Max Kurton

We’re looking for engineering leaders, DevOps leaders, platform engineering leaders, and software delivery experts to contribute insights for an upcoming article exploring what DevOps maturity actually looks like in 2026. The piece will focus on how engineering organisations are moving beyond static maturity checklists toward continuous measurement using delivery telemetry, operational visibility, reliability metrics, developer experience signals, and AI-assisted engineering workflows. We’re particularly interested in practical, experience-led insights rather than theoretical DevOps advice. Please answer one or more of the following questions: 1. What is the biggest flaw in traditional DevOps maturity models or maturity assessments today? 2. What operational signal or metric most reliably tells you whether an engineering organisation is truly maturing? 3. How has AI-assisted software development changed the way you think about engineering maturity or delivery performance? 4. What’s one sign an organisation has “maturity theatre” rather than genuine operational maturity? 5. Which DevOps metric do you think leaders overvalue, and which one do they undervalue? Provide: Name, Role, Company, link Optional: examples, metrics, or operational lessons from real engineering teams What you get: Quote attribution, name placement, link placement Potential inclusion in a long-term engineering leadership contributor network

We’re looking for engineering leaders, CTOs, engineering managers, DevOps leaders, and platform engineering experts to contribute insights for an upcoming article on improving developer productivity in modern engineering organisations. The article explores how engineering leaders improve productivity operationally through better visibility, workflow optimisation, delivery metrics, and bottleneck reduction, rather than relying on simplistic activity tracking or developer output metrics. We’re particularly interested in practical, experience-led insights from real engineering environments. Answer one or more of the following questions: 1. What is the biggest factor that slows developer productivity in most engineering organisations today? 2. Which developer productivity metric do you find most useful — and which do you think is overrated? 3. What operational bottleneck has had the biggest impact on productivity in your engineering teams? 4. How do you improve developer productivity without increasing burnout or pressure? 5. How has AI-assisted development changed the way you think about engineering productivity? 6. What’s one practical change that significantly improved engineering flow or delivery efficiency in your organisation? Requirements Name, role, company, link Optional: operational examples, metrics, or delivery lessons from real engineering teams What you get: Quote attribution, name and link attribution, potential inclusion in a long-term engineering leadership contributor network

We’re looking for engineering leaders, DevOps managers, platform engineering leaders, and software delivery experts to contribute insights for an upcoming article exploring how modern engineering organisations build useful DevOps dashboards. The article focuses on which DevOps metrics actually matter operationally, how leaders avoid vanity dashboards, and how dashboards support delivery visibility, bottleneck detection, predictability, and engineering decision-making across the SDLC. We’re particularly interested in practical, experience-led insights from real engineering environments. Answer one or more of the following questions: 1. What’s the biggest mistake organisations make when building DevOps dashboards? 2. What’s one metric or dashboard view that consistently helps you identify delivery bottlenecks early? 3. How do different engineering roles in your organisation use dashboards differently? 4. What separates a genuinely useful DevOps dashboard from a dashboard that just creates noise? 5. How has AI or predictive analytics changed the way your teams use DevOps dashboards? Include: Name, Role, Company, Link Optional: examples, metrics, or operational lessons from real engineering teams What you get Quote, name, and link attribution Potential inclusion in a long-term engineering leadership contributor network