aiqbench analysis
This study outlines a practical roadmap for embedded software organizations transitioning from AI-assisted workflows to autonomous agentic systems, addressing critical constraints like verification and traceability. The findings highlight how federated team structures and human-in-the-loop practices must evolve to sustainably integrate high-autonomy agents into safety-critical development environments.
Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development
arXiv:2609.30863v1 Announce Type: cross Abstract: The emergence of AI agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executing, and evaluating development tasks with increasing autonomy. This transition is particularly significant for embedded software organizations, where strict requirements for quality, traceability, verification, and long-term maintainability often apply. This paper presents a case study of a large embedded systems company and its transition toward becoming an AI-first organization. Through a mixed method, we analyzed data collected from a semi-structured workshop with 40 participants, including scrum masters, architects, management, and product owners. The findings show that the participants expect agentic AI to affect team structure, required competencies, organizational strategies, and developers' roles within the organization. Based on these findings, the paper discusses implications for federated AI team formation, human-in-the-loop practices in such an organization, and the sustainable adoption of AI agents in embedded software engineering. We also present a concrete roadmap for the organization towards becoming an AI-first organization.
Abstract:The emergence of AI agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executing, and evaluating development tasks with increasing autonomy. This transition is particularly significant for embedded software organizations, where strict requirements for quality, traceability, verification, and long-term maintainability often apply. This paper presents a case study of a large embedded systems company and its transition toward becoming an AI-first organization. Through a mixed method, we analyzed data collected from a semi-structured workshop with 40 participants, including scrum masters, architects, management, and product owners. The findings show that the participants expect agentic AI to affect team structure, required competencies, organizational strategies, and developers' roles within the organization. Based on these findings, the paper discusses implications for federated AI team formation, human-in-the-loop practices in such an organization, and the sustainable adoption of AI agents in embedded software engineering. We also present a concrete roadmap for the organization towards becoming an AI-first organization.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2609.30863 [cs.SE]
(or arXiv:2609.30863v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.30863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 258 words
Related coverage

News
Quantum Computing Report · Jun 7, 2026

Presentation: The Five Stages of AI Maturity in Engineering Organizations - Where and Why Teams Get Stuck
InfoQ · Aug 4, 2026

News
Quantum Computing Report · Jul 25, 2026

Presentation: Building Evals for AI Adoption: from Principles to Practice
InfoQ · May 29, 2026