{"content_id":"mvdwzfcar2","slug":"ai-collaboration-relationslipping-team-trust","locale":"en","schema_type":"Article","category":"knowledge_base","category_name":"Knowledge Base","title":"AI Collaboration: Relationslipping and Team Guidelines","summary":"Relationslipping is the weakening of work relationships as interactions with colleagues are replaced by AI. Related survey figures can be understood in the context of the environment for asking questions, asynchronous communication, and standards for sharing how AI is used.","sponsorship_disclosure":null,"affiliate_disclosure":null,"commerce_disclosure":null,"author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["Relationslipping is a phenomenon in which work relationships within a team weaken as people repeatedly turn to AI instead of colleagues.","In a survey featured by HBR, work coordination was 22.3% lower in teams where replacing colleagues with AI was common.","49% of respondents said they experienced colleagues turning to AI instead of them at least sometimes.","Psychological safety and consistent team communication help create an environment where people can ask colleagues questions.","When evaluating the results of AI use, contributions to knowledge sharing need to be considered alongside individual speed."],"content_markdown":"Even if AI helps you finish work faster, your ability to collaborate with colleagues can weaken. Relationslipping is the weakening of work relationships as people turn to AI instead of colleagues. Team management should address both the environment for asking questions and the ways knowledge is shared.\n\nThe survey figures in this article are based on an HBR research overview published on October 15, 2026.\n\n## What is Relationslipping?\n\nRelationslipping is the weakening of work relationships as AI replaces interactions with colleagues. The English term used by the researchers is Relationslipping. The key is not how much AI people use, but how their conversations with colleagues change.\n\nWhen you ask a colleague a question, you learn about their experience as well as getting an answer. As you listen to their explanation, you learn the criteria they use to assess a problem. These experiences help you decide whom to ask for help next time.\n\nHBR's research overview explains how relationships weaken. Skipping conversations reduces opportunities to discover a colleague's expertise. When AI takes over the work process, it also becomes harder to understand a colleague's judgment. This explanation is available in the [summary of HBR's research overview](https://store.hbr.org/product/team-dynamics-weaken-when-employees-turn-to-ai-instead-of-colleagues/H09BZ1?sku=H09BZ1-PDF-ENG).\n\n## What the survey figures show\n\nThe survey shows an association between replacing colleagues with AI and team relationships. It covered about 1,000 full-time office workers on project teams in the United States. The findings were presented in HBR in October 2026.\n\n| Survey measure | Reported figure | What to distinguish when reading |\n|---|---|---|\n| Work coordination in teams where replacing colleagues with AI is common | 22.3% lower | A finding about work coordination, not a rate of decline in output |\n| Team trust under the same conditions | 21.9% lower | A finding about trust among colleagues |\n| Respondents who had experienced colleagues turning to AI instead of them at least sometimes | 49% | Not the percentage of people who were laid off or whose roles disappeared |\n| Likelihood of replacing colleagues with AI among members with low psychological safety | At least twice as likely | An association with the behavior of turning to AI instead of colleagues |\n| Work coordination among AI users who stay in regular contact with their team | 28% higher | No guarantee that contact alone will produce the same effect |\n| Team trust among AI users who stay in regular contact with their team | 19% higher | An observed association between communication and trust |\n\nThese figures also appear in [BetterUp's post introducing the research](https://uk.linkedin.com/company/betterup/). The public summary alone does not make it easy to verify all the comparison groups and formulas. The survey dates must also be distinguished from the publication date.\n\nDifferences found in a survey do not, by themselves, establish causation. The possibility that teams with weaker relationships turned to AI more often must also be considered. Do not use these figures as effect sizes that apply to every organization.\n\n## Comparing individual productivity and team collaboration\n\nIndividual productivity focuses on the ability to finish one's own work. Team collaboration includes the process of connecting people's knowledge. It is therefore difficult to judge team performance solely by the speed of individual work.\n\n| Category | What to check in individual work | What to also check in team work |\n|---|---|---|\n| Getting answers | Whether the person quickly obtained the answer they needed | Whether they know colleagues with relevant experience |\n| Producing work | Whether the person completed a draft or analysis | Whether colleagues can understand the reasoning behind it |\n| Solving problems | Whether the person resolved what was blocking them | Whether someone else will have to solve the same problem again |\n| Experience using AI | Whether the person found an approach that works for them | Whether other members can also use that approach |\n\nThis table applies the concerns raised by the research to team reviews. It is not a validated assessment scale or diagnostic tool. Choose items that fit your team's work and use them as a basis for discussion.\n\n## Psychological safety and barriers to asking questions\n\nPsychological safety is the sense that you can ask questions or reveal mistakes without worrying about negative consequences. It is hard to approach colleagues if you worry that asking questions could hurt your evaluation. AI can provide someone to ask without this interpersonal burden.\n\nIf you are a leader, start by examining how you react when someone asks a question. A sigh or a dismissive attitude can make people hesitate to ask the next question. If you cannot answer right away, one option is to let them know when you can respond.\n\n- Identify where the person is stuck before judging the quality of the question.\n- When you do not know the answer, point them toward someone or something you can check together.\n- When you disagree, explain the reasoning behind your judgment.\n- Do not assume that asking for help is a sign of incompetence.\n\n## What to include in asynchronous communication\n\nAsynchronous communication is a conversation that can continue without everyone being online at the same time. Reading and replying later to messages left in Slack or email are examples. The researchers suggest staying in touch with colleagues through these channels.\n\nPosting only whether work is complete may not reveal where judgment is needed. Encourage people to share questions they are still working through. It helps to specify where a colleague's experience is needed.\n\n- Where you are currently stuck and what you have already checked\n- Parts of AI responses you have not yet been able to assess\n- New facts learned during the work and the evidence behind them\n- Questions that need a colleague's experience or review\n\nDo not turn a requirement for daily contact into an obligation to be constantly available. Asynchronous channels are a way to communicate without having to respond immediately. Message counts alone also make it difficult to assess the level of collaboration.\n\n## Guidelines for sharing the process of using AI\n\nWhen sharing AI output, include a record of the judgments made by people. A completed analysis alone makes it difficult to know which questions were effective. Document the process so colleagues can refer to it when doing the same work.\n\nUsing AI for customer analysis is one example. The items below are suggestions for preserving the results as team knowledge. They are not examples of actual business performance or verified time savings.\n\n| Item to share | What to record | What colleagues can learn |\n|---|---|---|\n| The problem you were trying to solve | The purpose of the analysis and the questions that needed answers | The work context in which to apply the method |\n| Requests made to AI | Questions and conditions that influenced the result | Ways of making requests that they can use |\n| Changes made by people | Responses that were accepted or excluded | The process of reviewing AI responses |\n| Reasons for decisions | Facts or work criteria used to make changes | Why the result was accepted |\n| Remaining questions | What has not yet been verified | What needs further review |\n\nSaving only prompts may leave out why the results were accepted. Recording the reasons for changes also gives colleagues clues to understand the judgment behind them. Check whether other people have used these records as well.\n\n## Criteria for evaluating the results of AI use\n\nEvaluations can include both individual work results and contributions left for the team. Checking only whether someone uses AI skillfully makes it difficult to assess knowledge sharing. Also look at whether they have documented their experience in a form colleagues can use.\n\nThe following are examples of applying the researchers' knowledge-sharing suggestions to evaluations. They are not criteria with specific scores or weights. Choose what to observe based on how your team works.\n\n- Whether the person documented work methods that other members can use\n- Whether they explained to colleagues why they changed AI responses\n- Whether they contributed to colleagues' questions or review requests\n- Whether the shared methods were used again in actual work\n\n## Common mistakes and misreadings of the figures\n\nThe most common misunderstanding is to read survey figures about relationships as measures of work performance. The same number means something different when the survey measure changes. Keeping the following distinctions in mind can help avoid overstating the findings.\n\n| Misunderstanding | Accurate distinction |\n|---|---|\n| If collaboration is 22.3% lower, output falls by the same amount | Work coordination and output are different measures |\n| 49% lost their jobs because of AI | The percentage who experienced colleagues turning to AI instead of them |\n| Sending messages every day increases trust by 19% | An association between communication and trust, not a guaranteed improvement |\n| People who use AI a lot cause relationships to weaken | The issue concerns how AI use replaces interactions with colleagues |\n| Posting only the output also shares the know-how behind its use | How questions were asked and why changes were made must be documented separately |\n\n## Where to start with a team review\n\nStart by identifying conversations that have decreased in recent, actual work. It is easier to discuss a single task than a vague impression of the whole team. The sequence below is an operational suggestion based on the concerns raised by the research.\n\n1. Choose one recent task that involved AI. Identify the problem you were trying to solve.\n2. Check whether any conversations with colleagues were skipped in the process. Review whether you missed experience you needed.\n3. Record the human judgment applied to the AI output. Leave it in a channel colleagues can refer to.\n4. Check whether the record was used in the next similar task. Adjust how you share any parts that were not helpful.","content_html":"\u003cp\u003eEven if AI helps you finish work faster, your ability to collaborate with colleagues can weaken. Relationslipping is the weakening of work relationships as people turn to AI instead of colleagues. Team management should address both the environment for asking questions and the ways knowledge is shared.\u003c/p\u003e\n\u003cp\u003eThe survey figures in this article are based on an HBR research overview published on October 15, 2026.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-is-relationslipping\" class=\"anchor\" id=\"what-is-relationslipping\"\u003e\u003c/a\u003eWhat is Relationslipping?\u003c/h2\u003e\n\u003cp\u003eRelationslipping is the weakening of work relationships as AI replaces interactions with colleagues. The English term used by the researchers is Relationslipping. The key is not how much AI people use, but how their conversations with colleagues change.\u003c/p\u003e\n\u003cp\u003eWhen you ask a colleague a question, you learn about their experience as well as getting an answer. As you listen to their explanation, you learn the criteria they use to assess a problem. These experiences help you decide whom to ask for help next time.\u003c/p\u003e\n\u003cp\u003eHBR's research overview explains how relationships weaken. Skipping conversations reduces opportunities to discover a colleague's expertise. When AI takes over the work process, it also becomes harder to understand a colleague's judgment. This explanation is available in the \u003ca href=\"https://store.hbr.org/product/team-dynamics-weaken-when-employees-turn-to-ai-instead-of-colleagues/H09BZ1?sku=H09BZ1-PDF-ENG\"\u003esummary of HBR's research overview\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-the-survey-figures-show\" class=\"anchor\" id=\"what-the-survey-figures-show\"\u003e\u003c/a\u003eWhat the survey figures show\u003c/h2\u003e\n\u003cp\u003eThe survey shows an association between replacing colleagues with AI and team relationships. It covered about 1,000 full-time office workers on project teams in the United States. The findings were presented in HBR in October 2026.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSurvey measure\u003c/th\u003e\n\u003cth\u003eReported figure\u003c/th\u003e\n\u003cth\u003eWhat to distinguish when reading\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eWork coordination in teams where replacing colleagues with AI is common\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003e22.3% lower\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eA finding about work coordination, not a rate of decline in output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eTeam trust under the same conditions\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003e21.9% lower\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eA finding about trust among colleagues\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eRespondents who had experienced colleagues turning to AI instead of them at least sometimes\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003e49%\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eNot the percentage of people who were laid off or whose roles disappeared\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eLikelihood of replacing colleagues with AI among members with low psychological safety\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003eAt least twice as likely\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eAn association with the behavior of turning to AI instead of colleagues\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eWork coordination among AI users who stay in regular contact with their team\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003e28% higher\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eNo guarantee that contact alone will produce the same effect\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Survey measure\"\u003eTeam trust among AI users who stay in regular contact with their team\u003c/td\u003e\n\u003ctd data-label=\"Reported figure\"\u003e19% higher\u003c/td\u003e\n\u003ctd data-label=\"What to distinguish when reading\"\u003eAn observed association between communication and trust\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThese figures also appear in \u003ca href=\"https://uk.linkedin.com/company/betterup/\"\u003eBetterUp's post introducing the research\u003c/a\u003e. The public summary alone does not make it easy to verify all the comparison groups and formulas. The survey dates must also be distinguished from the publication date.\u003c/p\u003e\n\u003cp\u003eDifferences found in a survey do not, by themselves, establish causation. The possibility that teams with weaker relationships turned to AI more often must also be considered. Do not use these figures as effect sizes that apply to every organization.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#comparing-individual-productivity-and-team-collaboration\" class=\"anchor\" id=\"comparing-individual-productivity-and-team-collaboration\"\u003e\u003c/a\u003eComparing individual productivity and team collaboration\u003c/h2\u003e\n\u003cp\u003eIndividual productivity focuses on the ability to finish one's own work. Team collaboration includes the process of connecting people's knowledge. It is therefore difficult to judge team performance solely by the speed of individual work.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCategory\u003c/th\u003e\n\u003cth\u003eWhat to check in individual work\u003c/th\u003e\n\u003cth\u003eWhat to also check in team work\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Category\"\u003eGetting answers\u003c/td\u003e\n\u003ctd data-label=\"What to check in individual work\"\u003eWhether the person quickly obtained the answer they needed\u003c/td\u003e\n\u003ctd data-label=\"What to also check in team work\"\u003eWhether they know colleagues with relevant experience\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Category\"\u003eProducing work\u003c/td\u003e\n\u003ctd data-label=\"What to check in individual work\"\u003eWhether the person completed a draft or analysis\u003c/td\u003e\n\u003ctd data-label=\"What to also check in team work\"\u003eWhether colleagues can understand the reasoning behind it\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Category\"\u003eSolving problems\u003c/td\u003e\n\u003ctd data-label=\"What to check in individual work\"\u003eWhether the person resolved what was blocking them\u003c/td\u003e\n\u003ctd data-label=\"What to also check in team work\"\u003eWhether someone else will have to solve the same problem again\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Category\"\u003eExperience using AI\u003c/td\u003e\n\u003ctd data-label=\"What to check in individual work\"\u003eWhether the person found an approach that works for them\u003c/td\u003e\n\u003ctd data-label=\"What to also check in team work\"\u003eWhether other members can also use that approach\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThis table applies the concerns raised by the research to team reviews. It is not a validated assessment scale or diagnostic tool. Choose items that fit your team's work and use them as a basis for discussion.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#psychological-safety-and-barriers-to-asking-questions\" class=\"anchor\" id=\"psychological-safety-and-barriers-to-asking-questions\"\u003e\u003c/a\u003ePsychological safety and barriers to asking questions\u003c/h2\u003e\n\u003cp\u003ePsychological safety is the sense that you can ask questions or reveal mistakes without worrying about negative consequences. It is hard to approach colleagues if you worry that asking questions could hurt your evaluation. AI can provide someone to ask without this interpersonal burden.\u003c/p\u003e\n\u003cp\u003eIf you are a leader, start by examining how you react when someone asks a question. A sigh or a dismissive attitude can make people hesitate to ask the next question. If you cannot answer right away, one option is to let them know when you can respond.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify where the person is stuck before judging the quality of the question.\u003c/li\u003e\n\u003cli\u003eWhen you do not know the answer, point them toward someone or something you can check together.\u003c/li\u003e\n\u003cli\u003eWhen you disagree, explain the reasoning behind your judgment.\u003c/li\u003e\n\u003cli\u003eDo not assume that asking for help is a sign of incompetence.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-to-include-in-asynchronous-communication\" class=\"anchor\" id=\"what-to-include-in-asynchronous-communication\"\u003e\u003c/a\u003eWhat to include in asynchronous communication\u003c/h2\u003e\n\u003cp\u003eAsynchronous communication is a conversation that can continue without everyone being online at the same time. Reading and replying later to messages left in Slack or email are examples. The researchers suggest staying in touch with colleagues through these channels.\u003c/p\u003e\n\u003cp\u003ePosting only whether work is complete may not reveal where judgment is needed. Encourage people to share questions they are still working through. It helps to specify where a colleague's experience is needed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you are currently stuck and what you have already checked\u003c/li\u003e\n\u003cli\u003eParts of AI responses you have not yet been able to assess\u003c/li\u003e\n\u003cli\u003eNew facts learned during the work and the evidence behind them\u003c/li\u003e\n\u003cli\u003eQuestions that need a colleague's experience or review\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo not turn a requirement for daily contact into an obligation to be constantly available. Asynchronous channels are a way to communicate without having to respond immediately. Message counts alone also make it difficult to assess the level of collaboration.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#guidelines-for-sharing-the-process-of-using-ai\" class=\"anchor\" id=\"guidelines-for-sharing-the-process-of-using-ai\"\u003e\u003c/a\u003eGuidelines for sharing the process of using AI\u003c/h2\u003e\n\u003cp\u003eWhen sharing AI output, include a record of the judgments made by people. A completed analysis alone makes it difficult to know which questions were effective. Document the process so colleagues can refer to it when doing the same work.\u003c/p\u003e\n\u003cp\u003eUsing AI for customer analysis is one example. The items below are suggestions for preserving the results as team knowledge. They are not examples of actual business performance or verified time savings.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem to share\u003c/th\u003e\n\u003cth\u003eWhat to record\u003c/th\u003e\n\u003cth\u003eWhat colleagues can learn\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to share\"\u003eThe problem you were trying to solve\u003c/td\u003e\n\u003ctd data-label=\"What to record\"\u003eThe purpose of the analysis and the questions that needed answers\u003c/td\u003e\n\u003ctd data-label=\"What colleagues can learn\"\u003eThe work context in which to apply the method\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to share\"\u003eRequests made to AI\u003c/td\u003e\n\u003ctd data-label=\"What to record\"\u003eQuestions and conditions that influenced the result\u003c/td\u003e\n\u003ctd data-label=\"What colleagues can learn\"\u003eWays of making requests that they can use\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to share\"\u003eChanges made by people\u003c/td\u003e\n\u003ctd data-label=\"What to record\"\u003eResponses that were accepted or excluded\u003c/td\u003e\n\u003ctd data-label=\"What colleagues can learn\"\u003eThe process of reviewing AI responses\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to share\"\u003eReasons for decisions\u003c/td\u003e\n\u003ctd data-label=\"What to record\"\u003eFacts or work criteria used to make changes\u003c/td\u003e\n\u003ctd data-label=\"What colleagues can learn\"\u003eWhy the result was accepted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to share\"\u003eRemaining questions\u003c/td\u003e\n\u003ctd data-label=\"What to record\"\u003eWhat has not yet been verified\u003c/td\u003e\n\u003ctd data-label=\"What colleagues can learn\"\u003eWhat needs further review\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eSaving only prompts may leave out why the results were accepted. Recording the reasons for changes also gives colleagues clues to understand the judgment behind them. Check whether other people have used these records as well.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#criteria-for-evaluating-the-results-of-ai-use\" class=\"anchor\" id=\"criteria-for-evaluating-the-results-of-ai-use\"\u003e\u003c/a\u003eCriteria for evaluating the results of AI use\u003c/h2\u003e\n\u003cp\u003eEvaluations can include both individual work results and contributions left for the team. Checking only whether someone uses AI skillfully makes it difficult to assess knowledge sharing. Also look at whether they have documented their experience in a form colleagues can use.\u003c/p\u003e\n\u003cp\u003eThe following are examples of applying the researchers' knowledge-sharing suggestions to evaluations. They are not criteria with specific scores or weights. Choose what to observe based on how your team works.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhether the person documented work methods that other members can use\u003c/li\u003e\n\u003cli\u003eWhether they explained to colleagues why they changed AI responses\u003c/li\u003e\n\u003cli\u003eWhether they contributed to colleagues' questions or review requests\u003c/li\u003e\n\u003cli\u003eWhether the shared methods were used again in actual work\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca href=\"#common-mistakes-and-misreadings-of-the-figures\" class=\"anchor\" id=\"common-mistakes-and-misreadings-of-the-figures\"\u003e\u003c/a\u003eCommon mistakes and misreadings of the figures\u003c/h2\u003e\n\u003cp\u003eThe most common misunderstanding is to read survey figures about relationships as measures of work performance. The same number means something different when the survey measure changes. Keeping the following distinctions in mind can help avoid overstating the findings.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMisunderstanding\u003c/th\u003e\n\u003cth\u003eAccurate distinction\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Misunderstanding\"\u003eIf collaboration is 22.3% lower, output falls by the same amount\u003c/td\u003e\n\u003ctd data-label=\"Accurate distinction\"\u003eWork coordination and output are different measures\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Misunderstanding\"\u003e49% lost their jobs because of AI\u003c/td\u003e\n\u003ctd data-label=\"Accurate distinction\"\u003eThe percentage who experienced colleagues turning to AI instead of them\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Misunderstanding\"\u003eSending messages every day increases trust by 19%\u003c/td\u003e\n\u003ctd data-label=\"Accurate distinction\"\u003eAn association between communication and trust, not a guaranteed improvement\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Misunderstanding\"\u003ePeople who use AI a lot cause relationships to weaken\u003c/td\u003e\n\u003ctd data-label=\"Accurate distinction\"\u003eThe issue concerns how AI use replaces interactions with colleagues\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Misunderstanding\"\u003ePosting only the output also shares the know-how behind its use\u003c/td\u003e\n\u003ctd data-label=\"Accurate distinction\"\u003eHow questions were asked and why changes were made must be documented separately\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca href=\"#where-to-start-with-a-team-review\" class=\"anchor\" id=\"where-to-start-with-a-team-review\"\u003e\u003c/a\u003eWhere to start with a team review\u003c/h2\u003e\n\u003cp\u003eStart by identifying conversations that have decreased in recent, actual work. It is easier to discuss a single task than a vague impression of the whole team. The sequence below is an operational suggestion based on the concerns raised by the research.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eChoose one recent task that involved AI. Identify the problem you were trying to solve.\u003c/li\u003e\n\u003cli\u003eCheck whether any conversations with colleagues were skipped in the process. Review whether you missed experience you needed.\u003c/li\u003e\n\u003cli\u003eRecord the human judgment applied to the AI output. Leave it in a channel colleagues can refer to.\u003c/li\u003e\n\u003cli\u003eCheck whether the record was used in the next similar task. Adjust how you share any parts that were not helpful.\u003c/li\u003e\n\u003c/ol\u003e\n","tags":["Psychological safety","Leadership","Organizational culture","Productivity","Generative AI"],"faqs":[{"question":"What does \"relationslipping\" mean?","answer":"It is a phenomenon in which working relationships within a team weaken as interactions with colleagues are replaced by AI. It includes the problem of having fewer opportunities to learn what colleagues know well."},{"question":"Does using AI necessarily reduce trust within a team?","answer":"That conclusion cannot be drawn. The research focuses on the behavior of replacing interactions with colleagues with AI. The association observed in the survey cannot be treated as a causal relationship that applies to every team."},{"question":"Does the figure showing 22.3% lower work coordination represent a decline in productivity?","answer":"It cannot be used as a rate of productivity decline. It is a figure related to work coordination from a survey presented by HBR. It does not mean that revenue or the amount of work completed fell by the same percentage."},{"question":"Does this mean that 49% of respondents lost their jobs to AI?","answer":"It does not mean layoffs or the elimination of jobs. It is the percentage of respondents who said they had experienced colleagues turning to AI instead of them at least sometimes."},{"question":"How is psychological safety related to AI collaboration?","answer":"It is hard to turn to colleagues if you worry that asking questions will lead to negative judgments. In the study, team members with lower psychological safety were more likely to turn to AI instead of colleagues."},{"question":"Does asynchronous communication mean holding meetings every day?","answer":"It does not mean meetings where everyone gathers at the same time. It means leaving messages in Slack or email for others to read and respond to when they can. You can share progress updates along with questions that need review."},{"question":"What should I document when sharing my experience using AI?","answer":"Document the problem you were trying to solve and the request you made to AI. You can also record the parts a person revised and the reasoning behind those decisions. Note any unresolved questions so colleagues can understand where the method can be applied."},{"question":"What else should be considered when evaluating AI skills?","answer":"You can consider contributions to knowledge sharing alongside individual work performance. This means checking whether other team members have used the method. Do not judge contributions solely by the number of documents shared."},{"question":"Can the survey findings be applied to all organizations in Korea?","answer":"Figures from a survey of workers in the United States cannot be directly applied as effect sizes for Korean organizations. They can serve as reference evidence when reviewing the environment for asking questions and how knowledge is shared."}],"sources":[{"url":"https://hbr.org/2026/10/team-dynamics-weaken-when-employees-turn-to-ai-instead-of-colleagues","title":"Harvard Business Review, Team Dynamics Weaken When Employees Turn to AI Instead of Colleagues, October 15, 2026","type":"source"},{"url":"https://store.hbr.org/product/team-dynamics-weaken-when-employees-turn-to-ai-instead-of-colleagues/H09BZ1?sku=H09BZ1-PDF-ENG","title":"Harvard Business Review Store, publication details and public summary of the article introducing the study","type":"source"},{"url":"https://uk.linkedin.com/company/betterup/","title":"BetterUp’s official LinkedIn, overview of the Relationslipping study’s participants and key figures","type":"data_point"}],"images":[{"id":2532,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MzkxOTMsInB1ciI6ImJsb2JfaWQifX0=--77da2917efa51c2c524b79700f9ca08f5cd2adf6/ai-e6148b85.webp","is_representative":true,"generation_method":"ai_photo","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"남성이 한 손을 내밀며 동료를 바라보지만, 동료는 태블릿의 채팅 화면에 집중하고 있다.","caption":"동료와의 대화를 AI로 대체하는 일이 반복되면 팀의 업무 관계가 약해질 수 있습니다.","description":null},"en":{"alt":"A man extends a hand toward a colleague who is focused on a chat screen on her tablet.","caption":"Repeatedly replacing conversations with colleagues with AI can weaken working relationships within a team.","description":null},"ja":{"alt":"男性が片手を差し出して同僚を見つめる一方、同僚はタブレットのチャット画面に集中している。","caption":"同僚との対話を繰り返しAIに置き換えると、チーム内の仕事上の関係が弱まるおそれがあります。","description":null},"es":{"alt":"Un hombre extiende una mano hacia una compañera, que se concentra en un chat en su tableta.","caption":"Sustituir repetidamente las conversaciones entre colegas por la IA puede debilitar las relaciones de trabajo del equipo.","description":null},"id":{"alt":"Seorang pria mengulurkan tangan ke arah rekan yang fokus pada tampilan percakapan di tabletnya.","caption":"Terus-menerus mengganti percakapan dengan rekan kerja dengan interaksi AI dapat melemahkan hubungan kerja dalam tim.","description":null},"pt":{"alt":"Um homem estende a mão em direção a uma colega, que se concentra em uma conversa no tablet.","caption":"Substituir repetidamente as conversas entre colegas pela IA pode enfraquecer as relações de trabalho da equipe.","description":null},"zh-hant":{"alt":"一名男子伸出一隻手望向同事，同事卻專注於平板上的聊天畫面。","caption":"反覆以 AI 取代與同事的對話，可能削弱團隊內的工作關係。","description":null},"de":{"alt":"Ein Mann streckt eine Hand zu einer Kollegin aus, die auf einen Chat auf ihrem Tablet schaut.","caption":"Wer Gespräche mit Kollegen wiederholt durch KI ersetzt, kann die Arbeitsbeziehungen im Team schwächen.","description":null}}},{"id":2533,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MzkxOTQsInB1ciI6ImJsb2JfaWQifX0=--3f45650b909f19f7ae3c01eacf9c186578d4fa6e/ai-d455ad4f.webp","is_representative":false,"generation_method":"ai_semi","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"파란 카디건을 입은 여성이 벽에 붙은 표시된 문서를 가리키며 동료들과 논의하고 있다.","caption":"AI 활용 과정과 판단 근거를 동료와 공유하는 기준이 필요합니다.","description":null},"en":{"alt":"A woman in a blue cardigan points to annotated documents on a wall while discussing them with colleagues.","caption":"Teams need standards for sharing how AI is used and the reasoning behind decisions.","description":null},"ja":{"alt":"青いカーディガンを着た女性が、壁に貼られた書き込み入りの資料を指しながら同僚と話し合っている。","caption":"AIの活用過程や判断の根拠を同僚と共有するための基準が必要です。","description":null},"es":{"alt":"Una mujer con un cárdigan azul señala documentos anotados en la pared mientras conversa con sus colegas.","caption":"Los equipos necesitan criterios para compartir cómo usan la IA y en qué basan sus decisiones.","description":null},"id":{"alt":"Perempuan berkardigan biru menunjuk dokumen bertanda di dinding sambil berdiskusi dengan rekan kerja.","caption":"Tim memerlukan pedoman untuk berbagi proses penggunaan AI dan dasar pengambilan keputusan.","description":null},"pt":{"alt":"Uma mulher de cardigã azul aponta para documentos anotados na parede enquanto conversa com colegas.","caption":"As equipes precisam de critérios para compartilhar como usam a IA e os fundamentos de suas decisões.","description":null},"zh-hant":{"alt":"一名穿著藍色針織外套的女性指著牆上做了標記的文件，與同事討論。","caption":"團隊需要訂定與同事分享 AI 使用過程及判斷依據的準則。","description":null},"de":{"alt":"Eine Frau in blauer Strickjacke zeigt auf markierte Dokumente an der Wand und spricht mit Kollegen.","caption":"Teams brauchen Regeln, um den KI-Einsatz und die Gründe für Entscheidungen miteinander zu teilen.","description":null}}}],"published_at":"2026-11-07T06:22:50+09:00","updated_at":"2026-11-07T06:25:33+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/ai-collaboration-relationslipping-team-trust"}