# Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development

arXiv: https://arxiv.org/abs/2609.30863 · HTML: https://mlsanity.com/paper/2609.30863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Exploring and Characterizing Large Language Models For Embedded System Development and Debugging](https://mlsanity.com/paper/2307.03817/) (2023)
- [Building Your Own Product Copilot: Challenges, Opportunities, and Needs](https://mlsanity.com/paper/2312.14231/) (2023)
- [How Well Does Agent Development Reflect Real-World Work?](https://mlsanity.com/paper/2603.01203/) (2026)
- [Agent for User: Testing Multi-User Interactive Features in TikTok](https://mlsanity.com/paper/2504.15474/) (2025)
- [Agentic AI in Industry: Adoption Level and Deployment Barriers](https://mlsanity.com/paper/2605.14675/) (2026)
- [Securing LLM-Generated Embedded Firmware through AI Agent-Driven Validation and Patching](https://mlsanity.com/paper/2509.09970/) (2025)
- [Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective](https://mlsanity.com/paper/2511.06428/) (2025)
- [Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges](https://mlsanity.com/paper/2601.10220/) (2026)

## Abstract (arXiv)

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.