The Loyalty Loop: How Systems Use Belonging to Blind Us
Loyalty feels like virtue — but in complex systems, it can become a tool of control. This piece explores how belonging is engineered to silence feedback and why awareness, not rebellion, is the real counter-tactic.
Progress as a Product: Rethinking Digital Transformation
Digital transformation succeeds or fails on trust. When progress is designed for optics instead of empathy, internal users build workarounds faster than systems can catch them. Progress as a Product explores how UX Strategy restores belief by designing the experience of work itself.
Diffusion of Innovations
Explains how new ideas or technologies move from invention to adoption through social systems, showing why innovators lead, laggards follow, and networks determine momentum.
Incentive Distortion: Reward vs Value
Misaligned incentives cause systems to optimise for metrics instead of meaning. When rewards outpace real value, performance looks strong but purpose erodes.
When Fear of AI Becomes the Real Risk
Fear of generative AI is often framed as caution. In reality, hesitation entrenches dysfunction — leaving teams buried in duplication and burnout.
The True Cost of Compliance: A Hidden Productivity Tax
Rules that once protected progress now drain it. This essay exposes the hidden productivity tax of outdated compliance — and why rewriting the rulebook is the only way to restore clarity.
Probabilistic Models — Mapping Uncertainty into Insight
Probabilistic Models apply probability theory to quantify uncertainty, turning incomplete data into forecasts that guide risk management, decision-making, and future planning.
Proxy Metrics: Stand-ins for Success vs Substitutes for Reality
Proxy metrics are substitute measures adopted when true outcomes are hard to track. Once they become targets, they often distort behaviour and drift into dysfunction.
Continuous Improvement: The Discipline of Incremental Change
Continuous Improvement, using Kaizen and the PDCA cycle, builds progress through small, disciplined steps — embedding change as a habit rather than a disruption.
Single-Loop Learning: Error Correction Within Stable Assumptions
Detects and corrects errors without questioning underlying rules — effective when assumptions are valid and context stable.