Durable AI Adoption

Context

< " The object being reshaped now is the individual work environment of every person contributing to an organization. " >

Protocolized:

"What electric power did to the vertical, steam-powered factory, artificial intelligence is doing to our digital work environments. AI provides a new power source for knowledge work – a unit drive of surplus tokenized intelligence available to every person in an organization. In the canonical 20th-century factory, the object being rearchitected was a building: a large, shared work environment. The object being reshaped now is the individual work environment of every person contributing to an organization. Each knowledge worker is, in effect, a small factory whose machine tools must be repurposed around the new power source.

This cannot happen by decree alone. A knowledge worker’s tooling is the residue of years of tacit decisions, each indebted to technical dependencies and arcane software, like the position of a lathe relative to a line shaft. The only way the repurposing actually happens is through the same process that relaid the factory floor: people must combine their domain expertise with the new power source – try things, build small fixtures for themselves, share the ones that work, and discard the ones that don’t.

AI adoption is the combination of deep domain expertise with the new power source, and this new arrangement has to be allowed a period of productive experiment before anyone tries to draw the new floor plan. Play has to precede planning. Desire lines must form before they can be paved.

This is what makes durable AI adoption a two-track initiative, without exception. The experimental phase cannot be skipped, because the patterns worth standardizing have not emerged yet. But chaos alone is lossy – patterns form and dissolve without ever consolidating into infrastructure the organization can rely on. The work is to read the chaos, reinforcing the traces that prove themselves and releasing the ones that don’t.'

(https://protocolized.summerofprotocols.com/p/durable-ai-adoption)

Typology

The Cultivated vs Governed Track of AI Adoption

Protocolized:

"For companies to be successful in adopting AI, they need to consider both the rearchitecting of the work environment and the change in subjective reality of members of the organization. To achieve this, any AI adoption initiative needs two tracks: governed and cultivated. The governed track is focused on architecting new environments, while the cultivated track is focused on the psychological and ergonomic effects of the new environment.

The cultivated track is bottom-up: individual play, experimentation, the personal workflows people build and share. This track is comparable to the amateur kit stage in the deployment of a technology. Technologies such as computers and cars went through a stage where amateurs tinkered and experimented with them on their own terms. In the case of radio, amateurs were the ones keeping the entire industry alive before standardization was even considered. Kit stages are characterized by rapid experimentation and formation of a field around a particular technology. The focus is on gaining tacit knowledge and being subjectively accustomed to the effects of the technology, and less on archiving knowledge for the longer term. A cultivated stage of adoption is important for AI because it is a technology that has significant effects both at the organizational and at the individual level. In many of the previous eras of technology, such as desktop computers and the early internet, cultivation of a field was left to artists and hackers who were early adopters of the technology. But today, the effects of AI are at once wide and solipsistic, which means that the cultivation stage is something that should be diffused through the entire organization, not just the early adopters and the 10× engineers.

The role of the governed track is to create new protocols from the outcomes of the experiments in the cultivated track. The governed track should set protocols at points where AI’s outputs become consequential: how an output gets verified, escalated, and owned as it crosses from machine to human or from one team to another. A traffic light is a protocol; a blockade is not. Both are rules, but one keeps the system moving while managing the underlying tension; the other just stops it.

The cultivated track is trace-making – how an organization produces the patterns of coordination that a new medium makes possible, the equivalent of the desire lines worn across a park, the kits and practices that no central planner could have specified in advance. The governed track is trace-selection – how an organization reads those emerging patterns and decides which to reinforce into shared infrastructure and which to let fade.

A governed track with nothing underneath it standardizes too early. It freezes a layout before new traces have formed, the way laggard manufacturers kept the line shaft. A cultivated track with no governance generates patterns endlessly but never consolidates them, accumulating risk it cannot see. It is frontier territory with no ecosystem to sustain and learn from it. The maturity ladder is really a measure of how well an organization coordinates these two tracks as the ground keeps shifting."

(https://protocolized.summerofprotocols.com/p/durable-ai-adoption)