.png)
WIP Limits for Engineering Teams: A Practical Guide
WIP limits are easily set... and easily broken. Learn how to make them stick and what to do if you hit them.
.png)
.png)
What Makes Engineering Transformation Stick
Most engineering transformations stall because they skip stages. Here's the five-stage framework for building AI capability that compounds.
AI Capabilities Maturity: What to Measure Beyond Adoption
Most AI maturity metrics track usage, not impact. Here's the framework engineering leaders need to assess AI maturity across the full organizational system.
Enterprise AI Capability Building: How to Do It Right
Enterprise AI capability building requires six organizational surfaces. Most strategies address one or two. Here's how to assess where yours stands.
.png)
What Microservices Should Have Taught Us About AI
How to avoid the key pitfalls from microservices in your AI adoption — and the lessons we learn on our way to maximizing gains from emerging tech.
.png)
The AI Transformation Stack: 10+ Tools Beyond Code Generation
Most AI coding initiatives fail to deliver ROI. Here are 12 tools that help engineering leaders transform throughout the SDLC.
The CI/CD Gap Holding Back Your AI Investment
AI CI/CD bottlenecks prevent most orgs from capturing efficiency gains. Learn why deployment stays flat even as velocity doubles — and what to fix first.
What Makes Engineering Transformation Stick
Most engineering transformations stall because they skip stages. Here's the five-stage framework for building AI capability that compounds.
AI Capabilities Maturity: What to Measure Beyond Adoption
Most AI maturity metrics track usage, not impact. Here's the framework engineering leaders need to assess AI maturity across the full organizational system.
Enterprise AI Capability Building: How to Do It Right
Enterprise AI capability building requires six organizational surfaces. Most strategies address one or two. Here's how to assess where yours stands.
What Microservices Should Have Taught Us About AI
How to avoid the key pitfalls from microservices in your AI adoption — and the lessons we learn on our way to maximizing gains from emerging tech.
Your First 90 Days as a New Engineering Leader
Learn the five priorities and strategic decisions that turn your first 90 days into long-term success.
How to Find Engineering Bottlenecks
Learn how to diagnose engineering bottlenecks. This guide for leaders covers actionable steps, real-world examples, and a framework for driving change.
The WAVE Framework for Engineering Effectiveness
Uplevel's WAVE Framework guides our approach to organizational change in engineering. Learn about our key indicators for engineering capability.
The AI Measurement Crisis
Here's what the research from a study of 100+ engineering leaders at large orgs reveals about AI measurement.
AI Won’t Solve Your Developer Productivity Problems for You
AI tools aren't the magic fix for developer productivity. Learn how to maximize impact with fundamentals.
.png)
Beyond Flow Metrics: A Holistic Approach to Software Value Delivery
Flow metrics and DORA look at software delivery through two very different lenses. Use both to optimize processes, identify bottlenecks, and drive value.
DORA Metrics Are a Start. Here's What Comes Next.
DORA metrics are lagging indicators of delivery performance because they don't explain context. Here's why engineering leaders need a better view.
Preserving Developer Flow in the Era of Always-On AI
Preserving developer flow and focus as an engineering leader isn't just good for team performance, but for productivity in a tough economic climate.
Lean Software Engineering Strategies in a Down Market
Discover how successful leaders navigate economic downturns with lean sofware engineering strategies.