
AI and the Collapse of Organisational Memory
AI knowledge retrieval can weaken organisational memory when summaries hide context, dissent, incident history, product reasoning, and uncertainty.
Articles
Engineering practices are the repeatable habits that help teams protect quality under real delivery pressure. Code reviews, standards, documentation, onboarding, technical spikes, testing, and sensible metrics all belong here when they make the work clearer, more maintainable, and easier for others to inherit.
Below you will find a subset of articles from my blog specifically about Engineering Practices. This is an area I have worked with for many years, and it has been a regular subject in my writing. There are sixteen articles collected together for you below.

AI knowledge retrieval can weaken organisational memory when summaries hide context, dissent, incident history, product reasoning, and uncertainty.


A practical explanation of AI, AGI and ASI for engineering and product teams, covering capability, autonomy, risk, governance and real‑world impact.

The AI content collapse makes cheap publishing less valuable, shifting durable content strategy towards proof, authorship, structure, trust, and expertise.

AI can automate management reporting, but this article separates status theatre from judgement, coaching, accountability, and real prioritisation.

AI coding tools make code faster to produce, but technical debt still needs review, ownership, tests, documentation, and senior engineering judgement.

AI can inflate output without improving outcomes. This article explains why weak metrics, faster generation, and shallow review create a productivity mirage.

AI will be OK if teams treat it as real technology, not magic, with adoption shaped by judgement, skills, governance, shared access, and careful autonomy.

How to design multi‑tenant Next.js architecture across routing, domains, configuration, content, caching, previews, analytics, and team ownership.

AI automation improves productivity, but unmanaged labour displacement risks weaker demand, brittle organisations, concentrated gains, and a race to the bottom.

Why production data breaks Next.js sites, including CMS fields, slugs, images, relations, dates, rich text, generated routes, and validation gaps.

Responsible AI becomes real only when decision ownership, data handling, audit trails, exceptions, procurement, and support are assigned to people.

A Next.js production triage checklist for broken deploys, covering rollback decisions, logs, environment drift, routes, auth, CMS data, and cache.

Enterprise AI delivery usually fails after the demo, when ownership, governance, support, procurement, data access, and measurement have to become real.

Agentic systems do not replace service design. They expose weak contracts, permissions, observability, retries, state ownership, and workflow boundaries.

Greener software starts with engineering discipline: lighter pages, fewer wasted requests, better caching, leaner CI, and deliberate use of AI compute.