From Co-founder to AI-First: Inside Braze's Engineering Evolution Under CTO Jon Hyman

In the fast-paced world of customer engagement, Braze has long been a standout platform. But behind the product lies a story of engineering leadership that spans nearly 15 years. At the helm is Jon Hyman, co-founder and CTO, who has navigated the company from a scrappy startup to a mature, AI-first engineering organization—all within the span of a few transformative months. Here's how he rethought engineering for the age of intelligent automation.

The Journey of a Co-founder CTO: Scaling Braze's Engineering Over 15 Years

When Jon Hyman co-founded Braze in 2011, the engineering team was small, agile, and focused on building a reliable customer engagement platform. Over the years, as the company grew to serve thousands of enterprise clients, Hyman's role evolved from writing code to architecting a culture of scalable innovation.

Lessons from the Growth Curve

Hiring for adaptability became a cornerstone. Rather than seeking specialists in one domain, Hyman prioritized engineers who could pivot as business needs shifted. This flexibility proved critical when Braze decided to embrace artificial intelligence across its stack. Instead of a painful restructuring, the team already had the mindset to absorb new tools and methodologies quickly.

Another key lesson was decentralizing decision-making. As Braze expanded internationally, Hyman empowered engineering leads in each region to make autonomous calls on architecture and process. This reduced bottlenecks and accelerated delivery—a move that later paid dividends during the AI transformation.

The Pivot to AI-First: A Complete Overhaul in Months

While many companies spend years tinkering with AI, Braze executed a full transformation in just a few months. Hyman describes the process as both a technological and cultural shift. The goal was not merely to add AI features, but to make AI the foundation of engineering decisions—from predictive models that optimize message timing to automated content generation.

Challenges and Strategies

The biggest obstacle was data readiness. Braze's platform processes billions of customer interactions daily; ensuring that data was clean, structured, and accessible for machine learning required significant investment in data pipelines and governance. Hyman's team tackled this by creating cross-functional squads of data engineers, ML specialists, and product managers—collaborating in two-week sprint cycles.

Another challenge was up-skilling the existing workforce. Not every engineer had deep AI expertise, so Braze launched internal bootcamps and paired junior developers with AI veterans. This approach fostered a growth mindset and reduced resistance to change. According to Hyman, the key was to “show, not tell”—demonstrating quick wins with AI prototypes that solved real customer pain points.

Finally, Hyman emphasized continuous learning. The engineering org adopted a culture of A/B testing for everything, including their own AI models. This allowed teams to iteratively improve and course-correct without waiting for annual reviews.

Key Takeaways for Engineering Leaders

Braze's experience offers valuable lessons for any CTO or engineering leader considering an AI-first transformation:

• Start with culture, not technology. A team that embraces adaptability and constant learning will adopt AI tools far faster than one that clings to legacy processes.
• Invest in data infrastructure early. Clean, accessible data is the fuel for AI. Without it, even the most sophisticated algorithms will fail.
• Build cross-functional AI squads. Bring together engineers, data scientists, and product managers to ensure AI solutions are both technically sound and aligned with customer needs.
• Celebrate small wins. Use rapid prototypes to demonstrate value and build momentum. Visible successes encourage broader adoption across the organization.

Jon Hyman's journey from co-founder to leader of an AI-first engineering organization shows that transformation doesn't have to be slow. With the right mindset, team structure, and a relentless focus on data, any engineering team can make the leap—just as Braze did in only a few months.

For more insights on engineering leadership and AI transformation, explore our other articles on scaling teams and rapid cultural pivots.