The age of AI experimentation is over. A new industry report, analyzing over 135,000 developers, confirms that AI-assisted engineering is now ubiquitous, with adoption soaring past 91%. On the surface, the ROI seems obvious. The data shows: * Developers are saving an average of 3.6 hours per week. * Daily
The buzz around AI in software development has been deafening. From code generation to automated reviews, the promises are grand: faster delivery, fewer bugs, and happier developers. But for engineering leaders and savvy developers, the question has always been: "where's the proof?" Can AI truly deliver
AI isn’t just transforming engineering metrics; it’s forcing all of us to rethink what success in software delivery really means. When I first started tracking DORA and SPACE frameworks, both were considered gold standards for quantifying how well teams deliver software: DORA with its sharp focus on speed
When software delivery feels chaotic, characterized by stagnant deployment frequency, creeping lead times, and an alarming change failure rate, the engineering data you already collect is the key to clarity. As an engineering leader, I once faced the piercing question: “Where exactly does our time go?” That moment launched me
Every engineering leader knows the tension between ambitious goals and limited bandwidth. Balancing people, priorities, and timelines isn’t just about who’s available; it’s about ensuring your teams can deliver sustainably, without constant context-switching or burnout. That’s where engineering resource planning comes in. It’s more than
The buzz is deafening. AI is here, and according to the latest 2025 State of Engineering Management Report, 90% of engineering orgs are already using AI-assisted coding tools, seeing an average 25% productivity jump. That's fantastic. But as a leader, you know that "productivity" number hides
I've been watching the enterprise software space closely, and this week's announcements from Salesforce and Amazon have me convinced we're at an inflection point. Agentic AI platforms are no longer futuristic concepts; they're here, and they're going to fundamentally change
I've been observing how the software engineering landscape is experiencing unprecedented transformation throughout 2024. A recent research surveying hundreds of engineering professionals reveals critical insights into the evolving state of engineering management and the challenges teams are facing as we move into 2025. A State of Constant Transformation
Bugs 🐞 , They're the people who show up at every software development party without being asked. In fact, more than a third of developers spend a huge 25% of their time just fixing bugs. 🐛 It feels like the bugs are in charge when you are stuck in a cycle
What does "quality" really mean to you as a leader in engineering? We get stuck on old ways of measuring things too often, like uptime or how many bugs were fixed. But that's a limited view. Let's look at it differently. Quality is the
As a leader in a tech organization, you’re no stranger to the ever-expanding lexicon of agile development. Sprint, velocity, stand-up… it can feel like a full-time job just keeping up. But amidst all the terminology, some concepts are more than just buzzwords—they are powerful levers for focus, alignment,
Engineering Excellence
This is one of the most common problems I hear from Directors and VPs of Engineering: "I want my team to use data more, but I don't know where to begin." How can I use metrics without making it seem like "big brother"? It&