The gap isn't primarily organisational readiness—the technology itself has to be substantially reworked to fit the specific productive context. Rosenberg's Inside the Black Box makes this the central point: the black box isn't transferable as-is, and the learning, adaptation, and complementary innovations required are so extensive that 'adoption' fundamentally mischaracterises what's happening.
Your electrification parallel actually illustrates this. The 1920s productivity gains weren't delayed by slow organisational learning—they waited on the development of safety practices, residual value tables, and the other infrastructure required for electric power transmission to work inside a factory rather than just delivering power to its perimeter. (I explored this in https://thepuzzleanditspieces.substack.com/p/the-failure-data-economy)
The same logic applies to GenAI. The current tooling is built around a generic conception of knowledge work. Getting real productive value means redeveloping them around specific work structures—which presupposes you understand those work structures first. The more fundamental question isn't whether people are ready for the tools, but whether the tools are being aimed at the right work in the first place.
Thanks. Yes, I think that might be true. But if we think of agentic AI (not just Gen AI) as a layered tech, the general purpose synthetic intelligence is very much a black box, but the layers above the GPT (context, data, application) all need to be adapted to the organisation and the work it does. In the linked article about software brain, the author talks about the futility of adapting people to the tool rather than tools to the people and their work.
But I do think there is a huge challenge with org readiness, and this is not an argument for changing the org to fit the tech, but changing the org to fix many extant weaknesses and problems in a way that makes it amenable to smarter control and guidance. For example, the apparent inability of many management teams to write things down and create clarity and coherence to improve the context in which people work has been experienced as a problem for the workforce - but it just so happens that it is also crucial for applying enterprise AI as well.
I think (hope?) agentic AI will become more tailored to specific work and processes, perhaps using smaller specialised models rather than just frontier models, without requiring people to adapt their thinking to the models.
You might find Peter Damerow useful here, specifically his work on the co-evolution of representational systems and cognition. It cuts at the ‘general purpose intelligence as substrate’ assumption fairly directly.
The gap isn't primarily organisational readiness—the technology itself has to be substantially reworked to fit the specific productive context. Rosenberg's Inside the Black Box makes this the central point: the black box isn't transferable as-is, and the learning, adaptation, and complementary innovations required are so extensive that 'adoption' fundamentally mischaracterises what's happening.
Your electrification parallel actually illustrates this. The 1920s productivity gains weren't delayed by slow organisational learning—they waited on the development of safety practices, residual value tables, and the other infrastructure required for electric power transmission to work inside a factory rather than just delivering power to its perimeter. (I explored this in https://thepuzzleanditspieces.substack.com/p/the-failure-data-economy)
The same logic applies to GenAI. The current tooling is built around a generic conception of knowledge work. Getting real productive value means redeveloping them around specific work structures—which presupposes you understand those work structures first. The more fundamental question isn't whether people are ready for the tools, but whether the tools are being aimed at the right work in the first place.
Thanks. Yes, I think that might be true. But if we think of agentic AI (not just Gen AI) as a layered tech, the general purpose synthetic intelligence is very much a black box, but the layers above the GPT (context, data, application) all need to be adapted to the organisation and the work it does. In the linked article about software brain, the author talks about the futility of adapting people to the tool rather than tools to the people and their work.
But I do think there is a huge challenge with org readiness, and this is not an argument for changing the org to fit the tech, but changing the org to fix many extant weaknesses and problems in a way that makes it amenable to smarter control and guidance. For example, the apparent inability of many management teams to write things down and create clarity and coherence to improve the context in which people work has been experienced as a problem for the workforce - but it just so happens that it is also crucial for applying enterprise AI as well.
I think (hope?) agentic AI will become more tailored to specific work and processes, perhaps using smaller specialised models rather than just frontier models, without requiring people to adapt their thinking to the models.
You might find Peter Damerow useful here, specifically his work on the co-evolution of representational systems and cognition. It cuts at the ‘general purpose intelligence as substrate’ assumption fairly directly.