You promote your best engineer to team lead. Three weeks later, productivity has tanked and people are frustrated. Sound familiar?

Here's what most engineering leaders don't realize: this productivity drop is completely normal and predictable. When you promote your best engineer, you're getting hit twice. You lose your best individual contributor while the team figures out how to work together. Understanding the four types of engineering leadership helps explain why this transition is so challenging.

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.

Every engineering team is racing to implement AI tools, but most are optimizing the wrong variable. They're tweaking prompts and comparing models while ignoring the fundamental truth: your AI output quality is entirely dependent on your input quality. When you ask a default AI model a question, you're getting the average of the internet. Those 1,000 words generated from your 50-word prompt? They're coming from random web content, not your expertise. The companies winning with AI aren't using better models. They're feeding those models better inputs through curated knowledge bases, documented processes, and structured organizational wisdom. This isn't just theory. Research shows that RAG systems pulling from quality knowledge sources increase accuracy by nearly 40% compared to models operating on training data alone. For engineering leaders, this means the competitive advantage isn't the AI tool itself. It's the quality of information you feed it. Start building your knowledge systems now, because input quality isn't just a performance optimization. It's your strategic moat in an AI-commoditized world.

As engineering teams grow beyond 10-15 people, a predictable pattern emerges: the same leadership challenges surface repeatedly, regardless of industry or technology stack. Teams that understand this pattern scale smoothly. Teams that don't find themselves repeatedly hitting the same bottlenecks despite adding more engineers. Engineering leadership breaks down into four distinct areas, each requiring different skills and focus: People Management, Project Organization, Developer Experience, and Platform Architecture. Most growing teams try to handle all four areas without dedicated focus, creating predictable problems - unreliable deadlines, frustrated developers fighting their tools, team members leaving for better opportunities, and mounting technical debt. When teams recognize these four leadership areas early and plan for them intentionally, they avoid the common trap of overloading one person with responsibilities that require completely different skill sets. The transformation is remarkable - instead of engineering leaders burning out trying to handle everything, you get focused expertise that multiplies the effectiveness of the entire team.

Traditional war games fall apart when multiple teams try to learn incident response and team dynamics simultaneously. This two-phase approach separates the challenges: homogeneous sessions build incident response skills within existing teams, while heterogeneous sessions focus on cross-team coordination with a shared foundation.

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.

Time management lives alongside prioritization and communication as the foundational skills for being an effective team leader. Team leads (alongside everyone else in most organizations) have more work than they can handle, so what work should they do and when? The key to these decisions is understanding the three levels of time management.

How long do you spend talking about your burndown chart during your sprint retrospectives? Burndown charts tell you so much more than if you finished all the work in the sprint or not. From these charts, you can learn:

• How well your team is estimating story points

• How many injections occur during the sprint

• How well your team is breaking down tickets

• Bottlenecks in your team’s software development pipeline

We’ll walk through a few examples of burndown charts in this article and discuss what we can learn.

Slack Analytics are a powerful tool for understanding the way your company communicates. How and where your team chats is a pillar of your overall team communication structure.

Your product will reflect the positives and negatives of how your company communicates. In this article, you’ll learn how to analyze the single most critical Slack metric and take action to improve how your company communicates.

Busy, busy, busy.

In any company (small companies especially), it’s easy for leaders to get busy. Rushing between planning meetings, standups, retros, and one-on-ones, then trying to squeeze in some dev work as well. The work adds up quickly and you can barely catch your breath.

So with all this work, how do you know that you’re making progress? What if you’re just running in place? Let’s take a step back and evaluate the difference between doing the right work and just good work.

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?

I like to joke that every problem, at its core, is a communication problem.

It’s true more often than not, mainly when the problem involves well-meaning individuals.

Here’s a bit of a paradox: the better you are at solving SaaS production incidents, the harder each incident is to solve.

At first glance, this doesn’t make a lot of sense. Wouldn’t being better make solving production incidents easier? No. The trick is that once you get good at production incidents, you don’t get hit with the easy ones anymore: you solve them for good. That leaves only the new and challenging problems for you to solve. The average incident is more complex, but your reward is that the frequency of incidents goes way down.

I’d take that trade any day.

A little excitement in your job is usually a good thing. It could be learning a new development language, preparing to release a new feature, or taking on new responsibilities as part of a promotion. That’s great for most jobs, but not operations. Let me tell you why.

Production SaaS platforms require operations maintenance, support, tech debt payments, bug fixes, and more. This isn’t even counting the feature work customers, sales, and PM are asking for.

So with all this work to do, how can we manage what to do when? How can we as a team agree on our shared day-to-day priorities? This is a critical challenge to solve, especially now that remote work is so prevalent.

Planning is critical to running a successful War Game. One of the core goals is for the incident to feel real, so expect to spend 3-4x the amount of time planning the War Game as you spend running it.

In the first article of this series, we identified a few challenges of production incidents. They’re fast, filled with pressure, and are (hopefully) brand new failures. If the best-case scenario is a new failure (remember: repeated failures mean we never solved it the first time), how can we practice?

It’s 3 AM and your phone is ringing. There’s only one number you let ring through your Do Not Disturb settings. You open one eye and look at the first of 12 on-call notifications.

“Database down. Need help.”

It’s gonna be a long night.