Concern 915 · Working draft · editorial review in progress
Cultural resistance and adoption plateaus
An adoption plateau occurs when initial interest in AI does not turn into sustained useful practice, often because the tool, workflow or support does not fit people's needs.
Understanding the concern
Low use may reflect poor training or unclear expectations, but it can also be a sensible response to a tool that adds checking work or makes mistakes. Calling all hesitation resistance can hide those problems. Teams need to understand what people experience during actual tasks.
A multi-firm experiment found individual time savings without clear broad changes in task composition over its observation period. That helps distinguish using a tool from transforming an organization. Think of a new kitchen appliance that saves one step but does not change the whole meal. Listening to users and measuring real outcomes can reveal whether to improve, narrow or stop an adoption effort.
An example
Staff try an AI tool enthusiastically, then stop because correcting its output takes longer than their existing method.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Training
- The process of using examples to shape how an AI model behaves. Learning patterns from material does not establish that the material is true or fair.
Evidence and its limits
The field experiment concerns a particular workplace intervention and time period. It does not prove that all stalled adoption is caused by culture or that long-term changes cannot occur.
Dillon, Jaffe, Immorlica and Stanton · NBER · Field experiment
Shifting Work Patterns with Generative AI ↗ (opens in a new tab)66 firms and 7,137 workers; individual time savings did not establish broader task reorganization during the study. Results depend on the intervention and observation period.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
What does everyday use reveal about fit, workload and support that launch-time enthusiasm missed?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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