Cloud as a Story - Vunvulea Radu

 Giving people AI tools is the easy part. Getting them to use those tools every day is the hard part. Too many companies buy licences, announce the tool, then expect adoption to happen by magic. People are busy, they don’t have time to learn, and often they don’t know where to start or what “good” looks like for their role. If you want real adoption, learning must happen in the flow of work: short, practical, role-focused and most importantly, hands-on. The goal Make people confident in using AI tools for daily work—not just for demos or toy examples. The plan (6 weeks, minimal overhead) Week 0: Sponsor & plan (30–60 min). Get a leader to agree to protected learning time and pick a handful of real use cases. Leadership backing is small but crucial. Week 1: Role-based awareness (60–90 min). Run short sessions for each role: developers, QA, product, PM, support and show three concrete examples for the role. Keep it practical: real tasks, not abstract slides. Weeks 2–4 : Study g...

 Many companies are giving employees access to AI tools, but many are creating real adoption. I see this pattern more and more across organisations. Companies roll out AI tools like Copilot or ChatGPT, but commonly lack training, structure, and support to help teams use AI in daily work. The main obstacle is time. Teams are busy, so learning new tools is deprioritized. Many need examples and guidance on applying AI to their roles. Access alone is not sufficient. Instead, use simple mechanisms for learning-by-doing. Study groups and weekly sessions help share ideas. Having an AI-savvy person work with a team shows practical uses, like an engineer demonstrating Copilot in Visual Studio. For real AI adoption, companies need to invest in both tools and enablement. A simple prompt to for leaders: Don’t stop at buying licenses. Create role-based AI awareness sessions. Start small study groups inside teams. Give people protected time to learn. Use early adopters to coach teams hands-on. C...

 Many organisations begin cloud transformation by asking, “How do we move faster?” A better question is often, “What is slowing us down in the first place?” In my experience, Azure transformation programmes rarely slow down because teams are too cautious. More often, they slow down because the foundations are weak. Decisions are inconsistent. Too much is treated as bespoke work. What looks like speed at the beginning often becomes rework later. Teams push workloads forward but then need to come back and fix identity, networking, security, subscription design, resilience, or operational readiness. This is why I believe strongly that speed does not come from shortcuts. It comes from clarity, repeatability, and doing the important basics early. On Azure, this starts with a strong landing zone. When management groups, subscriptions, Azure Policy, RBAC, connectivity, monitoring, and security baselines are established early, delivery teams can move with much more confidence. They are not...

 I started using Windsurf as a practical assistant for my Home Assistant setup at home, and after a short time, it became clear to me that this is far more than a nice AI demo. It changed the way I do maintenance, debugging, and small improvements. The biggest value is not only the technical help, but the speed of interaction and the fact that I can keep full control while still moving much faster. Before going into details, here is the short version of the impact: Reduced my Home Assistant maintenance effort by around 80% Helped me clean up around 90 accumulated errors in about one hour Made debugging much faster because it can inspect logs over SSH Helped me add new automations and features with much less friction Kept the process safe, because it has read-only access only At home, I use Windsurf as a Home Assistant assistant—not to replace me, but to speed me up. What I like most is the way we interact. I do not write long prompts. Usually, I just say things like “check the logs...

 The primary goal of this work was to assess whether an AI system could define and support the entire migration process, from design through script creation to execution. This effort was not limited to faster code generation. The experiment aimed to determine whether AI could practically support all migration stages, including analysis, process structuring, task automation, documentation, and execution support. For this experiment, I migrated from Azure DevOps Server to Azure DevOps Services using Microsoft’s official Data Migration Tool. This scenario was selected for its complexity, which includes technical dependencies, validation points, identity management, infrastructure setup, and post-migration verification. Such migrations are prone to errors if the process is unclear or not repeatable. The objective was to automate as much of the end-to-end migration flow as possible. Typically, such migrations require several days for planning, scripting, testing, documentation, troubles...

 Infrastructure as Code should make cloud delivery faster, but compliance checks usually appear too late — after deployment or at the end of the pipeline. Then teams discover the same issues again: naming is not following conventions, mandatory tags are missing, TLS is not enforced, public access is enabled, diagnostics are not configured. None of this is new. Microsoft already provides strong guidance in the Well-Architected Framework (WAF) and Cloud Adoption Framework (CAF). The hard part is applying these rules consistently across many repos and many teams. This is why I built Azure IaC Compliance Checker : a small open-source CLI that checks Azure IaC before deployment , directly from code. Where the tool is available The tool is available on GitHub: vunvulear/azure-iac-checker . You can find it here: https://github.com/vunvulear/azure-iac-checker . Right now it’s early stage: there are no releases and no published packages , so the normal way to use it is to clone the reposit...