Concern 918 · Working draft · editorial review in progress
Executive coaching and decision-support AI
Executive AI advice can mislead leaders when confident recommendations receive more weight than the evidence and context behind them deserve.
Understanding the concern
A leader may use AI to rehearse a decision, compare options or discuss a difficult situation privately. The tool may lack important facts, frame the problem too narrowly or agree with the leader's assumptions. Fluent advice can feel like independent judgment even when it mostly reflects the prompt.
Research on business advice has found uneven effects, while work on sycophancy examines models' tendency to agree with users. Neither means that every AI coaching session is harmful. Think of an adviser who has read only your version of a disagreement. Leaders should actively seek missing perspectives, test assumptions and keep responsibility for decisions that affect other people.
An example
A manager receives encouraging AI advice about a restructuring plan without providing information about its effects on staff or customers.
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.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Prompt
- The instructions or information given to an AI to guide its response.
Evidence and its limits
The business study concerns entrepreneurs, not all executives, and the model study tests particular behaviors. The coaching scenario is an illustrative application.
Otis, Clarke, Delecourt and Holtz · Columbia Business School · Field experiment
The Uneven Impact of Generative AI on Entrepreneurial Performance ↗ (opens in a new tab)Kenyan entrepreneur study found differing effects by baseline performance and no established average treatment effect. Not a prediction for all businesses or proof that AI advice is always harmful.
Sharma and colleagues · 2023 · Research study
Towards Understanding Sycophancy in Language Models ↗ (opens in a new tab)Investigates models favoring users' stated beliefs over truthful responses and the role of preference judgments.
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 important perspective or evidence is absent from this advice, and who can challenge the recommendation independently?
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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