Adoption Curve Analysis

Every technology follows an adoption curve, but the shape of that curve varies dramatically. Consumer AI tools like ChatGPT can go from zero to 100 million users in two months. Enterprise AI deployments can take two years to move from pilot to production in a single organization. Understanding where a technology sits on its adoption curve — and how fast it is moving — determines whether a trend is early (opportunity to lead), maturing (time to adopt), or late (table stakes). The analysis is not about whether adoption will happen. It is about when, and whether you are positioned correctly for the timing.

1. Measure Adoption Velocity Track the rate of new deployments, new customers, or new use cases per quarter. Increasing velocity means the adoption curve is accelerating — the early majority is arriving. Stable velocity means linear growth — still early adopters. Decreasing velocity means saturation — the late majority is the remaining opportunity.
2. Identify the Chasm Geoffrey Moore's "Crossing the Chasm" model applies to AI ecosystem trends. The chasm is the gap between early adopters (who tolerate rough edges) and the early majority (who require polished, supported, integrated solutions). Many AI technologies stall in the chasm. Signals of chasm-crossing: vendor support organizations scale up, integration partners emerge, and industry-specific solutions replace horizontal offerings.
3. Time Your Response Before the chasm: evaluate and experiment, but do not commit strategically. During chasm-crossing: commit resources to adoption if the use case matches your needs. After the chasm: adopt quickly or risk competitive disadvantage. Too early is expensive. Too late is dangerous. The adoption curve tells you when the window opens.