Knowledge & perspectives
Expert perspectives on AI-amplified software delivery, enterprise technology, and the future of human-centric digital transformation.
An analysis of ThoughtWorks Technology Radar Vol. 34 and how it validates the need for a robust information architecture to combat AI-driven cognitive debt.
A critical look at the 'expertise' bubble in the AI landscape, arguing that traditional engineering judgment and information architecture are more vital for success than new AI labels.
An analysis of how AI acceleration is widening the gap between system complexity and human understanding, and why information architecture is the only structural cure.
A 'veteran' reflection on how software development methodologies became bloated with coordination ceremonies. Learn how AI-native delivery shifts the focus from managing overhead to solving genuine information architecture problems.
The biggest risk in AI adoption isn't the technology—it's our own legacy habits. We are facing an 'Unlearning Curve' that is often steeper than the learning one. As leaders, we must move past the 'wow' factor of LLMs and solve for the structural shifts: the Junior training gap, the obsolescence of current education, the re-education of our people and the creation of a workforce model that is both sustainable and measurable. I’ve shared some thoughts on how we can lead this change. How is your organization measuring the ROI of the 'human' side of AI?.
Why having AI tools isn't enough, and what it takes to deliver measurable enterprise value. Organizations face a systemic gap: AI tools designed for individuals deployed in contexts requiring coordination, governance, and measurement. This white paper explains why common approaches doesn't scale, what enterprise AI actually requires, and how it was proven through software development.