Cloud as a Story - Vunvulea Radu

Many executive teams ask: How quickly should we expand our AI capabilities? The typical response is to hire more AI/ML engineers, data scientists, or establish a dedicated AI department. While important, this addresses only part of the challenge. AI does not generate business value simply by increasing headcount. Value arises when models, data, applications, platforms, security, testing, and business processes are integrated. The key question is not just, “ How many AI engineers do we need? ” but rather, “ What organisational structure will turn AI ideas into production business outcomes? ” In many AI initiatives, AI/ML work accounts for only 20–40% of the total effort. The rest of the effort is in data engineering, cloud and platform engineering, automation, software development, QA, security, compliance, monitoring, cost management and business adoption. If an organisation grows AI talent without growing the surrounding capabilities, the bottleneck does not disappear. It only moves ...

 Today, most AI-generated platform engineering work still relies on classical prompt engineering: describe the outcome, add context and constraints, then iterate until the pipelines, infrastructure, and operational assets are “good enough.” More structured methods and tools (such as BMAD or SpecKit) are still relatively uncommon for tasks like DevOps pipelines, infrastructure-as-code, operational documentation, and production-readiness controls. That’s why I ran this comparison: I wanted to see whether structured approaches can materially outperform normal prompting on a realistic platform engineering task. I compared four approaches to generate pipelines, cloud infrastructure, and an operational layer for an existing application running on Azure App Service: SpecKit used with normal prompting, classical prompt engineering, SpecKit (method-driven), and BMAD. The goal wasn’t just “does it compile?”—it was whether the output looked like something a real platform team could run ...