Most teams focus on faster incident response. The real solution is preventing incidents from happening in the first place.

After 10+ years of being continuously on-call across multiple SaaS platforms, I've debugged production incidents, database failures, authentication service outages, and scaling crises. Each time, the immediate focus is the same: restore service, minimize customer impact, conduct a post-mortem. Most teams follow a structured incident response process, which is absolutely necessary for operational stability.

But here's what I've learned that most incident response frameworks miss: your operational pain is usually a symptom, not the disease.

Most people treat AI tools like a digital hoarding situation: dozens of half-finished conversations cluttering their workspace, making it impossible to find anything useful. The solution isn't better chat organization—it's a fundamental shift in how you approach AI collaboration. I delete almost every AI chat I have, and it's made me dramatically more productive. The key is a simple two-category rule: either I'm asking a specific question (delete after getting the answer) or I'm building something using artifacts as staging areas for development (save the result, delete the chat). This isn't about digital minimalism—it's about transforming AI from a conversation partner into a development tool. When you stop having endless discussions and start building tangible outputs, your AI workspace becomes as clean and purposeful as a well-managed inbox, unlocking measurable performance gains in how you work.

I had this beautiful dream once: perfectly structured JIRA tickets with clear requirements and proper hierarchies. Then I realized fast-moving teams naturally create content everywhere - Slack threads, Google Docs, meeting transcripts, architecture diagrams.

This isn't broken. It's what happens when teams are productive and moving fast. The problem isn't scattered content. It's that we're drowning in our own productivity.

The solution: Feed all that valuable content into AI project knowledge and let anyone ask natural language questions. No more single points of failure. Teams stay productive, and nobody drowns in their own success.

Every AI conversation starts the same way: explaining context you've already explained dozens of times before. By the time you finish setting the stage, you've burned half your thinking time on background instead of solving the actual problem.

I discovered something counterintuitive: the best AI projects don't just store information, they grow themselves. Each conversation becomes the foundation for better conversations. Instead of starting from scratch, you're building on increasingly sophisticated context. Your AI tools should learn and evolve with your work, not force you to start over every conversation.

When I was a backend team lead I would sometimes jump in and help during sprints by writing code or diving into operations. Occasionally I would even be the best person for the job because I had domain knowledge for that service or sub-system.

So why do I always prioritize dev work dead last on my list of to-dos?