A strategic framework developed during my MSc dissertation, the Cloud Value Proposition Model helps organisations compare cloud deployment options across eight critical dimensions—from cost and agility to flexibility and vendor lock-in.
AI tools promise to help tech teams do more with less—but often deliver mixed results. Many teams face rising tension, uneven output, and missed opportunities as leadership rushes to automate without fixing data, process, or culture. Efficiency isn’t fewer people — it’s fewer bottlenecks. Why leadership, architecture, and trust matter more than ever in the age of AI.
Can you really apply Agile to infrastructure when it involves physical hardware? Routers, switches, racks, cabling—the stuff you can actually trip over.
Short answer: Yes, you can. But you have to stop thinking like a landlord and start thinking like an architect.
The overused Einstein quote about insanity—doing the same thing and expecting different results—misses the mark in complex systems. True learning often involves iteration, not immediate change, and progress can look deceptively repetitive.
Slapping Agile labels on waterfall processes doesn’t make a team agile—it makes it confused, demoralised, and directionless. Fake Agile buries real progress under rituals that miss the point.
BYOD might sound progressive, but it’s a compliance and security disaster waiting to happen. Personal devices bypass corporate controls, open doors to unmanaged risks, and clash with any serious attempt at governance. Treating them like trusted endpoints is wishful thinking, not strategy.
Riding the big data wave? Beware—your tsunami of metrics might be polluted by garbage inputs. Even the slickest analytics tools can’t rescue insights tainted by bad, inconsistent or misunderstood data. Unchecked data quality issues bury value, skew decisions and leave you chasing phantom patterns.
