Organisational friction and AI
The previous articles in this series explored how AI deployment can fail through threats to identity, competence, and workload balance. This week: the O in my ADOPT framework: Organizational Friction. What happens when the design is sound, but the organisation still resists?
Two concepts from behavioral science explain this resistance: loss aversion and status quo bias. They're related but distinct, and addressing them requires different interventions.
Loss aversion, from Kahneman and Tversky's Prospect Theory, is the finding that losses loom larger than equivalent gains. Losing £100 feels twice as painful as gaining £100 feels good. This asymmetry shapes how people evaluate any change, including whether to adopt AI.
Status quo bias follows from this. Because change involves potential losses of routines, competencies, familiar workflows etc. people tend to stick with what they have. Loss aversion makes change feel risky. Status quo bias is the resulting inertia.
The UK Behavioural Insights Team's research illustrates both. In one experiment, participants preferred human help over AI, even when AI was more accurate. But when the task was reframed around avoiding losses rather than achieving gains, this preference vanished entirely. That's loss aversion: same choice, different frame, different behaviour.
Status quo bias operates more passively. In another BIT study, only 40% of participants messaged an available chatbot, even when it could help them. They didn't weigh the options and reject it. They simply kept doing what they were already doing. No decision required.
This is why my ADOPT framework treats Organizational Friction as a distinct diagnostic category. Loss aversion and status quo bias aren't design problems or awareness gaps, they're environmental forces. If management signals, incentives, and psychological safety work against change, even willing employees will struggle.
What might this mean for organisations?
For loss aversion: reframe AI as protection against errors or competitive disadvantage, not just a tool for gains. "Avoid falling behind" may motivate more than "get ahead."
For status quo bias: make AI the path of least resistance. Embed it in existing workflows rather than bolting it on as an extra step.
For both: create psychological safety. When managers protect employees who experiment and fail, the calculus shifts. Trying something new feels less risky. The status quo loses its gravitational pull.
Later in this series, I'll introduce the ADOPT diagnostic survey for measuring exactly where these forces are strongest.
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Kahneman and Tversky's Prospect Theory
