Use AI to Prepare for the Conversation\u2014Not Replace It<\/h2>\n
You\u2019re staring at a blank performance review.<\/p>\n
Your notes are scattered across emails, meeting agendas, and a document you haven\u2019t opened since March. The deadline is tomorrow. And you still need to find the right words to summarize and document a year of coaching conversations and performance discussions.<\/p>\n
So, you open your favorite AI tool.<\/p>\n
A few prompts later, you have three polished paragraphs filled with phrases like \u201cstrategic ownership,\u201d \u201cproactive communication,\u201d and \u201copportunities for continued growth.\u201d<\/p>\n
It sounds professional.<\/p>\n
But is it accurate? Is it specific? Does it reflect what you personally observed? Will the employee understand what they need to continue or change?<\/p>\n
And does it sound like something you would ever say to an actual human being?<\/p>\n
AI can make performance management more thoughtful and efficient. It can also help managers avoid the most important parts of their job: exercising judgment, having an honest conversation,<\/a> and building a relationship in which people can grow.<\/p>\n The difference comes down to what you ask AI to do\u2014and what you continue to own yourself.<\/p>\n Note: This is part of our series on human-centered performance managment, which begins here.<\/a><\/p>\n Managers can use AI to organize examples, improve the clarity of their feedback, prepare thoughtful questions, and summarize agreed-upon next steps.<\/p>\n Managers should never delegate performance ratings, judgments about attitude or potential, consequential employment decisions, confidential employee information, or the performance conversation itself to AI.<\/p>\n A useful rule is:<\/p>\n Use AI to prepare, clarify, and follow through. Don\u2019t ask it to observe, judge, or relate for you.<\/p>\n<\/blockquote>\n AI can support the work surrounding a performance conversation<\/a>. It cannot take responsibility for the leadership at the center of it.<\/p>\n Used thoughtfully, AI can save time and help you prepare for a more useful human conversation.<\/p>\n AI can help you sort observations into themes, identify patterns, or arrange examples chronologically.<\/p>\n For example, you might ask an approved AI tool to organize de-identified notes into categories such as:<\/p>\n The critical distinction is that AI is organizing evidence you already have. It isn\u2019t creating the evidence or deciding what the evidence means.<\/p>\n Before entering any information, follow your organization\u2019s AI and data-privacy policies. Don\u2019t paste employee names, medical information, accommodation requests, salary details, complaints, investigation materials, or other confidential information into an unapproved tool.<\/p>\n When possible, remove identifying details and include only the minimum information necessary.<\/p>\n A useful prompt might be:<\/p>\n \u201cOrganize these de-identified project examples chronologically. Separate specific, observable behaviors from interpretations or assumptions. Do not add information.\u201d<\/p>\n<\/blockquote>\n Then review every line yourself.<\/p>\n AI can organize your notes. It cannot verify that your notes are complete, fair, or accurate.<\/p>\n This is one of AI\u2019s most useful roles.<\/p>\n You can ask AI to identify vague phrases such as:<\/p>\n Those phrases might point toward a legitimate concern, but they don\u2019t tell an employee what you observed or what habits they need to adopt<\/a> to be more successful.<\/p>\n Try a prompt like this:<\/p>\n \u201cIdentify vague labels, generalizations, or conclusions in this draft. Highlight where I need a specific observable behavior, an example, an impact, or a clear expectation. Don\u2019t invent the examples for me.\u201d<\/p>\n<\/blockquote>\n You can also ask AI to identify language that sounds accusatory, patronizing, or so carefully softened that the real message has disappeared.<\/p>\n If you only ask AI to \u201cmake this sound nicer,\u201d it might bury your feedback under several layers of diaper genie feedback<\/a>.<\/p>\n Your goal isn\u2019t to make important feedback pleasant. Your goal is to make it clear, respectful, and useful.<\/p>\n AI can help you enter a conversation with curiosity instead of a predetermined conclusion.<\/p>\n For example:<\/p>\n \u201cBased on this de-identified situation, suggest five open-ended questions that would help me understand the employee\u2019s perspective. Avoid questions that assume motive, blame, or intent.\u201d<\/p>\n<\/blockquote>\n You can also ask AI to separate what you observed from what you\u2019ve concluded:<\/p>\n \u201cIdentify any places where I may be assuming attitude, motivation, personality, or intent. Separate observable facts from my interpretations.\u201d<\/p>\n<\/blockquote>\n You might discover that:<\/p>\n That distinction matters.<\/p>\n You can talk with an employee about behavior and impact. You can\u2019t see their motivation or know their intent without a conversation.<\/p>\n AI can help you prepare thoughtful questions. But once you\u2019re in the conversation, you need to listen to the answers.<\/p>\n After a real conversation, an approved AI tool can help you turn your notes into a concise recap.<\/p>\n You might ask it to organize:<\/p>\n Verify every detail before sharing the summary.<\/p>\n AI should never turn a tentative idea into a firm commitment or attribute words to an employee that they didn\u2019t say.<\/p>\n A helpful recap begins:<\/p>\n \u201cHere\u2019s what I heard us agree to today. Please let me know if I missed or misunderstood anything.\u201d<\/p>\n<\/blockquote>\n That keeps the process human and gives the employee an opportunity to confirm the shared understanding.<\/p>\n Some performance management responsibilities belong with the manager\u2014even when AI could technically assist with them.<\/p>\n A performance rating can affect compensation, promotion, development opportunities, job security, and an employee\u2019s reputation.<\/p>\n That judgment requires evidence, context, organizational standards, calibration, and human accountability.<\/p>\n Don\u2019t upload a collection of notes and ask:<\/p>\n \u201cWhat rating should I give this employee?\u201d<\/p>\n<\/blockquote>\n AI doesn\u2019t know whether your evidence is representative. It can\u2019t tell whether you documented one employee more carefully than another. It doesn\u2019t understand all the unwritten barriers, opportunities, changing priorities, or systemic issues that affected the work.<\/p>\n The same principle applies to decisions about promotions, performance improvement plans, disciplinary action, or termination.<\/p>\n If your organization uses an approved AI-enabled decision-support system, follow its policies and involve HR. But don\u2019t confuse a system-generated recommendation with an objective answer.<\/p>\n Existing employment laws still apply when organizations use AI in employment decisions. The U.S. Department of Labor<\/a> cautions that AI can produce discriminatory outcomes, and the Department of Labor emphasizes transparency, worker rights, human oversight, and protection of confidential information.<\/p>\n The manager and organization still own the decision.<\/p>\n AI can\u2019t determine whether someone is disengaged, ambitious, resistant to change, executive-ready, or committed to the organization.<\/p>\n It can generate an interpretation based on the information you provide. That interpretation may reinforce your assumptions instead of challenging them.<\/p>\n Be cautious with requests such as:<\/p>\n A performance dip could involve unclear expectations, a skill gap, low confidence, competing priorities, inadequate resources, a broken process, burnout, lack of commitment, or a poor role fit.<\/p>\n You discover what\u2019s happening through observation, conversation, and appropriate partnership with HR\u2014not through an AI-generated diagnosis.<\/p>\n AI should never be used to infer an employee\u2019s mental health, medical condition, disability, personality, or other sensitive personal information.<\/p>\n Don\u2019t paste private employee information into an AI platform simply because doing so is faster.<\/p>\n Use only tools your organization has approved for that purpose. Understand what information may be entered, how it is retained, who can access it, and whether the system uses inputs to improve its models.<\/p>\n When you\u2019re unsure, stop and ask HR, IT, legal counsel, or your data-security team.<\/p>\n Protecting employee information is part of protecting trust.<\/p>\n AI can help you prepare talking points. It shouldn\u2019t deliver your feedback.<\/p>\n Don\u2019t send an AI-generated message to avoid a conversation that deserves your attention. Don\u2019t hide behind a chatbot, an automated review, or a polished paragraph filled with corporate language.<\/p>\n Your employee needs to hear your observations, ask questions, share context, and understand what happens next.<\/p>\n They also need to see how you respond when they disagree, become emotional, or share information you didn\u2019t know.<\/p>\n That exchange is where leadership happens.<\/p>\n We call vague, overly softened performance feedback \u201cDiaper Genie feedback.\u201d<\/a> The manager wraps the real message in so many layers of pleasant language that the employee can no longer tell what\u2019s inside.<\/p>\n AI can produce Diaper Genie feedback with remarkable efficiency.<\/p>\n Consider this:<\/p>\n \u201cJordan consistently contributes valuable perspectives and demonstrates a strong commitment to team success. Continued focus on proactive communication and strategic ownership will help elevate Jordan\u2019s impact and effectiveness.\u201d<\/p>\n<\/blockquote>\n It sounds positive and polished.<\/p>\n It also tells Jordan almost nothing.<\/p>\n What does \u201cproactive communication\u201d mean? What did Jordan do or fail to do? Does Jordan understand the impact? What should change? And what does it mean to \u201celevate effectiveness\u201d?<\/p>\n A clearer version might be:<\/p>\n \u201cDuring the last three project reviews, you identified significant implementation risks but didn\u2019t share them until the final week. That gave the operations team very little time to adjust and contributed to two missed deadlines. I\u2019d like to understand what made it difficult to raise those concerns earlier. Going forward, the expectation is that you surface a significant project risk within one business day of identifying it.\u201d<\/p>\n<\/blockquote>\n Then have the conversation:<\/p>\n \u201cWhat\u2019s happening from your perspective?\u201d<\/p>\n<\/blockquote>\n \u201cWhat would help you raise these concerns sooner?\u201d<\/p>\n<\/blockquote>\n \u201cWhat specific action can you commit to for the next project?\u201d<\/p>\n<\/blockquote>\n \u201cWhen should we check in on progress?\u201d<\/p>\n<\/blockquote>\n That feedback is more direct and more human. It gives Jordan something specific to understand, discuss, and change.<\/p>\n Before you use AI-assisted language in a performance conversation or appraisal, check five things.<\/p>\n Can you personally verify every example, date, claim, and conclusion?<\/p>\n If AI added something that \u201csounds about right,\u201d remove it.<\/p>\n Does the feedback describe observable behavior and impact\u2014or rely on labels such as \u201cdifficult,\u201d \u201cuncommitted,\u201d or \u201cnot strategic\u201d?<\/p>\n Replace labels with what you actually saw or heard.<\/p>\n Did priorities change? Were resources available? Did the employee have the information, support, and authority needed to succeed?<\/p>\n Context doesn\u2019t eliminate accountability. It helps you apply it fairly.<\/p>\n Would you describe the behavior the same way if another employee had done it? Are you rewarding visibility more than contribution? Are you confusing a communication-style difference with a performance problem?<\/p>\n AI can help surface these questions. It can\u2019t guarantee that your conclusions are unbiased.<\/p>\n Would you say these words out loud? Does the feedback sound like you? Does it make the expectation clear and leave room for the employee\u2019s perspective?<\/p>\n If it sounds as though it came from a policy manual written by a committee of robots, keep working.<\/p>\n AI can help around the edges. The four dimensions r<\/a>emind you what still belongs to you.<\/p>\n AI can suggest an opening. You build the trust that makes it believable.<\/p>\n AI can spot vague language and missing examples. You decide what success looks like.<\/p>\n AI can suggest thoughtful questions. You ask them\u2014and listen closely to the answers.<\/p>\n AI can summarize next steps. You and your employee agree on what happens next.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<\/div>\n<\/div>\n Managers shouldn\u2019t have to invent their own rules for using AI in performance management.<\/p>\n HR, IT, legal, and senior leaders should clarify:<\/p>\n A policy that says \u201cuse AI responsibly\u201d isn\u2019t enough. Give managers realistic scenarios and opportunities to practice using AI as a preparation partner without surrendering their judgment.<\/p>\n Used well, AI can help you organize information, prepare better questions, improve clarity, and follow through more consistently.<\/p>\n Those are meaningful benefits.<\/p>\n But AI cannot care about your employee\u2019s success. It cannot notice the hesitation before they answer. It cannot repair trust, recognize courage, or respond thoughtfully to new information.<\/p>\n That work still belongs to you.<\/p>\n Use AI to prepare for the conversation.<\/p>\n Then close the tool, talk with your employee, stay curious, and lead.<\/p>\n See Also:<\/strong><\/p>\n How to Collaborate With AI Without Losing Your Voice<\/a><\/p>\n Performance Management Mistakes With HR<\/a><\/p>\n Are you looking to build a culture of human-centered accountability, where leaders at every level have the confidence and skills to set clear expectations and coach employees to high performance? We can help. Contact us<\/a> to learn more about our custom performance management for human-centered leaders programs, or learn more about our open-enrolment performance management accelerator here.<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n The post AI and Performance Management: What Managers Can Automate\u2014and What Must Stay Human<\/a> appeared first on Let's Grow Leaders<\/a>.<\/p>\n\n","protected":false},"excerpt":{"rendered":" Use AI to Prepare for the Conversation\u2014Not Replace It You\u2019re staring at a blank performance review. Your notes are scattered across emails, meeting agendas, and a document you haven\u2019t opened since March. The deadline is tomorrow. And you still need to find the right words to summarize and document a year of coaching conversations and […]<\/p>\n","protected":false},"author":1,"featured_media":7053161,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[215472,102685,145600,155733],"tags":[49682,215474,1064,5984,11419,5786,189858,215473,4761],"dealstore":[],"offerexpiration":[],"class_list":["post-7053160","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-training","category-by-karin-hurt-and-david-dye","category-performance-management","category-responsible-ai","tag-ai-training","tag-automateand","tag-human","tag-management","tag-managers","tag-performance","tag-performance-management","tag-responsible-ai","tag-stay"],"yoast_head":"\nAI and Performance Management: A Quick Answer<\/h3>\n
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Four Helpful Ways to Use AI in Performance Management<\/h3>\n
1. Organize Examples You Have Already Documented<\/h4>\n
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2. Find Vague, Confusing, or Overly Softened Feedback<\/h4>\n
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3. Prepare Better Questions and Pressure-Test Your Assumptions<\/h4>\n
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4. Summarize Agreed-Upon Next Steps<\/h4>\n
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Four Things Managers Should Never Delegate to AI<\/h3>\n
1. Don\u2019t Delegate Performance Ratings or Consequential Decisions<\/h4>\n
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2. Don\u2019t Ask AI to Infer Attitude, Motivation, Potential, or the Cause of Poor Performance<\/h4>\n
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3. Don\u2019t Put Confidential Employee Information Into Unapproved Tools<\/h4>\n
4. Don\u2019t Delegate the Conversation<\/h4>\n
How to Spot AI-Generated \u201cDiaper Genie\u201d Feedback<\/h3>\n
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AI and Performance Management: Five Questions to Ask Before Using AI-Assisted Feedback<\/h3>\n
1. Is it accurate?<\/h5>\n
2. Is it evidence-based?<\/h5>\n
3. Is important context missing?<\/h5>\n
4. Have I checked for bias?<\/h5>\n
5. Does it sound human?<\/h5>\n
Use the Four Dimensions of the Performance Loop to Keep It Human<\/h3>\n
Connection<\/h5>\n
Clarity<\/h5>\n
Curiosity<\/h5>\n
Commitment<\/h5>\n
Give Managers Clear Guardrails<\/h3>\n
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AI Can Save Time. It Can\u2019t Do the Work of Leadership.<\/h3>\n
![\"Performance](\"https:\/\/letsgrowleaders.com\/wp-content\/uploads\/2026\/07\/IMG_2167-1024x683.jpeg\")
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