# Daniel Jones & Tomasz Maj - More software, faster - Odevo's AI Native transformation - AI DevCon '26

## Executive summary

Odevo details its AI-native transformation journey, moving from a highly heterogeneous tech landscape across multiple acquisitions and time zones. The core message is that adopting generative/agentic coding practices requires more than just providing licenses; it demands fundamental improvements in organizational processes, including robust CI/CD pipelines, standardized coding guidelines, and cultural shifts toward 'boldness' and experimentation. By systematizing training and focusing on failure modes, Odevo achieved significant metrics increases, such as 94% AI adoption among developers.

## Key takeaways

- AI Adoption Requires Process Maturity: Adopting agentic coding is not simple. Success hinges on foundational elements like having a reliable CI/CD platform, standardized coding practices, and comprehensive testing protocols. Without these fundamentals, generating code quickly will only expose bottlenecks elsewhere.
- The Importance of Discovery and Workshops: Successful AI transformation requires a 'discovery' phase to understand the current state (the 'lay of the land') and using workshops (e.g., employing techniques like TRIZ) to address employee fears, reduce hesitancy, and build consensus on necessary improvements.
- Focus on Failure Modes: Training should not focus only on the 'happy path.' It is crucial to teach developers about failure modes (e.g., context management, hallucination) and how agents operate in complex environments.
- The Biggest Impact is Boldness: The most significant outcome of the AI transformation was not just increased efficiency but a cultural shift toward 'boldness'—the willingness to try ambitious, end-to-end agentic workflows and rewrite large systems quickly.

## Technical details

- AI Tools & Models: The organization utilizes tools like GitHub Copilot, a proprietary chat solution built on OpenAI's API (to handle sensitive data), and Claude Code. The discussion references advanced models such as Opus 4.7.
- Agentic Coding & Workflow: The shift involves moving beyond individual story development to entirely AI-first approaches. Developers now perform multiple passes of 'agentic code review' before raising a Pull Request (PR), reducing human bottlenecks and self-merging.
- Software Development Lifecycle (SDLC): The talk emphasizes reassessing the SDLC to move away from traditional Scrum/Kanban methodologies when using advanced AI tools. The goal is re-empowering business users and product managers to become 'builders.'
- Technical Fundamentals: Key technical fundamentals required for success include robust CI/CD platforms, standardized coding guidelines, and effective testing mechanisms that agents can run.

## Practical implications

- Before adopting AI coding agents, conduct a thorough 'discovery' session to assess existing CI/CD maturity and development process bottlenecks.
- Implement structured training that focuses heavily on failure modes (e.g., context window management) rather than just successful use cases.
- Structure learning as an iterative journey with small, real-world tasks back at the developer's desk to solidify knowledge gained in workshops.
- Foster a culture of 'boldness' and experimentation by encouraging teams to tackle large, end-to-end agentic workflows.

## Topics

AI Transformation, Generative AI, Build Engineering, Software Development Lifecycle (SDLC), DevOps, The AI Native Dev Podcast, Dora Report (2025)

Source: https://www.youtube.com/watch?v=mB74LGAmmV0