Short:

AI has made "code complete" nearly instantaneous, but it has created a massive friction point: pipelines designed for human-speed are now flooded with machine-speed workloads. This "heat" stalls delivery just as we should be accelerating. To survive, we must move beyond traditional CI/CD and embrace Intelligent Delivery.

In this talk, we’ll explore how to use AI and automation to lubricate the path to production. We’ll dive into the architecture of self-orchestrating pipelines, focusing on:

The AI-Augmented Paved Path: Using LLMs for automated triage and reviews.

Machine-Speed QA: Transitioning to dynamic, AI-driven testing.

Reducing Cognitive Load: Filtering the "noise" of generated code to free up senior engineers.

Learn to fight fire with fire—leveraging AI to eliminate the very bottlenecks it created.

Long:

AI has made "code complete" nearly instantaneous, but it has also created a massive friction point: a delivery pipeline designed for human-speed development is now being asked to process a machine-speed workload. This "heat" is slowing us down just when we should be moving faster.

The answer isn't to throw more humans at the problem—it’s to automate the gatekeepers.

To survive the AI revolution, we must move beyond traditional CI/CD and embrace Intelligent Delivery. This talk explores how to use AI and advanced automation to lubricate the path from "commit" to "production," turning the friction of manual reviews and rigid testing into high-velocity traction.

We will dive deep into the architecture of a self-orchestrating pipeline, focusing on:

* The AI-Augmented Paved Path: How to implement automated "guardrails" that use LLMs to perform first-pass code reviews, vulnerability triaging, and documentation checks.

* Smoothing the Flow with Machine-Speed QA: Moving from static testing to dynamic, AI-driven test generation that matches the scale of your incoming code tsunami.

* Removing the Human Friction: Strategies for reducing cognitive load on senior engineers by using automation to filter the "noise" of generated code, leaving only the high-impact decisions for the humans.

* From Heat to Traction: A blueprint for a "self-healing" pipeline that identifies deployment friction in real-time and adjusts gatekeeping logic automatically.

You’ll leave this session with a concrete strategy for fighting fire with fire—leveraging the very technology that created the bottleneck to finally eliminate it.

Key Learnings:
* Audit AI Bottlenecks: Identify where machine-speed code causes human-speed friction.

* Automate Cognitive Gatekeeping: Deploy LLM-based triage to eliminate manual review noise.

* Design High-Traction Guardrails: Build "paved paths" that balance velocity with safety.