AdoptAI Scrollytelling

When Does GenAI Adoption Become a Collective Workplace Norm?

A workplace story of GenAI adoption, simulated through Segregation and Threshold Theory.

Project Description

This project keeps the academic theory at the center. AdoptAI is used as a working concept: a workplace context that makes Segregation and Threshold Theory visible, concrete, and relatable.

The scrollytelling page explains how individual decisions can create collective workplace patterns. Using a simple agent-based simulation, it shows how GenAI adoption may remain isolated, break before spreading, reach a tipping point, cascade across a workplace, or form clusters of safe users, cautious users, non-users, and shadow users.

The goal is not to judge employees or promote uncontrolled AI use. The goal is to understand how adoption spreads socially, why it sometimes stalls, and how governance can help make AI adoption more visible, responsible, and inclusive.

Academic Foundation

This project is inspired by two important social science models:

• Thomas C. Schelling — Dynamic Models of Segregation (1971)
  Used to explain how small local preferences can produce larger patterns of separation or clustering.

• Mark Granovetter — Threshold Models of Collective Behavior (1978)
  Used to explain how people may join a behavior only after enough others have already joined, creating possible cascades.

Working Concept: AdoptAI

In this project, AdoptAI represents a workplace environment where employees respond differently to GenAI:

• Some adopt early.
• Some wait for social proof.
• Some remain cautious.
• Some avoid adoption.
• Some may use AI quietly when policy or guidance is unclear.

These differences are modeled through individual thresholds, peer influence, workplace clustering, and governance conditions.

What the Simulation Shows

The scrollytelling experience demonstrates several possible adoption patterns:

1. Isolated adoption — a few employees use GenAI, but adoption does not spread.
2. Broken cascade — adoption begins but stops because thresholds are not met.
3. Critical point — small interventions can decide whether adoption grows or collapses.
4. Full cascade — GenAI use becomes a collective workplace norm.
5. Segregated adoption — employees cluster into safe users, cautious users, non-users, and shadow users.
6. Governance intervention — training, policy clarity, approved tools, and human review can shift adoption toward safer and more visible use.

Why This Matters

GenAI adoption is not only a technology rollout. It is also a social threshold process. The same tool can spread differently depending on trust, visibility, peer behavior, manager support, and governance.

By combining Segregation Theory and Threshold Theory, this project shows that the important question is not simply:

Will employees adopt AI?

The deeper question is:

Will adoption become visible, governed, inclusive, and responsible — or will it become uneven, hidden, and clustered?

Project Type

• Scrollytelling website
• Conceptual agent-based simulation
• Academic visualization
• Workplace GenAI adoption model
• AI governance learning artifact

Suggested GitHub About Description

A scrollytelling simulation of workplace GenAI adoption using Schelling’s Segregation Theory and Granovetter’s Threshold Model to explain cascades, clustering, and governance risk.

Suggested Topics

genai ai-adoption threshold-models segregation-theory agent-based-modeling scrollytelling ai-governance collective-behavior schelling granovetter