In an industry where change is the only constant, few stories are as instructive as that of Braze's co-founder and CTO, Jon Hyman. Over nearly 15 years, he has guided the company's engineering organization through explosive growth and a radical shift to an AI-first mindset—all within a matter of months. This Q&A explores the key decisions, challenges, and philosophies behind that transformation, offering insights for leaders looking to navigate their own engineering evolution.
1. What drove Braze to transition from a traditional engineering team to an AI-first organization in just a few months?
The catalyst was the sudden maturity of generative AI and large language models. Jon Hyman recognized that these tools could fundamentally change how engineers solve problems, from automating code reviews to generating new features. Rather than a slow rollout, Braze chose a rapid, company-wide pivot. Hyman and his leadership team communicated a clear vision: every team would identify at least one AI-powered improvement within weeks. This urgency came from a belief that waiting would put them behind competitors who were already experimenting. The transformation was not about replacing engineers but augmenting their capabilities. By embracing AI as a core tool—not a side project—Braze aimed to double productivity while maintaining quality. The fast timeline was intentional to avoid the inertia of incremental adoption.
2. How did Jon Hyman prepare the engineering culture for such a drastic shift?
Hyman focused on three pillars: education, experimentation, and trust. First, he invested in training programs to demystify AI for all engineers. Lunch-and-learns, internal hackathons, and access to AI sandboxes helped staff feel comfortable with the new tools. Second, he encouraged a culture of rapid experimentation where failure was acceptable as long as lessons were shared. Teams were given the autonomy to test small AI integrations without lengthy approval processes. Third, he emphasized trust in the workforce. Rather than imposing mandates, Hyman asked each team to propose their own AI-first initiatives. This bottom-up approach fostered ownership and creativity. Regular all-hands meetings celebrated wins and discussed setbacks, reinforcing the idea that the shift was a collective journey. Within months, the culture evolved from cautious curiosity to active enthusiasm for AI-driven engineering.
3. What were the biggest challenges Braze faced during this transformation, and how were they overcome?
The main obstacles were technical debt, skill gaps, and resistance to change. Legacy systems made it hard to plug in AI models without significant refactoring. Braze tackled this by creating a dedicated platform team that built a middleware layer to abstract AI calls, allowing existing services to connect quickly. The skill gap was addressed through a blend of hiring AI specialists and upskilling existing engineers via online courses and mentorship. Resistance came from a few engineers who feared job displacement. Hyman countered this by demonstrating that AI would handle tedious tasks, freeing up time for creative and strategic work—which actually made roles more valuable. Regular one-on-ones and transparent communication about job security helped. By acknowledging concerns and providing concrete examples, the team built trust. The transformation succeeded because the leadership listened and adapted the approach based on feedback.
4. Can you describe a specific example of how Braze uses AI in its engineering workflow today?
One standout example is automated code review and quality assurance. Braze now uses AI models to scan every pull request for potential bugs, security vulnerabilities, and style inconsistencies before a human reviewer even sees it. This has reduced review time by 40% and caught issues that manual checks missed. Additionally, AI generates unit tests for new code blocks, ensuring better coverage. Another application is in feature development: engineers can describe a desired outcome in natural language, and an AI assistant suggests an initial implementation, which the engineer then refines. This has accelerated feature delivery by 30%. These tools are integrated directly into their CI/CD pipeline, making AI an invisible but powerful collaborator. The key was training the models on Braze's specific codebase and engineering standards, ensuring relevance and accuracy.
5. How did nearly 15 years of leadership experience shape Hyman's approach to this AI transformation?
Over a decade and a half, Hyman learned that successful engineering transformations require both strategic vision and tactical humility. Earlier in his career, he might have mandated changes from the top; but experience taught him that lasting change comes from empowering teams. He also understood the importance of patience—even in a rapid shift, some teams needed more time to adapt. His long tenure gave him a deep understanding of Braze's codebase, culture, and talent pool, enabling him to make informed decisions about where AI could have the most impact. Furthermore, he had built a leadership team that trusted his judgment and could execute quickly. The transformation was not a one-size-fits-all playbook but a tailored approach based on years of observing what works and what doesn't at Braze. Hyman's longevity allowed him to anticipate resistance and address it proactively.
6. What advice does Jon Hyman have for other CTOs looking to lead their engineering teams into the agentic era?
Hyman's first piece of advice is to start small but think big. Pick one measurable outcome—like reducing bug count by 20%—and let a single team experiment with AI to achieve it. Once that success is visible, others will want to join. Second, invest in your people's learning. AI is evolving fast; engineers need ongoing education, not just a one-time workshop. Third, build a culture of transparency about what AI can and cannot do. Overpromising leads to skepticism. Fourth, design for human-AI collaboration, not replacement. Finally, measure relentlessly to prove value to stakeholders. Hyman emphasizes that the agentic era is not about having the coolest tech—it's about using AI to solve real customer problems faster and better. For him, the journey is just beginning, and the most important trait a CTO can have is curiosity.