The software industry is currently obsessed with “AI-assisted coding.” We are equipping developers with powerful generative tools to write code faster than ever before. But if writing code was ever the constraint, it hasn’t been for decades.

If your developers write 50% more code, but your QA process, deployment pipeline, or requirements gathering remains static, you haven’t accelerated delivery—you’ve just created a traffic jam.

In this data-driven session, Steve Pereira will apply the principles of Flow Engineering to AI adoption. We will move beyond the hype to rigorous systemic analysis, using modeling and visualization based on Amdahl’s Law and Little’s Law to prove why optimizing non-bottlenecks (like coding speed) yields diminishing returns and often degrades system performance by flooding the system with WIP.

Whether it’s using LLMs upstream to clarify ambiguity in requirements, or downstream to automate compliance, the highest leverage points for AI are rarely in the IDE. Stop guessing where to put AI. Measure your flow, find your constraint, and apply intelligence where it matters.

Key Takeaways

1. System over Silo: Understand why optimizing local developer efficiency with AI often harms global system throughput.
2. Constraint-Driven Adoption: Learn to prioritize AI investments based on your actual bottlenecks (e.g., Testing, Requirements) rather than market hype.
3. Evidence-Based Tactics: Leave with a method to justify AI initiatives using hard data and collaborative modeling rather than vague promises of productivity.