Strategy on AI For Human Expertise

Which Doors Should Machines Walk Through?

Sat, 11 Jul 2026 00:00:00 +0000

Jeff Bezos gave managers a simple test for how much care a decision deserves. Some decisions are two-way doors: walk through, dislike what you find, and walk back out at little cost. These should be made quickly, by individuals or small teams, because caution buys nothing. Others are one-way doors: irreversible, or nearly so, and worth slow deliberation and senior sign-off. His warning was that growing organisations tend to apply the heavyweight, one-way process to everything, and grind to a halt as a result.

Constrained Generation Is Symbolic AI Smuggled Into LLMs

Mon, 06 Jul 2026 00:00:00 +0000

The Toolkit Most Practitioners Already Reach For

Anyone who has shipped a language-model application beyond the prototype stage has, at some point, used constrained generation. The shape of the encounter is consistent. The model produces text that is almost right and occasionally wrong in ways the consumer cannot tolerate. Someone on the team reaches for one of a small set of tools: Outlines, lm-format-enforcer, the JSON-schema mode in their model API, a regex post-filter, a structured-output decoder. The wrong answers stop. The system ships.

From RAG To KAG: The Structured-Knowledge Upgrade

Mon, 29 Jun 2026 00:00:00 +0000

RAG Solved One Problem And Created Another

Retrieval augmented generation became the default architecture for grounding language model outputs in 2023 and 2024, and for good reason. It addressed the most obvious failure mode of out-of-the-box large language models, which was producing fluent text on topics the model did not actually have current or correct information about. RAG pulls relevant documents into the model’s context at inference time. The model can cite them. Hallucinations drop. Useful systems get built.

A Five-Role Diagnostic For Any AI Agent

Mon, 22 Jun 2026 00:00:00 +0000

Most Agent Problems Have A Diagnostic

When an AI agent fails in production, the conversation almost always turns to the model. The team waits for a bigger model, a better-tuned variant, a different vendor, a new context window. Sometimes the team turns to prompts instead: more careful instructions, more examples, a sharper system message. Most failures are neither. They are failures of structure, and the structure question is a thirty-year-old framework most engineers have never read.

The Harness Is Where Symbolic AI Returned

Mon, 15 Jun 2026 00:00:00 +0000

The Standard Story About AI Agents Misses Half The System

The dominant story about building AI agents is straightforward: take a language model, prompt it well, give it tools, and let it loop. The model is doing the work. Everything else is plumbing.

This story is incomplete in a way that has practical consequences. The plumbing is not plumbing. It is a body of design decisions that determines whether the agent works in production, fails silently, or quietly degrades over time. And some of it, the parts that earn the name, is doing what symbolic AI has always tried to do: making reasoning explicit, checkable, and tied to a model of the world.

Designing AI That Protects Expertise

Sat, 30 May 2026 00:00:00 +0000

Erosion Is a Design Choice, Not a Destiny

The first two posts in this series made an uncomfortable case: AI can quietly erode the very expertise it depends on, and across domains that erosion is already happening. It would be easy to read that as an argument against AI. It is not.

AI systems do not have to deskill the people who use them. That outcome is the result of design decisions, usually unexamined ones. Make different decisions and the same technology can preserve expertise, and in some cases actively strengthen it. This post is about those decisions.

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.