Duncan Grazier

A few years ago, I wrote about the creeping danger of debt. Debt being the slow accumulation of manual processes, fragile deployments, and tribal knowledge that eventually drowns engineering teams in busywork. The message was simple: automate or die (slowly).

Fast forward to 2026, and we’re not just talking about bash scripts and CI/CD pipelines anymore. AI has fundamentally changed what’s possible in automation, and importantly, it’s no longer theoretical. The tools are here, they’re mature enough to deliver real value, and teams that aren’t leveraging them are already falling behind.

So what can you do this week to reduce busywork, ship “faster”, and let your team focus on building instead of grinding.

The New Automation Frontier

Pre-AI automation required you to discover every scenario, write explicit rules, and maintain scripts that broke whenever thins changed. AI-powered automation learns patterns, adapts to context, and handles the messy, ambiguous problems that used to require human judgment.

The difference is striking. Instead of automating only the repetitive and predictable, we can now automate that adaptation.

AI-Driven Code Review and Testing

Use AI to generate comprehensive test cases based on your code changes. I’ve seen teams reduce their test-writing time dramatically while actually improving coverage. The AI catches edge cases humans miss because it doesn’t assume the happy path.

For code review, tools like CodeRabbit or PR-Agent can provide first-pass reviews that catch common issues before human reviewers ever look. This isn’t about replacing senior engineers. Allow the conversation to focus on “you forgot to handle the null case” to “should this be a separate service?”

Start by adding an AI code review bot to your next PR. See what it catches, and adjust your process based on what adds value.

Intelligent Incident Response

This is how it happens today: an alert fires. Someone gets paged. They grep through logs, check Datadog’s dashboards, correlate events, and eventually find the root cause after a few incorrect assumptions.

This is how it should & will work: AI agents that ingest your logs, metrics, and traces in real-time, automatically correlate anomalies, and present probable root causes with supporting evidence where your engineers are working.

Tools like BigPanda, Moogsoft, or even custom solutions using LLM APIs can reduce your mean time to resolution (MTTR) while reducing the cognitive load on your on-call engineers Your senior engineers stop spending their nights debugging and start spending their days building.

Start feeding your incident data to an LLM. Ask it to summarize incidents, identify patterns, or suggest runbooks.

Self-Optimizing CI/CD Pipelines

Your CI/CD pipeline is a house of cards built on flaky tests, slow builds, fragile dependency trees, and every optimization requires manual work.

AI can analyze your pipeline performance, identify bottlenecks, and automatically adjust parallelization, caching strategies, and test ordering to minimize build times.

GitHub Actions and GitLab CI now have AI-powered features that learn from your build history. They can predict which tests are most likely to fail based on code changes and run those first. They can identify flaky tests and auto-retry them with exponential backoff.

Build times dropping from 20 minutes to 8 minutes might not sound revolutionary, but multiply that across hundreds of daily builds and thousands of developer hours.

You can audit your slowest pipelines, implement intelligent test ordering and parallel execution today.

The Mindset Shift

The technology isn’t the hard part. Shifting your entire organizations mindset is:

1. The teams winning with AI aren’t the ones with elaborate strategies, they’re the ones shipping experiments every week and learning what works.
2. The goal is augmentation, not full automation. AI won’t replace your engineers.
3. Every AI implementation should have a clear metric: reduced MTTR, faster builds, fewer production bugs, less time spent on toil.

Avoiding the Traps

1. AI will happily scale your dysfunction. Fix the process first, then automate it.
2. If your team doesn’t understand what the AI is doing, they won’t trust it.
3. AI systems need continuous feedback to improve. Build that into your workflow from day one.

Start Small, Think Big

You don’t need to rebuild your entire engineering organization to start seeing value from AI. Pick one painful process, automate it with AI, measure the impact, learn and iterate. The teams that win aren’t the ones with the best AI strategy but they are the ones learning the fastest.