Adeva

Every engineering team is feeling the same pressure right now - "can you do it with AI" Some are already shipping copilots and chatbots. Others are still trying to get their data in shape. But here’s what most teams miss - you can’t bolt AI onto an foundation that isn’t ready for it. AI readiness isn’t about having an ML team or the latest LLM API. It’s about five fundamentals: - Clean, structured data - APIs that actually talk to each other - Safe environments for experimentation - Real observability and feedback loops - Teams fluent enough in how AI behaves Before you build an AI feature, make sure your systems (and people) can sustain it. Because "AI-ready" isn’t a feature. It’s a foundation. https://lnkd.in/dWb9kF_u

Tech Leadership is changing fast. Our friends at LeadDev surveyed 600+ engineering leaders worldwide - and the results tell a clear story: AI, distributed teams, and tighter budgets are reshaping what "engineering leadership" actually means. The 2025 report breaks down how roles are shifting - from org structure and hiring trends to the tug-of-war between centralization and autonomy. If you’re planning next year’s org design, this report should be on your desk. Link in the first comment below.

If your hiring funnel takes 8 weeks, you’re not testing for quality. You’re testing for patience. Speed is a hiring signal. The faster team wins.

Why are we still pretending take-homes tell us anything? A six-hour coding task doesn’t measure real-world performance. It measures: • How much free time someone has. • How patient they are with pointless hoops. • How badly they want out of their current job. Here’s what actually predicts success: • Can they ship under constraints? • Can they communicate clearly with teammates? • Can they own outcomes, not just output? Hiring should measure impact, not endurance.

Most companies say they “empower engineers.” Few actually do. Without a clear growth framework, careers stall. Promotions become politics. Motivation drops. A good framework does 3 things: - Shows engineers what “good” looks like at every level - Aligns career growth with business impact - Builds a culture of continuous improvement The result? Engineers stay engaged. Teams move faster. Leaders retain talent.

AI isn’t going to kill the developer market because it’s “too smart.” It’s going to kill mediocre workflows because people start leaning on it without understanding what’s under the hood.

The best engineers don’t just fix bugs. They fix surprises... Fragile tests Missing ownership Undocumented edge cases They remove the things that burn weekends.

Waiting for the perfect hire is how roadmaps die. We’ve all seen it: • A critical role sits open for 6 months. • Deadlines slip. • Engineers burn out. Meanwhile, there are highly capable contractors who could start next week. Perfect is the enemy of delivery. Stop waiting for unicorns. Start hiring for velocity.

Most engineering leaders underestimate the cost of context switching. One engineer bouncing between 3 projects? That’s not 100% utilization. That’s wasted attention. Great teams don’t maximize “busy.” They minimize fragmentation.

Everyone’s being asked the same question right now: “Where’s our AI feature?” But here’s the catch: AI doesn’t fail because of the models. It fails because of everything around them: messy data, no sandbox to experiment, zero monitoring. In this week’s Engineering Lead Insights, we break down 5 signs your stack is (isn't) AI-ready, plus an exclusive conversation with Laura Tacho (CTO at DX) on how DevEx + AI shape team performance in 2025.