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In many fields, there is some critical knowledge that is very difficult to codify. “It’s the kind of knowledge that is never written down and yet can be crucial, even in the highest of hi-tech enterprises. And you won’t find it in ChatGPT, either”, says John Naughton in The Guardian.

KM expert, Nick Milton discussed the codification of knowledge and created this breakdown.

1. impossible to express,
2. can be expressed but has not been yet,
3. expressed in speech but not documented,
4. recorded knowledge, or
5. information.

The first two levels of knowledge are important to understand because we cannot easily categorize or retrieve them. They are messy. And as John Naughton states, you won’t find them with them with generative language models. In this article, he gives an example of semi-conductor manufacturing and how just having the specifications is not enough to create a functioning industry.

What’s fascinating about all this is how much of it comes down, not to finance or technology, but to people and what they know. In that sense the Financial Times’s deep dive into TSMC’s [Taiwan Semiconductor Manufacturing Company] travails reminded me of a striking piece of research conducted decades ago by the philosopher of science Harry Collins when he was a PhD student. Collins was interested in how knowledge gets transferred and intrigued by a particular piece of technology, the TEA laser. This was a device that was comprehensively documented in the physics literature but which research laboratories were unable to replicate. What Collins discovered was that “nobody could make the laser work if they hadn’t spent time in a laboratory that already had a working laser. There was very good information in the journals about how to build such a laser. But anybody who tried to put one together using written articles failed. They had something that looked like a laser on their bench, but it wouldn’t lase.” —The Guardian 2023-10-21

Practices such as supporting self-directed learning, narrating our work, and curating organizational knowledge are essential in ensuring that difficult-to-codify knowledge is able to flow between trusted nodes in a human network. This was one of the objectives of our working smarter initiative at Citibank. It’s one more reason that managers have to get out the office and do some network walking.