Short form:
AI code reviews often feel like tossing a coin. Heads - you get endless nitpicks about style. Tails - it finds real issues. And even when the signal is good, are we really getting quality and productivity benefits?
The secret isn't the AI, it's how we're deploying it. This talk explores what actually drives value in AI-assisted code reviews: rich contextual integration, scalable specialization, and measurable impact on developer productivity. Learn to transform AI reviews from a compliance checkbox into a strategic advantage for your engineering organization.

Long Form:
Most AI code review implementations focus on the wrong metrics—counting comments generated or code accepted rather than measuring developer velocity and code quality improvements. The real value lies in intelligent context integration and organizational learning at scale.
This talk examines successful AI code review deployments across engineering organizations, revealing four critical success factors: seamless integration with your existing development context (codebase, tickets, architectural decisions), specialized review guidelines that scale from 5 to 50,000 repositories, comprehensive observability to understand where AI adds value versus where it creates noise, and strategic human-AI collaboration patterns.
We'll explore real case studies of teams who've moved beyond basic linting to utilizing AI code review that understands business logic, catches architectural anti-patterns, and actually accelerate PR cycles. You'll learn when AI reviews shine, when they don't, and how to measure the difference.
This isn't about replacing human reviewers—it's about building an intelligent system that amplifies human expertise and reduces cognitive load where it matters most.

Key Takeaways:

- Context advantage: How connecting AI reviews to codebase history, work tickets, and architectural documentation transforms generic feedback into business-aware insights that actually matter
- Scaling specialization: Navigate the challenge of maintaining review guidelines across thousands of repositories—from monorepo strategies to automated guideline propagation and conflict resolution
- Observability and measurement: Build dashboards that track AI review impact on PR cycle times, code quality metrics, and developer satisfaction—plus warning signs that your AI is becoming a rubber stamp
- Strategic usage patterns: Learn when to lean into AI reviews (security patterns, performance anti-patterns) versus when human judgment is irreplaceable (architectural decisions, product trade-offs)
- Human-AI collaboration patterns: Discover proven workflows where AI handles pattern matching and context synthesis while humans focus on architectural decisions and mentoring junior developers