Human-Expertise on AI For Human Expertise

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.

Deskilling Is Already Happening: Evidence Across Domains

Mon, 11 May 2026 00:00:00 +0000

The Pattern Beneath the Hype

It is tempting to treat the deskilling AI can cause as a future risk, something to worry about once the technology is more capable. The evidence says otherwise. Across very different fields, driven by the same human factors, the erosion is already measurable. The lesson from each domain is the same one, dressed in different clothes.

What Deskilling Actually Looks Like

Deskilling is rarely dramatic. It shows up in four recognisable ways:

The Cognitive Cost of Convenience

Thu, 23 Apr 2026 00:00:00 +0000

The AI Expertise Paradox

AI offers tremendous convenience and real productivity gains. The question that rarely gets asked is: at what cost to our skills?

Here is a quick thought experiment. What happens tomorrow if your favourite AI system disappears? Who on your team still knows how to do the task without it? For a growing number of tasks, the honest answer is becoming uncomfortable.

This is the paradox at the heart of how most organisations are adopting AI: the more we rely on it for convenience, the more our underlying skills may decline. The gain is immediate and visible. The cost is gradual and easy to miss, right up until the moment you need the skill and find it has quietly gone.

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.