What does it actually look like to build software with AI today? Not in theory, but in practice.
What does it actually look like to build software with AI today? Not in theory, but in practice.
At the Leadership Exchange, this was the question at the center of the Developer Panel, where leaders from across the industry unpacked what’s really changing inside engineering teams and what organizations need to do right now to keep up.
The Developer Panel at the Leadership Exchange explored the cutting edge of AI in software engineering and examined what organizations should focus on today to prepare for the future. Moderated by Jeff Cross, Co-Founder & CEO at Nx, the panel featured Victor Savkin, Cofounder & CTO at Nx, Alex Sover, Vice President of Engineering at OpenAP, Brent Zucker, Senior Director of Engineering at Visa, and Jonathan Fontanez, AI Engineering Lead at This Dot Labs. Panelists shared insights into how AI is transforming the software development lifecycle and how teams can adopt tools effectively while preparing for organizational change.
Panelists discussed emerging workflows, including CI-in-the-loop, agentic healing, and context engineering. They examined how validation, code reviews, and PRDs are evolving alongside AI capabilities and how teams are integrating external sources such as production traces to improve quality and reliability. The discussion also covered what the next generation of agentic tools might look like and how these capabilities will shape engineering practices in the near future.
Adoption of AI comes with challenges. Teams often rely on plugins or extensions without foundational understanding, and individual contributors may fear displacement. Panelists emphasized that education, governance, and skill-building are essential for teams to manage AI agents effectively while maintaining quality. They also highlighted the need to standardize workflows and ensure organizational alignment to fully leverage AI capabilities.
The conversation extended beyond technical challenges to organizational implications. Panelists discussed how teams can avoid issues like Conway’s Law, manage distributed teams effectively, and evolve engineering practices alongside AI adoption. Leadership and management strategies play a crucial role in ensuring that AI integration delivers meaningful outcomes while maintaining efficiency and alignment with business objectives.
Key Takeaways
- AI workflows require both technical and organizational preparation.
- Education, governance, and skill development are essential for successful implementation.
- Forward-looking teams are rethinking validation, CI pipelines, and context management to fully leverage agentic AI.
The discussion highlighted that adopting AI at the cutting edge is not just about new tools - it is about rethinking processes, workflows, and organizational culture. Companies that embrace this holistic approach are most likely to succeed in leveraging AI to its full potential.
Are you interested in more conversations like this? Message us for an invite to the next, or for a private discussion around these topics. Tracy can be reached at tlee@thisdot.co....