The CTO's Guide to Building an AI-First Engineering Organization

Introduction

In today's rapidly evolving tech landscape, the shift toward AI-first engineering is no longer optional—it's a competitive necessity. Jon Hyman, co-founder and CTO of Braze, led his engineering team through nearly 15 years of growth and successfully transformed it into an AI-first organization in just a few months. This guide distills his approach into actionable steps you can follow to reengineer your own engineering team for the agentic era. Whether you're a CTO, VP of Engineering, or a team lead, these strategies will help you navigate the transition with clarity and confidence.

The CTO's Guide to Building an AI-First Engineering Organization — Source: stackoverflow.blog

What You Need

Executive Buy-In: Support from the CEO and board to allocate resources and prioritize the transformation.
Clear Vision: A well-defined goal for what “AI-first” means for your specific company and product.
Skilled Talent: Access to data scientists, ML engineers, and AI-aware software engineers (or a plan to upskill existing staff).
Data Infrastructure: Clean, accessible datasets and the tools to manage them (e.g., data lakes, feature stores).
Budget for Experimentation: Time and funding for pilot projects, training, and new tooling.
Cultural Readiness: A team open to change and willing to adopt new workflows and technologies.

Step-by-Step Guide

Step 1: Assess Your Current Engineering Maturity

Before you can transform, you need to understand where you stand. Evaluate your team's current processes, tech stack, and skill levels. Ask yourself:

How much of our work is manual vs. automated?
Do we have a data-driven culture, or do decisions rely on gut feeling?
What AI capabilities already exist within the team (e.g., ML models in production)?

Conduct surveys, hold workshops, and review project histories. Hyman's approach at Braze started with an honest audit of their strengths and gaps, which informed every subsequent move.

Step 2: Define Your AI-First Vision

Create a clear, inspiring vision of what AI-first means for your organization. This goes beyond simply “use AI” — it means embedding intelligence into every layer of the engineering stack: development workflows, code review, testing, product features, and user experience. At Braze, Hyman communicated that the goal was to make AI a seamless part of how engineers solve problems, not an afterthought. Document this vision and share it broadly to align the team.

Step 3: Identify Low-Hanging Fruit

Choose 2–3 high-impact, low-risk areas where AI can deliver immediate value. Examples include:

Code generation and review: Use LLMs to assist with boilerplate code, unit tests, or documentation.
Intelligent alerting: Apply ML to reduce false positives in monitoring.
Personalized product features: Integrate a recommendation engine into your product.

Braze started by automating parts of their development pipeline and quickly validated the benefits, building momentum for larger initiatives.

Step 4: Upskill Your Team

Invest in training programs to bridge the AI knowledge gap. This doesn't mean turning every engineer into a data scientist. Instead, focus on:

Basic AI literacy: Workshops on how to interact with AI models, interpret outputs, and understand limitations.
Hands-on projects: Internal hackathons or sprint projects where engineers apply AI tools to real problems.
Pairing with experts: Partner experienced ML engineers with generalists to accelerate learning.

Hyman emphasized that at Braze, the transformation was a team-wide effort, not just a top-down mandate. They created a culture where learning AI was seen as an opportunity, not a burden.

Step 5: Implement AI Tools and Infrastructure

Select and deploy the right tools to support your AI-first vision. This includes:

AI coding assistants: GitHub Copilot, Amazon CodeWhisperer, or similar.
ML platforms: SageMaker, Vertex AI, or open-source alternatives like MLflow.
Data pipelines: Ensure smooth integration between data sources and AI models.
Observability: Monitor AI behavior in production (drift, hallucinations, latency).

Braze adopted a phased approach—starting with a small set of tools, then expanding as the team gained confidence.

Step 6: Pilot and Measure

Run a pilot project for each identified opportunity. Define clear success metrics: accuracy, speed, developer satisfaction, customer impact. Set a short timeline (e.g., 2–4 weeks) to keep momentum. Hyman stressed the importance of measuring outcomes objectively and not being afraid to kill failed experiments quickly. At Braze, early pilots informed the broader strategy and helped secure further investment.

Step 7: Scale and Iterate

Once pilots prove successful, scale them across the organization. This means:

Standardizing workflows: Establish best practices for using AI in development.
Automating governance: Implement guardrails for responsible AI use (bias, security, compliance).
Continuous learning: Hold regular retrospectives to refine processes.
Celebrating wins: Share success stories internally to reinforce the cultural shift.

Braze's transformation happened in months because they moved quickly from pilot to organization-wide adoption, constantly iterating based on feedback.

Tips for Success

Start small, think big. Avoid trying to overhaul everything at once. Focus on a few high-impact areas and expand from there.
Communicate constantly. Keep the entire team informed about milestones, changes, and why AI-first matters. Transparency reduces resistance.
Invest in data quality. AI models are only as good as the data they're trained on. Prioritize data hygiene from the start.
Embrace experimentation. Not every AI initiative will succeed. Treat failures as learning opportunities and iterate quickly.
Measure what matters. Tie AI initiatives to business outcomes like productivity gains, revenue impact, or customer satisfaction.
Lead by example. As a leader, use AI tools yourself and share your experiences. Your enthusiasm will be contagious.

Transforming an engineering organization into an AI-first powerhouse is challenging but achievable. By following these steps—assessing your current state, defining a clear vision, starting with small wins, upskilling your team, implementing the right tools, piloting rigorously, and scaling thoughtfully—you can replicate the success that Jon Hyman and the Braze team achieved. The agentic era demands bold action. Start today.

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