Data, Delivered

Fred Brooks told us in 1975 that adding people to a late software project makes it later. Fifty years on, we are relearning that lesson, this time with AI copilots and aggressive migration timelines. Executives see a Copilot demo and assume migration velocity is solved. It is not. AI accelerates the easy 60%. The hard 40% is tribal knowledge, undocumented rules, and stakeholder complexity. No copilot fixes that.

Data isn't optional, it underpins how modern organisations operate, compete, and innovate. Yet many still assess data investment through outdated governance models built for tangible, short-term returns. The result is a focus on cost savings over true value. Until governance evolves to recognise foundational, long-term impact, organisations risk underinvesting in the very capability that drives future advantage.

For decades, project success has been defined by the Iron Triangle: on time, on budget, to scope. But in modern software, especially in agile and AI-driven environments, scope evolves as learning unfolds. This article revisits why the traditional model struggles in discovery-led work, and explores a more honest definition of success based on value, adaptability, and sustained delivery rather than rigid constraint compliance.