work that is prime for automation through AI agents. However, moving from a single pilot to a scalable agentic strategy requires a structured approach to identify the right use cases, map the gaps in current data and processes, and assess the risks.


The thesis should assess whether an agentic approach is appropriate for each opportunity; simpler approaches such as workflow automation, search, or a conventional AI assistant may be more suitable in some cases.


  • Opportunity Mapping: Analyze current Sourcing workflows to identify repetitive, time-consuming tasks suited to agentic automation.
  • Gap Analysis: Evaluate data availability and process documentation. What is missing to make agents autonomous and reliable (skills, IT infrastructure, APIs, policies, process maps)?
  • Prioritization: Prioritization framework of available use cases.
  • Assess agentic feasibility: Assess whether an agentic approach is appropriate for each opportunity.
  • Risk & Constraint Assessment: Identify risks such as hallucinations in critical decisions, data privacy, and LLM dependency.
  • Agentic Design Framework: Define a conceptual "Mixture-of-Agents" structure (chains and handoffs) for complex end-to-end sourcing processes.

The work is expected to combine a literature review, stakeholder interviews, process mapping, and structured use-case prioritization. The research design, evaluation criteria, and methods will be refined with the students and their academic supervisor. 


The work will identify and prioritize potential use cases, assess what would be needed to make them feasible and reliable, and recommend practical next steps. The thesis is not expected to deliver production-ready software or guarantee efficiency gains.


Deliver a strategic approach and feasibility report clarifying required actions, expected efficiency gains, and a prioritized list of next-generation agents to build. 


We are looking for Master students with a strong interest in the intersection of Artificial Intelligence (specifically LLMs and Agentic Frameworks) and Business Process Optimization. You are likely studying Computer Science, Industrial Engineering, or a similar field with a focus on Digital Transformation or Supply Chain Management.You are analytical, enjoy "connecting the dots" between technical possibilities and business needs, and are comfortable interviewing stakeholders to uncover pain points in their daily work.


The project requires an understanding of LLMs (Large Language Models) and a conceptual grasp of how AI agents differ from standard chatbots. As well as strong analytical and documentation skills.


It would be appreciated if you also possessed knowledge of Supply Chain/Sourcing processes. As well as experience with prompt engineering or AI agent frameworks. And also an understanding of business process mapping (BPMN). 


If you're excited by the intersection of Agentic AI and global supply chain, we'd love to hear from you. There are a few things for you to keep in mind when applying:

  • Applications are accepted in both Swedish and English, and you apply via this posting.
  • The announced thesis is open only to students affiliated with a Swedish University/College either directly or via an exchange program.
  • When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
  • Please attach your CV and University/College grade summary.

Supervisors:

Christian Fagerlind (GSQ) Christian.Fagerlind@axis.com

Axel Mauritzson (CM) Axel.Mauritzson@axis.com@axis.com

Thesis Worker (Fixed Term)

2026-11-20

Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.

We enable a smarter and safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.


With around 6,000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.



For more information about Axis, please visit our website www.axis.com.

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Location & Eligibility

Where is the job
Sweden - Lund
On-site at the office
Who can apply
Same as job location

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October 23, 2026
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Master Thesis – Strategic Approach for Agentic AI Adoption in Sourcing