Tacit-Knowledge on AI For Human Expertise

The LLM Wiki: A Pattern for Smarter Organisational Knowledge Bases

Sun, 05 Apr 2026 00:00:00 +0000

A Better Pattern for Organisational Knowledge

Andrej Karpathy - the AI researcher known for his work at OpenAI and Tesla - recently shared a pattern he calls the “LLM Wiki.” It addresses a fundamental limitation in how most organisations use large language models for knowledge work today.

The standard approach is retrieval-augmented generation (RAG): upload documents, the AI retrieves relevant chunks at query time, and generates an answer. It powers tools like ChatGPT file uploads and most enterprise search products. It works - but nothing accumulates. Every question forces the AI to rediscover knowledge from scratch, piecing together fragments across documents each time.

AI for Knowledge Transfer: The Opportunity Most Organisations Miss

Sun, 08 Mar 2026 00:00:00 +0000

AI for Knowledge Transfer: The Opportunity Most Organisations Are Missing

Across industries, the dominant conversation about AI is about augmentation: helping people work faster, automating repetitive tasks, and processing existing information more efficiently. That is valuable, and organisations are beginning to see the returns across areas like reporting, scheduling, and operational monitoring.

But there is an equally important application that is largely overlooked, and in our experience building AI systems inside large organisations, one of the highest-value problems AI is actually well-suited to address.

AI Doesn't Just Miss Tacit Knowledge. It Can Destroy It.

Mon, 09 Feb 2026 00:00:00 +0000

The Standard Framing Gets Something Important Wrong

The dominant logic behind most AI adoption is straightforward: find the task, automate the task, reduce the cost or time. It is a reasonable starting point. The problem is what it misses.

Tasks do not exist in isolation. Behind most of the tasks worth automating is a body of expertise: judgement built over years, pattern recognition that operates faster than it can be explained, contextual awareness that no process document has ever fully captured. This is tacit knowledge, and it is almost never accounted for in the standard AI adoption model.

Why Most AI Projects Fail and How to Beat the Odds

Sat, 07 Feb 2026 00:00:00 +0000

Why AI Projects Fail: Three Root Causes

The numbers are stark: research consistently shows that around 80% of AI projects fail to deliver meaningful business value. Not because the technology doesn’t work, but because organisations approach AI the wrong way.

80%

of AI projects fail to deliver meaningful business value.

Research consensus across recent industry studies.

After working across dozens of AI initiatives, we’ve identified three root causes that explain the vast majority of failures.

Five Questions to Ask Before Starting Any AI Project

Tue, 03 Feb 2026 00:00:00 +0000

Why These Questions Matter Before Any AI Project

Before starting any AI project, the questions that matter most are not technical; they are foundational. We’ve seen millions spent on AI initiatives that were doomed from the start, not because the technology was wrong, but because nobody paused to think critically about the fundamentals.

Here are five questions we ask at the start of every engagement. They’re simple, but the answers are revealing.