Cloud as a Story - Vunvulea Radu

  Whenever AI and IT teams come up in conversation, I notice the discussion quickly moves to jobs and headcount. How many people will be replaced? Which roles will disappear first? Personally, I think that discussion misses the bigger point. From what I’ve seen, the biggest impact of AI isn’t just task automation. What really changes things is the way it cuts down handovers, reduces repetitive coordination, and removes a lot of the delays that teams have learned to live with. If we look at a typical software delivery organization, work is often split across many specialized roles. Business analysts gather requirements, product owners manage backlogs, developers write code, testers validate functionality, DevOps engineers prepare releases, infrastructure teams manage environments, and support teams handle incidents. There’s nothing fundamentally wrong with that model. But in practice, every handoff creates overhead. Meetings need to happen, tickets get opened, documents get wr...

 When we discuss AI in IT delivery, the main point isn’t that AI will take over everyone’s jobs. The bigger shift is that AI will cut down on the number of handovers between people. Right now, a feature might go from a Business Analyst to a Product Owner, then to a developer, tester, DevOps, and finally support. Each step adds meetings, tickets, explanations, and sometimes delays. With AI, many of these tasks won’t go away completely, but they’ll be combined into broader roles. Teams might get smaller in some areas, but more importantly, the way teams are structured will change. Before going into more details, you can find below a possible mapping of new roles, covering the full SDLC. Before With AI BA + PO + Process Analyst + Data Analyst Product Discovery Lead Scrum Master + PMO + Project Coordinator Delivery Manager Developer + QA Automation + Basic Tester AI Product Engineer Ma...

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 ...