The first article in this series highlighted a shift in AI Build teams: creating code, documentation, and tests is no longer the hard part. AI has significantly reduced the effort needed for these tasks, allowing small teams to deliver at speeds that were unimaginable just a few years ago.
Software development is changing. AI engineering tools, agentic environments, automated testing, AI-generated documentation and spec-driven development are reshaping how teams are structured, how decisions are made and what leadership looks like in a delivery team.
In a recent DVT Insights Webinar, Dael Williamson outlined why the next phase of enterprise AI will depend less on chatbots and productivity gains, and more on data, governance, organisational design and new ways of working.
Back in 1979, an IBM training manual included a warning that feels strangely fitting today: "A computer can never be held accountable, therefore a computer must never make a management decision." In software engineering, every line of code is a decision. The architecture you choose, the edge cases you handle and the way you structure your data are all choices that decide whether a system succeeds or fails. With generative AI now part of our toolkit, there's one question worth asking: who is making those decisions in your codebase?
There is a conversation happening in the boardrooms of South Africa's mining houses, chemical companies, and manufacturers right now. It usually starts with a frustrated CIO or COO pointing at a list: the mobile application for field maintenance crews that has been in development for eighteen months. The plant operations dashboard that went live missing half its integrations. The AI-powered scheduling tool that is still a pilot, twelve months after the pilot was supposed to end. The digital platform that works in the demo environment and nowhere else.
As organisations deploy AI at scale, leaders need to confront a critical question: who owns the decisions of the machines?
Artificial Intelligence is shifting the software development landscape faster than any wave we have seen in years. Developers are no longer merely writing code manually; they are prompting, reviewing, refining, and orchestrating AI-assisted outputs. Teams are rapidly adopting AI coding assistants, automated testing, AI-generated documentation, and spec-driven development models where detailed specifications guide AI agents and development workflows.
Discover the four foundational pillars to creating mobile apps that stand out and deliver impact.
With ransomware-as-a-service kits going for less than the price of a team lunch, it’s no longer surprising that even non-technical bad actors are launching sophisticated cyber-attacks. Security has become less about guarding the perimeter and more about managing risk from the inside out, and across every layer of an organisation.