Read the scenario, examine the four habits, then complete the reflection activities before revealing the suggested responses.
Daniel is a mechanical engineer working on a project to upgrade a production line. A design package prepared by another engineer, Maya, is scheduled to be released to the fabrication team the following morning. During his final technical review, Daniel notices that one of the proposed support brackets may not meet the required load conditions.
Daniel is concerned because fabrication is about to begin. If the issue is not addressed before release, the team may need to modify or remake the component later, leading to additional cost and delay.
Daniel writes the following message to Maya:
From Daniel's perspective, the message is clear and urgent. His intention is to prevent a technical problem and protect the project schedule. However, Maya may receive the message differently. She may feel that she is being blamed for the problem, that her engineering competence is being questioned, or that Daniel is suggesting she has repeatedly made mistakes.
This illustrates an important challenge of written communication. A sender normally reads a message with full knowledge of their own intention, while the receiver only sees the words that were written. Without tone of voice, facial expression, or an immediate opportunity to clarify meaning, the same message can create very different interpretations.
Before sending the message, Daniel considers the possible consequences of misinterpretation.
The technical issue is important, but the relationship between the engineers is also important. If Maya feels personally criticised, she may become defensive and focus on explaining or defending her work rather than investigating the engineering concern. The message could also reduce trust between team members or make future technical discussions more difficult.
Daniel therefore recognises that this is a higher-risk message. It concerns another person's work, identifies a possible error, refers to a previous discussion, and creates time pressure. These factors increase the possibility that the receiver may interpret the message differently from what the sender intended.
Being risk-aware does not mean avoiding difficult messages. It means recognising when additional care may be useful before pressing “Send.”
Daniel uses the Coconsider prompt with a Gen AI tool as an interpretation mirror. His purpose is not to ask the AI to decide whether the engineering design is correct. Instead, he asks it to help him examine how his written message could be interpreted by the receiver.
For example, he asks the AI to consider the message from Maya's perspective and identify wording that could sound blaming, dismissive, or unnecessarily confrontational.
The AI points out that phrases such as “your bracket design does not meet the load requirement,” “I already mentioned,” and “we cannot keep discovering these problems” may create a sense of personal criticism. It also notes that the message presents the technical conclusion as certain, even though Daniel has identified the issue during a review and Maya may have additional information that he has not considered.
This perspective helps Daniel separate two things that had become mixed together in his original message: the technical concern that needs urgent attention and his frustration about discovering the issue late in the process.
Daniel does not automatically accept everything suggested by the AI.
The AI does not understand the complete engineering design, the project's technical standards, the previous discussions between Daniel and Maya, or the urgency of the fabrication schedule. It may also suggest language that is too soft and could reduce the clarity of an important technical warning.
Daniel therefore reviews the AI's observations carefully. He keeps the need for immediate technical action clear while removing wording that is not necessary to achieve that purpose.
He decides that his main objectives are to alert Maya to the possible load issue, ask her to review it before fabrication, and offer the information needed to resolve the concern quickly.
Daniel remains responsible for the final communication.
His goal is not simply to make the message sound polite. His goal is to communicate an important engineering concern accurately, obtain a timely review, and support effective collaboration.
After considering the different interpretations, Daniel revises his message:
The revised message still communicates the technical concern and its urgency. However, it focuses on the issue rather than the person. It also allows for the possibility that Daniel's interpretation may be incomplete and invites the other engineer to contribute relevant information.
Daniel makes the final decision to send the message because he judges that it supports both the technical objective and the working relationship.
The four habits work together.
Being risk-aware helps Daniel recognise that the message deserves additional attention. Using Gen AI gives him another perspective on how his wording may be received. Using AI with care prevents him from treating its interpretation as automatically correct. Staying human-led ensures that the final message remains aligned with the engineering purpose, project requirements, and real situation.
Using only one of these habits would be less effective. For example, asking AI to rewrite every message without first understanding the communication risk may create unnecessary dependence on the tool. Similarly, accepting AI suggestions without review could weaken an important technical message or introduce inaccurate assumptions.
The value comes from using AI as support for human judgement rather than as a replacement for it.
In this scenario, the Coconsider approach helps Daniel identify emotional or interpersonal signals in his wording that he may not have noticed himself. It encourages him to consider the receiver's perspective before the message creates an unintended reaction.
The approach can also improve engineering collaboration. Technical work often involves reviewing, challenging, correcting, and questioning the work of others. Clear communication helps teams discuss these issues without unnecessarily turning technical disagreements into personal disagreements.
The process does not require Daniel to remove urgency, disagreement, or difficult information. Instead, it helps him communicate them in a way that is more likely to support the intended outcome.
The approach cannot guarantee that a message will be interpreted exactly as intended. Different people bring different experiences, expectations, pressures, and cultural backgrounds to communication.
Gen AI also does not know everything about the situation unless the relevant context is provided. It may misunderstand the working relationship, overlook technical factors, or suggest language that is inappropriate for the organisation or engineering risk involved.
Most importantly, Gen AI should not be used to judge technical correctness simply because it is being used to review communication. Daniel must still rely on engineering calculations, standards, procedures, competent technical review, and professional judgement when deciding whether the design is acceptable.
For particularly sensitive, urgent, or complex issues, written communication may also not be the best method. A direct conversation or meeting may be more appropriate, followed by written confirmation where necessary.
Type your own response. When you are ready, compare it with the suggested answer.
Daniel intended to raise an urgent technical concern, but some of his wording could be interpreted as personal blame. Statements such as “I already mentioned” and “we cannot keep discovering these problems” introduced frustration that was not necessary to explain the technical issue. This could cause the receiver to focus on defending herself rather than addressing the engineering concern.
Gen AI does not have complete knowledge of the engineering design, project requirements, team relationship, or level of urgency. Its suggestions may therefore be incomplete or inappropriate. Daniel must review the output and decide whether it supports his actual communication and engineering objectives.
It means that Daniel remains responsible for defining the purpose of the communication, judging the AI's suggestions, confirming the technical facts, and deciding what message to send. The AI provides another perspective, but the human makes the final decision.