AI-Driven Development at Instacart: Scaling Impact and Increasing Velocity
Instacart's AI adoption began as informal, grassroots experimentation by individual engineers. To make adoption consistent and durable at organizational scale, they needed a structured approach beyond organic Slack threads and demos.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · AI tools embedded in daily tasks
Engineers embed AI tools directly into day-to-day engineering tasks across the full development lifecycle.
Tools used
AvaCursorGleanClaude-3.7Claude-3.5
Outcome
Instacart achieved up to 20% time savings on frontend workflows for Fizz, shipped Fizz from concept to customer-ready in a few short months, and cleaned up 15+ feature flags from a single service in a single PR using an AI coding agent. Tasks that traditionally took several days were completed in hours.
What failed first
AI coding agents struggled with large legacy monolith codebases where unclear boundaries led to slower suggestions, incomplete answers, or hallucinated code. Very large files (5,000+ lines) caused repeated task failures, and AI code translation sometimes carried over flawed logic without correcting it.